From aea8a1f3d9d9247e4d932d17a6741a6a23fd97c1 Mon Sep 17 00:00:00 2001 From: Stephan Koenig Date: Thu, 8 Oct 2020 15:55:14 -0700 Subject: [PATCH 01/22] Add growth curve data for MICB322 tutorial --- inst/tutorials/growth_curve/growth_data.csv | 62 ++++++++++++++++++ .../growth_curve/well_information.png | Bin 0 -> 677942 bytes 2 files changed, 62 insertions(+) create mode 100644 inst/tutorials/growth_curve/growth_data.csv create mode 100644 inst/tutorials/growth_curve/well_information.png diff --git a/inst/tutorials/growth_curve/growth_data.csv b/inst/tutorials/growth_curve/growth_data.csv new file mode 100644 index 0000000..4261ad8 --- /dev/null +++ b/inst/tutorials/growth_curve/growth_data.csv @@ -0,0 +1,62 @@ +Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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z9Gs1Tnh?}@0X>k|el`R)1U3XV1U3XV1U3XV1U3Y&K_KJ74im+iQcW?YXEFeU&jt%+ zpC6YJO?h_lOBXFqu8V>$k91co@$b2Gl>frG`HQPOgeUQ&EgT<+n;)K(+fJMKo)JS_ z+<1x){%}wp43x>JDbJC3`BA>S;AQtzeEGTLC0tqti?j?!`DPrJ&+N`+l!v;?6A#r{ zKK^+_9Oa@6C29SSm;Ucx7Pvz^D(k`bPn6w`4gCTzUTL*S}o8+w#t`3;yN!R?Zh+{O!qae*5-_ pi`UOS`)t(PcX5 Date: Mon, 12 Oct 2020 23:19:27 -0700 Subject: [PATCH 02/22] Draft 1 of E coli growth curve analysis tutorial First draft includes code to: merge well data and plate reader data, plot growth curves, calculate growth rates using the "growthrates" library, plot data distribution and boxplots and use a wilcoxen test to determine whether there is a significant difference between media. Code is interactive but questions / text are limited --- inst/growth_data.csv | 62 +++++ inst/resources/data/growth_data.csv | 62 +++++ inst/resources/data/well_information.csv | 97 +++++++ inst/resources/images/growth_data.csv | 62 +++++ inst/resources/images/well_information.csv | 97 +++++++ .../growth_curve/growth_curve_analysis.Rmd | 251 ++++++++++++++++++ .../growth_curve/well_information.csv | 97 +++++++ .../growth_curve/well_information.png | Bin 677942 -> 0 bytes inst/well_information.csv | 97 +++++++ 9 files changed, 825 insertions(+) create mode 100644 inst/growth_data.csv create mode 100644 inst/resources/data/growth_data.csv create mode 100644 inst/resources/data/well_information.csv create mode 100644 inst/resources/images/growth_data.csv create mode 100644 inst/resources/images/well_information.csv create mode 100644 inst/tutorials/growth_curve/growth_curve_analysis.Rmd create mode 100644 inst/tutorials/growth_curve/well_information.csv delete mode 100644 inst/tutorials/growth_curve/well_information.png create mode 100644 inst/well_information.csv diff --git a/inst/growth_data.csv b/inst/growth_data.csv new file mode 100644 index 0000000..4261ad8 --- /dev/null +++ b/inst/growth_data.csv @@ -0,0 +1,62 @@ +Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 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,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial 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+1,251 @@ +--- +title: "E. coli Growth Rate Analysis" +author: "Avery J. C. Noonan
    + Enabled by notes from Stephan Koenig and Linnea, as well as the contribution of several Educe TAs.
    + A tutorial by [ECOSCOPE](http://ecoscope.ubc.ca/) at UBC." +date: "version `r format(Sys.time(), '%B %d, %Y')`" +description: In-depth description will follow. +output: + learnr::tutorial: + progressive: true + allow_skip: true +runtime: shiny_prerendered +--- + +```{r setup, include=FALSE} +# General learnr setup +library(learnr) +knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE) +library(educer) +# Helper function to set path to images to "/images" etc. +setup_resources() + +# Tutorial specific setup +library(tidyverse) +library("growthrates") +library(rstatix) +``` + +## Introduction +*E. coli* cultures were innoculated in a 96-well plate in both rich media (LB) and minimal media (MM). Optical density (OD~600~) measurements were taken over approximately 24 hours, as a metric of cell number, using a microplate reader. + +In this tutorial, we will import optical density (OD~600~) data and plot growth curves of the *E. coli* cultures. We will then use the `growthrates` package to calculate maximum growth rates, before determining whether there s a statistical difference between the growth rates of culture in LB and MM. + +## Environment Setup and Data Import +Necessary packages are first loaded into R, using the `library()` function. In this tutorial, we use the following packages: + +* `tidyverse` +* `"growthrates"` +* `rstatix` + +Files `growth_data.csv` and `well_information.csv` are imported using the `tidyverse` function `read_csv()`. + +```{r import, exercise=TRUE, message=TRUE} +library(tidyverse) +library("growthrates") +library(rstatix) + +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +``` + +## Data Processing +When analyzing data, it is often important to reformat and combine data sets for analysis or visualizations using specific packages or functions. We will use function from the `tidyverse` package to reformat, combine and filter OD~600~ and well information data accordingly. + +```{r data_reformating_setup} +library(tidyverse) +library("growthrates") +library(rstatix) + +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +``` + +```{r data_reformating, exercise=TRUE, exercise.setup = "data_reformating_setup"} +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) + +blank_OD <- growth_data_info %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time)) %>% + group_by(media) %>% + summarise(blank_OD = mean(OD600)) + +growth_data_LB <- growth_data_info %>% + filter(media == 'LB') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'LB') %>% + .$blank_OD) + +growth_data_MM <- growth_data_info %>% + filter(media == 'MM') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'MM') %>% + .$blank_OD) + +growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +``` + +## Plotting Growth Curves + +```{r plotting_growth_curves_setup} +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) + +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) + +blank_OD <- growth_data_info %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time)) %>% + group_by(media) %>% + summarise(blank_OD = mean(OD600)) + +growth_data_LB <- growth_data_info %>% + filter(media == 'LB') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'LB') %>% + .$blank_OD) + +growth_data_MM <- growth_data_info %>% + filter(media == 'MM') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'MM') %>% + .$blank_OD) + +growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +``` + +```{r plotting_growth_curves, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +growth_data_full %>% + filter(!is.na(student), blank == FALSE) %>% + ggplot(aes(x=Time, y=net_OD, color = media, group = media)) + + geom_point(shape=19, size=1) + +growth_data_full %>% + filter(!is.na(student), blank == TRUE) %>% + ggplot(aes(x=Time, y=net_OD, color = media, group = media)) + + geom_point(shape=19, size=1) +``` + +## Calculating Growth Rates + +```{r test_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +test_data <- growth_data_full %>% + filter(well == 'H10') + +fit_test <- fit_easylinear(test_data$Time, test_data$net_OD) + +fit_test@obs %>% + ggplot(aes(x= time, y = y)) + + geom_point() + +coef(fit_test) +plot(fit_test) +``` + +```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +growth_rates <- growth_data_full %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + +growth_rates$growth_rate <- NA +for (w in 1:length(growth_rates$well)){ + growth_rate_data <- growth_data_full %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + + growth_rates$growth_rate[w] = growth_fit@par[[3]] +} +``` +## Data Distribution + +```{r calculating_growth_rates_setup, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +growth_rates <- growth_data_full %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + +growth_rates$growth_rate <- NA +for (w in 1:length(growth_rates$well)){ + growth_rate_data <- growth_data_full %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + + growth_rates$growth_rate[w] = growth_fit@par[[3]] +} +``` + +```{r plotting_growth_rates_setup} +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) + +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) + +blank_OD <- growth_data_info %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time)) %>% + group_by(media) %>% + summarise(blank_OD = mean(OD600)) + +growth_data_LB <- growth_data_info %>% + filter(media == 'LB') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'LB') %>% + .$blank_OD) + +growth_data_MM <- growth_data_info %>% + filter(media == 'MM') %>% + mutate(net_OD = OD600 - blank_OD %>% + filter(media == 'MM') %>% + .$blank_OD) + +growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) + +growth_rates <- growth_data_full %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + +growth_rates$growth_rate <- NA +for (w in 1:length(growth_rates$well)){ + growth_rate_data <- growth_data_full %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + + growth_rates$growth_rate[w] = growth_fit@par[[3]] +} +``` + +```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} +growth_rates %>% + ggplot(aes(x=growth_rate, color = media)) + + geom_density() +``` + +## Statistical Analysis + +```{r staistical_analysis, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} +growth_rates %>% + ggplot(aes(x=media, y = growth_rate, fill = media)) + + geom_boxplot() + +growth_rate_wilcox <- growth_rates %>% + wilcox_test(growth_rate ~ media, paired = FALSE) %>% + add_significance +growth_rate_wilcox +``` diff --git a/inst/tutorials/growth_curve/well_information.csv b/inst/tutorials/growth_curve/well_information.csv new file mode 100644 index 0000000..a046051 --- /dev/null +++ b/inst/tutorials/growth_curve/well_information.csv @@ -0,0 +1,97 @@ +well,student,blank,media +A1,NA,NA,NA +A2,Student A,TRUE,LB +A3,Student A,TRUE,LB +A4,Student A,FALSE,LB +A5,Student A,FALSE,LB +A6,NA,NA,NA +A7,Student B,TRUE,MM +A8,Student B,TRUE,MM +A9,Student B,FALSE,MM +A10,Student B,FALSE,MM +A11,NA,NA,NA +A12,NA,NA,NA +B1,NA,NA,NA +B2,Student C,TRUE,LB +B3,Student C,TRUE,LB +B4,Student C,FALSE,LB +B5,Student C,FALSE,LB +B6,NA,NA,NA +B7,Student D,TRUE,MM +B8,Student 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mode 100644 inst/tutorials/growth_curve/well_information.csv delete mode 100644 inst/well_information.csv diff --git a/inst/growth_data.csv b/inst/growth_data.csv deleted file mode 100644 index 4261ad8..0000000 --- a/inst/growth_data.csv +++ /dev/null @@ -1,62 +0,0 @@ -Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, - ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 -0:00:34,36.9,0.09,0.15,0.15,0.371,0.336,0.094,0.088,0.085,0.183,0.177,0.092,0.094,0.091,0.148,0.147,0.34,0.33,0.095,0.088,0.088,0.191,0.186,0.093,0.093,0.092,0.152,0.148,0.337,0.315,0.094,0.09,0.086,0.186,0.178,0.092,0.091,0.096,0.149,0.15,0.343,0.321,0.092,0.089,0.091,0.19,0.185,0.093,0.097,0.096,0.149,0.153,0.329,0.324,0.092,0.085,0.083,0.193,0.188,0.091,0.091,0.091,0.157,0.151,0.32,0.312,0.092,0.087,0.085,0.178,0.178,0.094,0.095,0.09,0.154,0.148,0.317,0.298,0.092,0.084,0.083,0.178,0.174,0.09,0.09,0.09,0.148,0.146,0.317,0.306,0.091,0.084,0.085,0.189,0.178,0.09,0.091 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-3:00:34,37,0.09,0.152,0.151,0.77,0.728,0.093,0.088,0.086,0.319,0.317,0.092,0.093,0.091,0.149,0.151,0.724,0.715,0.095,0.088,0.088,0.331,0.331,0.093,0.092,0.091,0.154,0.149,0.715,0.722,0.093,0.087,0.087,0.313,0.313,0.091,0.091,0.096,0.149,0.152,0.734,0.695,0.092,0.092,0.092,0.276,0.276,0.093,0.097,0.097,0.151,0.158,0.723,0.735,0.092,0.086,0.085,0.396,0.491,0.091,0.091,0.091,0.157,0.152,0.693,0.695,0.092,0.089,0.086,0.286,0.364,0.094,0.096,0.089,0.155,0.149,0.746,0.69,0.092,0.084,0.084,0.318,0.318,0.09,0.09,0.09,0.149,0.146,0.71,0.689,0.09,0.084,0.085,0.313,0.371,0.09,0.09 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,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, - ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, - ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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Subject: [PATCH 04/22] Updated test in Data Processing --- inst/tutorials/growth_curve/growth_curve_analysis.Rmd | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 0d5dbc0..c188c0c 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -52,6 +52,16 @@ well_data <- read_csv(system.file("resources/data", "well_information.csv", pack ## Data Processing When analyzing data, it is often important to reformat and combine data sets for analysis or visualizations using specific packages or functions. We will use function from the `tidyverse` package to reformat, combine and filter OD~600~ and well information data accordingly. +The key steps in this process are: + +1. `raw_growth_data` reformatting + + Functions: `pivot_longer()` + +2. Joining `growth_data` and `well_data` + + Functions: `right_join()` and `filter()` + +3. Calculating OD~600~ in "blank" wells at T~0~ to generate `blank_OD` + ```{r data_reformating_setup} library(tidyverse) library("growthrates") From 7bfae64552eb8b6bbd689bf0bd6aa468d94c2dbc Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 25 Oct 2020 17:36:13 -0700 Subject: [PATCH 05/22] Growth rate helper functions Adds 3 helper functions to help calculate / visualize maximum growth rates --- .../growth_curve/growth_curve_analysis.Rmd | 107 ++++++++++-------- 1 file changed, 57 insertions(+), 50 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index c188c0c..d297cfe 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -130,6 +130,37 @@ growth_data_MM <- growth_data_info %>% .$blank_OD) growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) + +growthrate_model <- function(filtered_dataframe) { + growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + + output <- growth_fit@par + return(output) +} + +growthrate_plot <- function(filtered_dataframe) { + growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + output <- plot(growth_fit) + return(output) +} + +calc_growthrates <- function(dataframe) { + growth_rates <- dataframe %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + + growth_rates$growth_rate <- NA + for (w in 1:length(growth_rates$well)){ + growth_rate_data <- dataframe %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } + output <- growth_rates + return(output) +} ``` ```{r plotting_growth_curves, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} @@ -146,55 +177,26 @@ growth_data_full %>% ## Calculating Growth Rates +Here, we use the `growthrate_model()` and `growthrate_plot()` function to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. + ```{r test_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} test_data <- growth_data_full %>% - filter(well == 'H10') + filter(well == 'A10') -fit_test <- fit_easylinear(test_data$Time, test_data$net_OD) +growthrate_model(test_data) -fit_test@obs %>% - ggplot(aes(x= time, y = y)) + - geom_point() - -coef(fit_test) -plot(fit_test) +growthrate_plot(test_data) ``` +Use the `calc_growthrate()` function to calculate growth rates for all the wells in a your dataframe, outputting a new dataframe. + ```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -growth_rates <- growth_data_full %>% - distinct(well, .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - -growth_rates$growth_rate <- NA -for (w in 1:length(growth_rates$well)){ - growth_rate_data <- growth_data_full %>% - filter(well == growth_rates$well[w]) - - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) +growth_rates <- calc_growthrates(growth_data_full) - growth_rates$growth_rate[w] = growth_fit@par[[3]] -} +growth_rates ``` ## Data Distribution -```{r calculating_growth_rates_setup, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -growth_rates <- growth_data_full %>% - distinct(well, .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - -growth_rates$growth_rate <- NA -for (w in 1:length(growth_rates$well)){ - growth_rate_data <- growth_data_full %>% - filter(well == growth_rates$well[w]) - - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - - growth_rates$growth_rate[w] = growth_fit@par[[3]] -} -``` - ```{r plotting_growth_rates_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) @@ -225,20 +227,25 @@ growth_data_MM <- growth_data_info %>% growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) -growth_rates <- growth_data_full %>% - distinct(well, .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - -growth_rates$growth_rate <- NA -for (w in 1:length(growth_rates$well)){ - growth_rate_data <- growth_data_full %>% - filter(well == growth_rates$well[w]) +calc_growthrates <- function(dataframe) { + growth_rates <- dataframe %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - - growth_rates$growth_rate[w] = growth_fit@par[[3]] + growth_rates$growth_rate <- NA + for (w in 1:length(growth_rates$well)){ + growth_rate_data <- dataframe %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } + output <- growth_rates + return(output) } + +growth_rates <- calc_growthrates(growth_data_full) ``` ```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} From 2f7629c5665c142ad83e60105727c8638ddf7bf5 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 11:25:33 -0700 Subject: [PATCH 06/22] Blank OD600 helper functions Helper function calc_blank() was added to the Data Processing section --- .../growth_curve/growth_curve_analysis.Rmd | 38 ++++++++++++------- 1 file changed, 24 insertions(+), 14 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index d297cfe..113c36b 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -40,7 +40,7 @@ Necessary packages are first loaded into R, using the `library()` funct Files `growth_data.csv` and `well_information.csv` are imported using the `tidyverse` function `read_csv()`. -```{r import, exercise=TRUE, message=TRUE} +```{r import, message=TRUE} library(tidyverse) library("growthrates") library(rstatix) @@ -60,7 +60,8 @@ The key steps in this process are: 2. Joining `growth_data` and `well_data` + Functions: `right_join()` and `filter()` -3. Calculating OD~600~ in "blank" wells at T~0~ to generate `blank_OD` +3. Calculating OD~600~ in "blank" wells at T~0~ to generate `blank_**` values for different media types + + Functions: `calc_blank(, )` ```{r data_reformating_setup} library(tidyverse) @@ -69,6 +70,18 @@ library(rstatix) raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) + +calc_blank <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% + summarise(blank_OD = mean(OD600)) + + output <- blank_OD[[1]] + + return(output) +} + ``` ```{r data_reformating, exercise=TRUE, exercise.setup = "data_reformating_setup"} @@ -78,23 +91,17 @@ growth_data <- raw_growth_data %>% growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) -blank_OD <- growth_data_info %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time)) %>% - group_by(media) %>% - summarise(blank_OD = mean(OD600)) +blank_LB <- calc_blank(growth_data_info, 'LB') + +blank_MM <- calc_blank(growth_data_info, 'MM') growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'LB') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_LB) growth_data_MM <- growth_data_info %>% filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'MM') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_MM) growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) ``` @@ -140,7 +147,8 @@ growthrate_model <- function(filtered_dataframe) { growthrate_plot <- function(filtered_dataframe) { growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - output <- plot(growth_fit) + plot <- plot(growth_fit) + output <- plot return(output) } @@ -183,6 +191,8 @@ Here, we use the `growthrate_model()` and `growthrate_plot(< test_data <- growth_data_full %>% filter(well == 'A10') +# plot(fit_easylinear(test_data$Time, test_data$net_OD)) + growthrate_model(test_data) growthrate_plot(test_data) From a925fee988fcca84545d561536f6121278ea8a9f Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 11:28:35 -0700 Subject: [PATCH 07/22] Blank OD script update --- .../growth_curve/growth_curve_analysis.Rmd | 38 +++++++++++++------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 113c36b..09a9573 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -112,6 +112,17 @@ growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +calc_blank <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% + summarise(blank_OD = mean(OD600)) + + output <- blank_OD[[1]] + + return(output) +} + growth_data <- raw_growth_data %>% pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') @@ -126,15 +137,11 @@ blank_OD <- growth_data_info %>% growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'LB') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_LB) growth_data_MM <- growth_data_info %>% filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'MM') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_MM) growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) @@ -211,6 +218,17 @@ growth_rates raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +calc_blank <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% + summarise(blank_OD = mean(OD600)) + + output <- blank_OD[[1]] + + return(output) +} + growth_data <- raw_growth_data %>% pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') @@ -225,15 +243,11 @@ blank_OD <- growth_data_info %>% growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'LB') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_LB) growth_data_MM <- growth_data_info %>% filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_OD %>% - filter(media == 'MM') %>% - .$blank_OD) + mutate(net_OD = OD600 - blank_MM) growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) From fe9a1b3c4f55f3a752eebecc2f5d1583680d725f Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 11:43:56 -0700 Subject: [PATCH 08/22] Update Data Processing Section --- .../growth_curve/growth_curve_analysis.Rmd | 55 ++++++++++--------- 1 file changed, 30 insertions(+), 25 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 09a9573..7823f6f 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -38,7 +38,9 @@ Necessary packages are first loaded into R, using the `library()` funct * `"growthrates"` * `rstatix` -Files `growth_data.csv` and `well_information.csv` are imported using the `tidyverse` function `read_csv()`. +Custom function with the suffix `EDU` (`_EDU()`) were designed for this tutorial and are combine functions from the package above. For educational purposes details can be found in the "Helper Function" section of this tutorial. + +Files `growth_data.csv` and `well_information.csv` are imported using the `tidyverse` function `read_csv()`. ```{r import, message=TRUE} library(tidyverse) @@ -61,7 +63,13 @@ The key steps in this process are: + Functions: `right_join()` and `filter()` 3. Calculating OD~600~ in "blank" wells at T~0~ to generate `blank_**` values for different media types - + Functions: `calc_blank(, )` + + Functions: `calc_blank_EDU(, )` + +4. Calculating `net_OD` values (blank OD~600~ values subtracted from measured OD~600~) for each media type separately + + Functions: `filter()` and `mutate() ` + +5. Binding separated dataframes to make a single dataframe with `net_OD` values for all wells and timepoints + + Functions: `bind_rows()` ```{r data_reformating_setup} library(tidyverse) @@ -71,7 +79,7 @@ library(rstatix) raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) -calc_blank <- function(dataframe, Media) { +calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% @@ -91,9 +99,9 @@ growth_data <- raw_growth_data %>% growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) -blank_LB <- calc_blank(growth_data_info, 'LB') +blank_LB <- calc_blank_EDU(growth_data_info, 'LB') -blank_MM <- calc_blank(growth_data_info, 'MM') +blank_MM <- calc_blank_EDU(growth_data_info, 'MM') growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% @@ -108,11 +116,12 @@ growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) ## Plotting Growth Curves + ```{r plotting_growth_curves_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) -calc_blank <- function(dataframe, Media) { +calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% @@ -129,11 +138,9 @@ growth_data <- raw_growth_data %>% growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) -blank_OD <- growth_data_info %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time)) %>% - group_by(media) %>% - summarise(blank_OD = mean(OD600)) +blank_LB <- calc_blank_EDU(growth_data_info, 'LB') + +blank_MM <- calc_blank_EDU(growth_data_info, 'MM') growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% @@ -145,21 +152,21 @@ growth_data_MM <- growth_data_info %>% growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) -growthrate_model <- function(filtered_dataframe) { +growthrate_model_EDU <- function(filtered_dataframe) { growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) output <- growth_fit@par return(output) } -growthrate_plot <- function(filtered_dataframe) { +growthrate_plot_EDU <- function(filtered_dataframe) { growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) plot <- plot(growth_fit) output <- plot return(output) } -calc_growthrates <- function(dataframe) { +calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% distinct(well, .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% @@ -200,15 +207,15 @@ test_data <- growth_data_full %>% # plot(fit_easylinear(test_data$Time, test_data$net_OD)) -growthrate_model(test_data) +growthrate_model_EDU(test_data) -growthrate_plot(test_data) +growthrate_plot_EDU(test_data) ``` Use the `calc_growthrate()` function to calculate growth rates for all the wells in a your dataframe, outputting a new dataframe. ```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -growth_rates <- calc_growthrates(growth_data_full) +growth_rates <- calc_growthrates_EDU(growth_data_full) growth_rates ``` @@ -218,7 +225,7 @@ growth_rates raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) -calc_blank <- function(dataframe, Media) { +calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% @@ -235,11 +242,9 @@ growth_data <- raw_growth_data %>% growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) -blank_OD <- growth_data_info %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time)) %>% - group_by(media) %>% - summarise(blank_OD = mean(OD600)) +blank_LB <- calc_blank_EDU(growth_data_info, 'LB') + +blank_MM <- calc_blank_EDU(growth_data_info, 'MM') growth_data_LB <- growth_data_info %>% filter(media == 'LB') %>% @@ -251,7 +256,7 @@ growth_data_MM <- growth_data_info %>% growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) -calc_growthrates <- function(dataframe) { +calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% distinct(well, .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% @@ -269,7 +274,7 @@ calc_growthrates <- function(dataframe) { return(output) } -growth_rates <- calc_growthrates(growth_data_full) +growth_rates <- calc_growthrates_EDU(growth_data_full) ``` ```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} From d443a16751f266a016ff5d5880c9724ae2e00c4f Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 11:53:10 -0700 Subject: [PATCH 09/22] Update Plotting Growth Rates Text --- inst/tutorials/growth_curve/growth_curve_analysis.Rmd | 2 ++ 1 file changed, 2 insertions(+) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 7823f6f..dddf89e 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -115,7 +115,9 @@ growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) ``` ## Plotting Growth Curves +_E. coli_ growth curves represent the optical density of cultures over time. The shape of these curves has been extensively characterized by the scientific community and plotting these curves can help identify irregularities or contamination. +We are generating these plots using the `ggplot2` package, wihich is part of the `tidyverse` library. ```{r plotting_growth_curves_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) From c8b846bce3e9cc88581493e0ba2f30ceafb46204 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 12:04:48 -0700 Subject: [PATCH 10/22] Update Explanation text --- inst/tutorials/growth_curve/growth_curve_analysis.Rmd | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index dddf89e..8630c53 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -201,7 +201,7 @@ growth_data_full %>% ## Calculating Growth Rates -Here, we use the `growthrate_model()` and `growthrate_plot()` function to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. +Here, we use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` function to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. ```{r test_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} test_data <- growth_data_full %>% @@ -221,7 +221,11 @@ growth_rates <- calc_growthrates_EDU(growth_data_full) growth_rates ``` + ## Data Distribution +Understanding the distribution of data and the relationship between data sets is important in determining what kind of statistical analyses should be performed. + +In the following section, we plot the distribution of the calculated growth rates using the `geom_density` function from `ggplot2` ```{r plotting_growth_rates_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) @@ -286,6 +290,7 @@ growth_rates %>% ``` ## Statistical Analysis +In order to determine whether there is a significant difference between growth rates in LB and MM media, we will employ the `wilcox_test()` function, from the `rstatix` package. Growth rate distributions are also plotted using the `geom_boxplot()` function, from the `ggplot2` package. ```{r staistical_analysis, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} growth_rates %>% From 4f8bbf9fde30d72b11a454e3b44c296aa6cf0590 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sat, 31 Oct 2020 12:15:03 -0700 Subject: [PATCH 11/22] Update Growth Rate Helper Functions --- inst/tutorials/growth_curve/growth_curve_analysis.Rmd | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 8630c53..d823cd7 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -157,15 +157,13 @@ growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) growthrate_model_EDU <- function(filtered_dataframe) { growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - output <- growth_fit@par + output <- growth_fit@par[[3]] return(output) } growthrate_plot_EDU <- function(filtered_dataframe) { - growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - plot <- plot(growth_fit) - output <- plot - return(output) + growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + return(plot(growth_fit)) } calc_growthrates_EDU <- function(dataframe) { @@ -207,8 +205,6 @@ Here, we use the `growthrate_model_EDU()` and `growthrate_pl test_data <- growth_data_full %>% filter(well == 'A10') -# plot(fit_easylinear(test_data$Time, test_data$net_OD)) - growthrate_model_EDU(test_data) growthrate_plot_EDU(test_data) From ce7c5cdf4de7679ddfb18b2a251caed4a6b82227 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 15 Nov 2020 11:16:00 -0800 Subject: [PATCH 12/22] Add Base R t.test() --- inst/tutorials/growth_curve/growth_curve_analysis.Rmd | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index d823cd7..117f1b8 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -12,6 +12,10 @@ output: runtime: shiny_prerendered --- +- Add R base t.test() +- Focus on Friday data +- Make questions + ```{r setup, include=FALSE} # General learnr setup library(learnr) @@ -23,7 +27,6 @@ setup_resources() # Tutorial specific setup library(tidyverse) library("growthrates") -library(rstatix) ``` ## Introduction @@ -293,8 +296,6 @@ growth_rates %>% ggplot(aes(x=media, y = growth_rate, fill = media)) + geom_boxplot() -growth_rate_wilcox <- growth_rates %>% - wilcox_test(growth_rate ~ media, paired = FALSE) %>% - add_significance -growth_rate_wilcox +growth_rate_ttest <- wilcox.test(growth_rate ~ media, data = growth_rates, paired = FALSE) +growth_rate_ttest ``` From 47883d7d498eb5cf3b7689117a1db54215176cab Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 15 Nov 2020 11:21:08 -0800 Subject: [PATCH 13/22] Add new data and rame Friday data to Growth data --- inst/resources/data/Thursday_data.csv | 62 ++++++++++++++++ inst/resources/data/Tuesday_data.csv | 62 ++++++++++++++++ inst/resources/data/growth_data.csv | 102 +++++++++++++------------- 3 files changed, 175 insertions(+), 51 deletions(-) create mode 100644 inst/resources/data/Thursday_data.csv create mode 100644 inst/resources/data/Tuesday_data.csv diff --git a/inst/resources/data/Thursday_data.csv b/inst/resources/data/Thursday_data.csv new file mode 100644 index 0000000..4261ad8 --- /dev/null +++ b/inst/resources/data/Thursday_data.csv @@ -0,0 +1,62 @@ +Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/29/2020_03:45 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,10/02/2020_03:43 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 600 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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a/inst/resources/data/Friday.csv b/inst/resources/data/Friday.csv new file mode 100644 index 0000000..a0df222 --- /dev/null +++ b/inst/resources/data/Friday.csv @@ -0,0 +1,62 @@ +Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,10/02/2020_03:43 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 600 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 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wilcox.test(growth_rate ~ media, data = growth_rates, paired = FALSE) +growth_rate_ttest <- t.test(growth_rate ~ media, data = growth_rates, paired = FALSE) growth_rate_ttest ``` From 859f6054a57439092d56dc252da4ebfbd901be13 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 15 Nov 2020 11:43:59 -0800 Subject: [PATCH 15/22] Add new data and remove 'rstatix' --- inst/resources/data/Friday.csv | 62 ------------------- .../growth_curve/growth_curve_analysis.Rmd | 5 +- 2 files changed, 2 insertions(+), 65 deletions(-) delete mode 100644 inst/resources/data/Friday.csv diff --git a/inst/resources/data/Friday.csv b/inst/resources/data/Friday.csv deleted file mode 100644 index a0df222..0000000 --- a/inst/resources/data/Friday.csv +++ /dev/null @@ -1,62 +0,0 @@ -Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,10/02/2020_03:43 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, - ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 600 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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library("growthrates") -library(rstatix) raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) @@ -77,7 +76,6 @@ The key steps in this process are: ```{r data_reformating_setup} library(tidyverse) library("growthrates") -library(rstatix) raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) @@ -242,7 +240,8 @@ calc_blank_EDU <- function(dataframe, Media) { } growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') %>% + filter(Time != min(Time)) growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) From cd37a9c30cff4a5467e92c30d4517db92ea52234 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 15 Nov 2020 20:18:54 -0800 Subject: [PATCH 16/22] Add questions to Data Processing % Plotting Growth Curves sections --- .../growth_curve/growth_curve_analysis.Rmd | 128 +++++++++++++++--- 1 file changed, 110 insertions(+), 18 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 313a265..8e1ba5b 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -12,13 +12,13 @@ output: runtime: shiny_prerendered --- -- Add R base t.test() -- Focus on Friday data - Make questions ```{r setup, include=FALSE} # General learnr setup library(learnr) +library(gradethis) +gradethis_setup() knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE) library(educer) # Helper function to set path to images to "/images" etc. @@ -39,7 +39,6 @@ Necessary packages are first loaded into R, using the `library()` funct * `tidyverse` * `"growthrates"` -* `rstatix` Custom function with the suffix `EDU` (`_EDU()`) were designed for this tutorial and are combine functions from the package above. For educational purposes details can be found in the "Helper Function" section of this tutorial. @@ -83,17 +82,24 @@ well_data <- read_csv(system.file("resources/data", "well_information.csv", pack calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% - summarise(blank_OD = mean(OD600)) + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) output <- blank_OD[[1]] return(output) } +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) ``` -```{r data_reformating, exercise=TRUE, exercise.setup = "data_reformating_setup"} +The code below shows how the functions described above can be use to reformat `raw_growth_data` into the dataframe that will be used for future analysis. + +```{r data_reformating1, exercise=TRUE, exercise.setup = "data_reformating_setup"} growth_data <- raw_growth_data %>% pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') @@ -115,10 +121,53 @@ growth_data_MM <- growth_data_info %>% growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) ``` +Use the interactive box below to calculate (as shown above) and display the values of `blank_LB` and `blank_MM` variables. + +```{r data_reformating2, exercise=TRUE, exercise.setup = "data_reformating_setup"} +blank_LB <- +print(blank_LB) + +blank_MM <- +print(blank_MM) +``` + +What is the value of `blank_LB`? + +```{r blank_LB_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} + +``` + +```{r blank_LB_check-check} +grade_result( + pass_if(~identical(.result, 0.085)) +) +``` + +What is the value of `blank_MM`? + +```{r blank_MM_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} + +``` + +```{r blank_MM_check-check} +grade_result( + pass_if(~identical(.result, 0.075)) +) +``` + +```{r letter-a, echo=FALSE} +question("Does the relationship between `blank_LB` and `blank_MM` values make sense?", + answer("No, we would expect values to be identical"), + answer("Yes, LB medium is darker coloured that Minimal medium, so we expect the OD~600~ of LB to be slightly higher", correct = TRUE), + answer("No, Minimal medium is darker coloured that LB medium, so we expect the OD~600~ of MM to be slightly higher"), + answer("We have no way of knowing what these values might be") +) +``` + ## Plotting Growth Curves _E. coli_ growth curves represent the optical density of cultures over time. The shape of these curves has been extensively characterized by the scientific community and plotting these curves can help identify irregularities or contamination. -We are generating these plots using the `ggplot2` package, wihich is part of the `tidyverse` library. +We are generating these plots using the `ggplot2` package, which is part of the `tidyverse` library. ```{r plotting_growth_curves_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) @@ -127,8 +176,8 @@ well_data <- read_csv(system.file("resources/data", "well_information.csv", pack calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% - summarise(blank_OD = mean(OD600)) + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) output <- blank_OD[[1]] @@ -186,16 +235,59 @@ calc_growthrates_EDU <- function(dataframe) { } ``` -```{r plotting_growth_curves, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +The code below generates a graph showing all measured OD~600~ values over time. Using the `filter()` function, modify the code below to show only non-blank samples. + +```{r plotting_growth_curves1, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} growth_data_full %>% - filter(!is.na(student), blank == FALSE) %>% - ggplot(aes(x=Time, y=net_OD, color = media, group = media)) + - geom_point(shape=19, size=1) + # filter(blank == FALSE) %>% + ggplot(aes(x=Time, y=net_OD)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` +```{r plotting_growth_curves1-solution} growth_data_full %>% - filter(!is.na(student), blank == TRUE) %>% - ggplot(aes(x=Time, y=net_OD, color = media, group = media)) + - geom_point(shape=19, size=1) + filter(blank == FALSE) %>% + ggplot(aes(x=Time, y=net_OD)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` + +Now copy the code above into the box below and define the `colour` aesthetic as `media`, in order to differentiate between LB and MM. + +```{r plotting_growth_curves2, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} + +``` + +```{r plotting_growth_curves2-solution} +growth_data_full %>% + filter(blank == FALSE) %>% + ggplot(aes(x=Time, y=net_OD, colour = media)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` + +Try modifying the code to show only blank samples and use the `colour` aesthetic as `media`, in order to differentiate between LB and MM. + +```{r plotting_growth_curves3, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} + +``` + +```{r plotting_growth_curves3-solution} +growth_data_full %>% + filter(blank == TRUE) %>% + ggplot(aes(x=Time, y=net_OD, colour = media)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` + +```{r growth_data_question, echo=FALSE} +question("Are there any irregularities in the graphs above? (select ALL that apply)", + answer("Only two blank LB wells grew, we would expect to see more"), + answer("The first measurement seems to have given erroneous values", correct = TRUE), + answer("We don't expect to see any growth in the blank wells, but a number seem to have been contaminated", correct = TRUE), + answer("LB and MM growth curves should look the same, as both are E. coli"), + answer("The data looks as expected")) ``` ## Calculating Growth Rates @@ -231,8 +323,8 @@ well_data <- read_csv(system.file("resources/data", "well_information.csv", pack calc_blank_EDU <- function(dataframe, Media) { blank_OD <- dataframe %>% filter(!is.na(Time)) %>% - filter(blank == TRUE, Time == min(.$Time), media == Media ) %>% - summarise(blank_OD = mean(OD600)) + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) output <- blank_OD[[1]] From b8225441bc7db3b917d11aff9e3e3cceba35f363 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 22 Nov 2020 19:15:42 -0800 Subject: [PATCH 17/22] Add questions / excercises for growth rate calculations --- .../growth_curve/growth_curve_analysis.Rmd | 199 +++++++++++++++++- 1 file changed, 194 insertions(+), 5 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 8e1ba5b..83cd99b 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -18,6 +18,7 @@ runtime: shiny_prerendered # General learnr setup library(learnr) library(gradethis) +library(knitr) gradethis_setup() knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE) library(educer) @@ -160,7 +161,9 @@ question("Does the relationship between `blank_LB` and `blank_MM` values make se answer("No, we would expect values to be identical"), answer("Yes, LB medium is darker coloured that Minimal medium, so we expect the OD~600~ of LB to be slightly higher", correct = TRUE), answer("No, Minimal medium is darker coloured that LB medium, so we expect the OD~600~ of MM to be slightly higher"), - answer("We have no way of knowing what these values might be") + answer("We have no way of knowing what these values might be"), + random_answer_order = TRUE, + allow_retry = TRUE ) ``` @@ -290,22 +293,208 @@ question("Are there any irregularities in the graphs above? (select ALL that app answer("The data looks as expected")) ``` +Use the `filter()` and `min()` functions to remove the first timepoint from the dataset. Plot the growth curves of non-blank cultures. + +```{r plotting_growth_curves4, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} + +``` + +```{r plotting_growth_curves4-hint-1} +Time != # Time is not equal to +``` + +```{r plotting_growth_curves4-hint-2} +Time != min(Time) # Time is not equal to the minimum (first) timepoint +``` + +```{r plotting_growth_curves4-hint-3} +filter(blank == FALSE) +``` + +```{r plotting_growth_curves4-solution} +growth_data_full %>% + filter(blank == FALSE) %>% + # filter(Time != min(Time), blank == TRUE) %>% + ggplot(aes(x=Time, y=net_OD, colour = media)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` + ## Calculating Growth Rates -Here, we use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` function to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. +Here, we use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` functions to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. ```{r test_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -test_data <- growth_data_full %>% +single_well_data <- growth_data_full %>% filter(well == 'A10') -growthrate_model_EDU(test_data) +growthrate_model_EDU(single_well_data) + +growthrate_plot_EDU(single_well_data) +``` + +Use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` functions to calculate and plot maximum growth rates for the following cultures: +```{r table1, echo = FALSE, results = 'asis'} +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) + +calc_blank_EDU <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) + + output <- blank_OD[[1]] + + return(output) +} + +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) + +blank_LB <- calc_blank_EDU(growth_data_info, 'LB') + +blank_MM <- calc_blank_EDU(growth_data_info, 'MM') + +growth_data_LB <- growth_data_info %>% + filter(media == 'LB') %>% + mutate(net_OD = OD600 - blank_LB) + +growth_data_MM <- growth_data_info %>% + filter(media == 'MM') %>% + mutate(net_OD = OD600 - blank_MM) + +growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) + +growthrate_model_EDU <- function(filtered_dataframe) { + growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + + output <- growth_fit@par[[3]] + return(output) +} + +growthrate_plot_EDU <- function(filtered_dataframe) { + growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + return(plot(growth_fit)) +} + +calc_growthrates_EDU <- function(dataframe) { + growth_rates <- dataframe %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + + growth_rates$growth_rate <- NA + for (w in 1:length(growth_rates$well)){ + growth_rate_data <- dataframe %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } + output <- growth_rates + return(output) +} + +table <- growth_data_full %>% + filter(well %in% c('C2', 'C4', 'C8', 'C10')) %>% + filter(Time == min(Time)) %>% + select(c(well, student, blank, media)) + +kable(table, caption = '_Table 1:_ Wells for `growthrate` calculations') +``` + +Using what you've learned, plot the growth curves for all of the wells identified in _Table 1_ + +```{r test_growth_rates1, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} + +``` -growthrate_plot_EDU(test_data) +```{r test_growth_rates1-hint-1} +well == '***' # plot one well at a time filter for all 4 using the OR operator ' | ' +``` + +```{r test_growth_rates1-hint-2} +filter(well == 'C2' | well == 'C4'...) +``` + +```{r test_growth_rates1-solution} +growth_data_full %>% + filter(Time != min(Time)) %>% + filter(well == 'C2' | well == 'C4' | well == 'C8' | well == 'C10') %>% + ggplot(aes(x=Time, y=net_OD, colour = media)) + + geom_point() + + labs(x = 'Time (h:m:s)', y = 'OD600') +``` + +Using the functions described above, calculate and plot growthrates for all non-blank wells identified in _Table 1_ (the `growthrate_model_EDU()` is not dsigned to handle growth rates of near 0) + +```{r test_growth_rates2, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} + +``` + +```{r test_growth_rates2-hint-1} +single_well_data <- growth_data_full %>% # dataframe must be filtered to 1 well + filter(well == 'C4') +``` + +```{r test_growth_rates2-solution} +single_well_data <- growth_data_full %>% + filter(well == 'C8') + +growthrate_model_EDU(single_well_data) + +growthrate_plot_EDU(single_well_data) +``` + +What is the growth rate of well C4? + +```{r C4_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} + +``` + +```{r C4_check-check} +grade_result( + pass_if(~identical(.result, 0.0001456611)) +) +``` + +What is the growth rate of well C10? + +```{r C10_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} + +``` + +```{r C10_check-check} +grade_result( + pass_if(~identical(.result, 9.485003e-05)) +) +``` + +```{r growth_rates_question, echo=FALSE} +question("Is the relationship between _E. coli_ growth rates in LB and MM as expected?", + answer("The max growth rate in LB is more than 1.5 times the max growth rate in MM. We would expect it to be slightly higher.", correct = TRUE), + answer("The max growth rate in LB is more than 10 times the max growth rate in MM. We would expect it to be higher."), + answer("The max growth rate in LB is about 1.5 times the max growth rate in MM. This seem incorect, as we would expect it to be slightly lower."), + answer("We don't have a way of predicting the relationship between growth rates in LB vs. MM"), + answer("The max growth rate in MM is about 1.5 times the max growth rate in LB This seem incorect, as we would expect it to be slightly lower."), + random_answer_order = TRUE, + allow_retry = TRUE + ) ``` Use the `calc_growthrate()` function to calculate growth rates for all the wells in a your dataframe, outputting a new dataframe. ```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +growth_rates <- + +growth_rates +``` + +```{r calculating_growth_rates-solution} growth_rates <- calc_growthrates_EDU(growth_data_full) growth_rates From ff5f6201a797646f4dd1b22e0b8f6cd1348ac476 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 22 Nov 2020 20:04:12 -0800 Subject: [PATCH 18/22] Update growth rate excercises --- .../growth_curve/growth_curve_analysis.Rmd | 157 +++++++++++++----- 1 file changed, 120 insertions(+), 37 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 83cd99b..b55c4d5 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -19,6 +19,7 @@ runtime: shiny_prerendered library(learnr) library(gradethis) library(knitr) +library(berryFunctions) gradethis_setup() knitr::opts_chunk$set(echo = TRUE, message=FALSE, warning=FALSE) library(educer) @@ -293,10 +294,13 @@ question("Are there any irregularities in the graphs above? (select ALL that app answer("The data looks as expected")) ``` -Use the `filter()` and `min()` functions to remove the first timepoint from the dataset. Plot the growth curves of non-blank cultures. +Use the `filter()` and `min()` functions to remove the first timepoint from the dataset. Define a new dataframe called `growth_data_trim` and plot the growth curves of non-blank cultures. ```{r plotting_growth_curves4, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} - +growth_data_trim <- growth_data_full %>% + +growth_data_trim %>% + ggplot(aes(x = , y =)) + ``` ```{r plotting_growth_curves4-hint-1} @@ -312,9 +316,11 @@ filter(blank == FALSE) ``` ```{r plotting_growth_curves4-solution} -growth_data_full %>% +growth_data_trim <- growth_data_full %>% + filter(Time != min(Time)) + +growth_data_trim %>% filter(blank == FALSE) %>% - # filter(Time != min(Time), blank == TRUE) %>% ggplot(aes(x=Time, y=net_OD, colour = media)) + geom_point() + labs(x = 'Time (h:m:s)', y = 'OD600') @@ -324,8 +330,77 @@ growth_data_full %>% Here, we use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` functions to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. -```{r test_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -single_well_data <- growth_data_full %>% +```{r calculating_rates_setup} +raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) +well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) + +calc_blank_EDU <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) + + output <- blank_OD[[1]] + + return(output) +} + +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') + +growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% + filter(!is.na(student), !is.na(Time)) + +blank_LB <- calc_blank_EDU(growth_data_info, 'LB') + +blank_MM <- calc_blank_EDU(growth_data_info, 'MM') + +growth_data_LB <- growth_data_info %>% + filter(media == 'LB') %>% + mutate(net_OD = OD600 - blank_LB) + +growth_data_MM <- growth_data_info %>% + filter(media == 'MM') %>% + mutate(net_OD = OD600 - blank_MM) + +growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) + +growthrate_model_EDU <- function(filtered_dataframe) { + growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + + output <- growth_fit@par[[3]] + return(output) +} + +growthrate_plot_EDU <- function(filtered_dataframe) { + growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) + return(plot(growth_fit)) +} + +calc_growthrates_EDU <- function(dataframe) { + growth_rates <- dataframe %>% + distinct(well, .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + + growth_rates$growth_rate <- NA + for (w in 1:length(growth_rates$well)){ + growth_rate_data <- dataframe %>% + filter(well == growth_rates$well[w]) + + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } + output <- growth_rates + return(output) +} + +growth_data_trim <- growth_data_full %>% + filter(Time != min(Time)) +``` + +```{r test_growth_rates, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +single_well_data <- growth_data_trim %>% filter(well == 'A10') growthrate_model_EDU(single_well_data) @@ -383,24 +458,27 @@ growthrate_plot_EDU <- function(filtered_dataframe) { calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% - distinct(well, .keep_all = TRUE) %>% + distinct(interaction(well), .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% filter(blank == FALSE) growth_rates$growth_rate <- NA for (w in 1:length(growth_rates$well)){ growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) + filter(well == growth_rates$well[w]) - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] + error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) + if (error_check == FALSE) { + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } } output <- growth_rates return(output) } table <- growth_data_full %>% - filter(well %in% c('C2', 'C4', 'C8', 'C10')) %>% + filter(well %in% c('A2', 'A4', 'A8', 'A10')) %>% filter(Time == min(Time)) %>% select(c(well, student, blank, media)) @@ -409,7 +487,7 @@ kable(table, caption = '_Table 1:_ Wells for `growthrate` calculations') Using what you've learned, plot the growth curves for all of the wells identified in _Table 1_ -```{r test_growth_rates1, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +```{r test_growth_rates1, exercise=TRUE, exercise.setup = "calculating_rates_setup"} ``` @@ -418,13 +496,12 @@ well == '***' # plot one well at a time filter for all 4 using the OR operator ' ``` ```{r test_growth_rates1-hint-2} -filter(well == 'C2' | well == 'C4'...) +filter(well == 'A2' | well == 'A4'...) ``` ```{r test_growth_rates1-solution} -growth_data_full %>% - filter(Time != min(Time)) %>% - filter(well == 'C2' | well == 'C4' | well == 'C8' | well == 'C10') %>% +growth_data_trim %>% + filter(well == 'A2' | well == 'A4' | well == 'A8' | well == 'A10') %>% ggplot(aes(x=Time, y=net_OD, colour = media)) + geom_point() + labs(x = 'Time (h:m:s)', y = 'OD600') @@ -432,55 +509,55 @@ growth_data_full %>% Using the functions described above, calculate and plot growthrates for all non-blank wells identified in _Table 1_ (the `growthrate_model_EDU()` is not dsigned to handle growth rates of near 0) -```{r test_growth_rates2, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +```{r test_growth_rates2, exercise=TRUE, exercise.setup = "calculating_rates_setup"} ``` ```{r test_growth_rates2-hint-1} -single_well_data <- growth_data_full %>% # dataframe must be filtered to 1 well - filter(well == 'C4') +single_well_data <- growth_data_trim %>% # dataframe must be filtered to 1 well + filter(well == 'A4') ``` ```{r test_growth_rates2-solution} -single_well_data <- growth_data_full %>% - filter(well == 'C8') +single_well_data <- growth_data_trim %>% + filter(well == 'A4') growthrate_model_EDU(single_well_data) growthrate_plot_EDU(single_well_data) ``` -What is the growth rate of well C4? +What is the growth rate of well A4? -```{r C4_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} +```{r C4_check, exercise=TRUE, exercise.setup = "calculating_rates_setup"} ``` ```{r C4_check-check} grade_result( - pass_if(~identical(.result, 0.0001456611)) + pass_if(~identical(.result, 0.0001247002)) ) ``` -What is the growth rate of well C10? +What is the growth rate of well A10? -```{r C10_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} +```{r C10_check, exercise=TRUE, exercise.setup = "calculating_rates_setup"} ``` ```{r C10_check-check} grade_result( - pass_if(~identical(.result, 9.485003e-05)) + pass_if(~identical(.result, 9.921588e-05)) ) ``` ```{r growth_rates_question, echo=FALSE} question("Is the relationship between _E. coli_ growth rates in LB and MM as expected?", - answer("The max growth rate in LB is more than 1.5 times the max growth rate in MM. We would expect it to be slightly higher.", correct = TRUE), + answer("The max growth rate in LB is more than 1.25 times the max growth rate in MM. We would expect it to be slightly higher.", correct = TRUE), answer("The max growth rate in LB is more than 10 times the max growth rate in MM. We would expect it to be higher."), - answer("The max growth rate in LB is about 1.5 times the max growth rate in MM. This seem incorect, as we would expect it to be slightly lower."), + answer("The max growth rate in LB is about 1.25 times the max growth rate in MM. This seem incorect, as we would expect it to be slightly lower."), answer("We don't have a way of predicting the relationship between growth rates in LB vs. MM"), - answer("The max growth rate in MM is about 1.5 times the max growth rate in LB This seem incorect, as we would expect it to be slightly lower."), + answer("The max growth rate in MM is more than 1.25 times the max growth rate in LB This seem incorect, as we would expect it to be slightly lower."), random_answer_order = TRUE, allow_retry = TRUE ) @@ -488,14 +565,14 @@ question("Is the relationship between _E. coli_ growth rates in LB and MM as exp Use the `calc_growthrate()` function to calculate growth rates for all the wells in a your dataframe, outputting a new dataframe. -```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "calculating_rates_setup"} growth_rates <- growth_rates ``` ```{r calculating_growth_rates-solution} -growth_rates <- calc_growthrates_EDU(growth_data_full) +growth_rates <- calc_growthrates_EDU(growth_data_trim) growth_rates ``` @@ -543,23 +620,29 @@ growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% - distinct(well, .keep_all = TRUE) %>% + distinct(interaction(well), .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% filter(blank == FALSE) growth_rates$growth_rate <- NA for (w in 1:length(growth_rates$well)){ growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) + filter(well == growth_rates$well[w]) - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] + error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) + if (error_check == FALSE) { + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } } output <- growth_rates return(output) } -growth_rates <- calc_growthrates_EDU(growth_data_full) +growth_data_trim <- growth_data_full %>% + filter(Time != min(Time)) + +growth_rates <- calc_growthrates_EDU(growth_data_trim) ``` ```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} From 1e36eceebf7c6179e0d78b8b85fe0502e8a20c14 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Sun, 22 Nov 2020 20:51:11 -0800 Subject: [PATCH 19/22] Update GrowthRates function --- .../growth_curve/growth_curve_analysis.Rmd | 22 ++++++++++++------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index b55c4d5..a0ce391 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -222,17 +222,20 @@ growthrate_plot_EDU <- function(filtered_dataframe) { calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% - distinct(well, .keep_all = TRUE) %>% + distinct(interaction(well), .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% filter(blank == FALSE) growth_rates$growth_rate <- NA for (w in 1:length(growth_rates$well)){ growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) + filter(well == growth_rates$well[w]) - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] + error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) + if (error_check == FALSE) { + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } } output <- growth_rates return(output) @@ -379,17 +382,20 @@ growthrate_plot_EDU <- function(filtered_dataframe) { calc_growthrates_EDU <- function(dataframe) { growth_rates <- dataframe %>% - distinct(well, .keep_all = TRUE) %>% + distinct(interaction(well), .keep_all = TRUE) %>% select(c(well, student, blank, media)) %>% filter(blank == FALSE) growth_rates$growth_rate <- NA for (w in 1:length(growth_rates$well)){ growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) + filter(well == growth_rates$well[w]) - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] + error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) + if (error_check == FALSE) { + growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } } output <- growth_rates return(output) From 35625022c3fdefb456a4d219b38b30f6ae37bf2f Mon Sep 17 00:00:00 2001 From: Noonanav Date: Wed, 25 Nov 2020 08:27:52 -0800 Subject: [PATCH 20/22] Switch to Thursday dataset --- inst/resources/data/Friday_data.csv | 62 +++++++++++ inst/resources/data/Thursday_data.csv | 62 ----------- inst/resources/data/growth_data.csv | 102 +++++++++--------- .../growth_curve/growth_curve_analysis.Rmd | 34 +++++- 4 files changed, 145 insertions(+), 115 deletions(-) create mode 100644 inst/resources/data/Friday_data.csv delete mode 100644 inst/resources/data/Thursday_data.csv diff --git a/inst/resources/data/Friday_data.csv b/inst/resources/data/Friday_data.csv new file mode 100644 index 0000000..a0df222 --- /dev/null +++ b/inst/resources/data/Friday_data.csv @@ -0,0 +1,62 @@ +Protocol Name:,evelyn,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Plate Type: ,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,10/02/2020_03:43 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, + ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 600 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,96-well,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read: ,Kinetic,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Read Speed: ,Normal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Blanks: ,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, - ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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,None,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Shake/Delay: ,Linear Shake (0:00:30),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Date/Time:,10/02/2020_03:43 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Date/Time:,09/30/2020_03:48 PM,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Final Status:,Complete,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Serial Number:,1910078,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, PN/Ver:,1770200 1.09 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, -Raw OD 600 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Raw OD 450 nm,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, Time,Temp,A1 ,A2 ,A3 ,A4 ,A5 ,A6 ,A7 ,A8 ,A9 ,A10 ,A11,A12,B1 ,B2 ,B3 ,B4 ,B5 ,B6 ,B7 ,B8 ,B9 ,B10 ,B11,B12,C1 ,C2 ,C3 ,C4 ,C5 ,C6 ,C7 ,C8 ,C9 ,C10 ,C11,C12,D1 ,D2 ,D3 ,D4 ,D5 ,D6 ,D7 ,D8 ,D9 ,D10 ,D11,D12,E1 ,E2 ,E3 ,E4 ,E5 ,E6 ,E7 ,E8 ,E9 ,E10 ,E11,E12,F1 ,F2 ,F3 ,F4 ,F5 ,F6 ,F7 ,F8 ,F9 ,F10 ,F11,F12,G1 ,G2 ,G3 ,G4 ,G5 ,G6 ,G7 ,G8 ,G9 ,G10 ,G11,G12,H1 ,H2 ,H3 ,H4 ,H5 ,H6 ,H7 ,H8 ,H9 ,H10 ,H11,H12 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determining what kind of statistical analyses should be performed. -In the following section, we plot the distribution of the calculated growth rates using the `geom_density` function from `ggplot2` +In the following section, we plot the distribution of the calculated growth rates using the `geom_density()` function from `ggplot2` ```{r plotting_growth_rates_setup} raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) @@ -651,14 +651,44 @@ growth_data_trim <- growth_data_full %>% growth_rates <- calc_growthrates_EDU(growth_data_trim) ``` +Use what you learned previously about plotting with `ggplot` the distributions of __growthrate__ in the `growth_rates` dataframe you made earlier _(Note that only the `x = growthrate` and `color = media` aesthetics need to be defined for the `geom_density` function)_ + ```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} + +``` + +```{r data_distribution-hint-1} +growth_rates %>% + ggplot(***) + +``` + +```{r data_distribution-hint-2} +growth_rates %>% + ggplot(aes(***)) + + geom_density() +``` + +```{r data_distribution-solution} growth_rates %>% ggplot(aes(x=growth_rate, color = media)) + geom_density() ``` + +```{r data_distribution_shapiro, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} +LB_rates <- growth_rates %>% + filter(media == 'LB') + +MM_rates <- growth_rates %>% + filter(media == 'MM') + +shapiro.test(LB_rates$growth_rate) + +shapiro.test(MM_rates$growth_rate) +``` + ## Statistical Analysis -In order to determine whether there is a significant difference between growth rates in LB and MM media, we will employ the `wilcox_test()` function, from the `rstatix` package. Growth rate distributions are also plotted using the `geom_boxplot()` function, from the `ggplot2` package. +In order to determine whether there is a significant difference between growth rates in LB and MM media, we will employ the `t.test()` function. Growth rate distributions are also plotted using the `geom_boxplot()` function, from the `ggplot2` package. ```{r staistical_analysis, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} growth_rates %>% From 4e0dc70f22519b7330214828812014f52b4e0969 Mon Sep 17 00:00:00 2001 From: Noonanav Date: Wed, 25 Nov 2020 12:41:42 -0800 Subject: [PATCH 21/22] Remove min(Time) filter --- .../growth_curve/growth_curve_analysis.Rmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd index 122dfc7..4fa663a 100644 --- a/inst/tutorials/growth_curve/growth_curve_analysis.Rmd +++ b/inst/tutorials/growth_curve/growth_curve_analysis.Rmd @@ -319,8 +319,8 @@ filter(blank == FALSE) ``` ```{r plotting_growth_curves4-solution} -growth_data_trim <- growth_data_full %>% - filter(Time != min(Time)) +growth_data_trim <- growth_data_full #%>% + # filter(Time != min(Time)) growth_data_trim %>% filter(blank == FALSE) %>% @@ -401,8 +401,8 @@ calc_growthrates_EDU <- function(dataframe) { return(output) } -growth_data_trim <- growth_data_full %>% - filter(Time != min(Time)) +growth_data_trim <- growth_data_full #%>% + # filter(Time != min(Time)) ``` ```{r test_growth_rates, exercise=TRUE, exercise.setup = "calculating_rates_setup"} @@ -604,8 +604,8 @@ calc_blank_EDU <- function(dataframe, Media) { } growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') %>% - filter(Time != min(Time)) + pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') #%>% + # filter(Time != min(Time)) growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% filter(!is.na(student), !is.na(Time)) @@ -645,8 +645,8 @@ calc_growthrates_EDU <- function(dataframe) { return(output) } -growth_data_trim <- growth_data_full %>% - filter(Time != min(Time)) +growth_data_trim <- growth_data_full #%>% + # 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"E. coli Growth Rate Analysis" +title: "E. coli growth rate analysis" author: "Avery J. C. Noonan
      - Enabled by notes from Stephan Koenig and Linnea, as well as the contribution of several Educe TAs.
      + Enabled by notes from Stephan Koenig and Linnea, as well as the contribution of several EDUCE TAs.
      A tutorial by [ECOSCOPE](http://ecoscope.ubc.ca/) at UBC." date: "version `r format(Sys.time(), '%B %d, %Y')`" description: In-depth description will follow. @@ -12,8 +12,6 @@ output: runtime: shiny_prerendered --- -- Make questions - ```{r setup, include=FALSE} # General learnr setup library(learnr) @@ -28,104 +26,187 @@ setup_resources() # Tutorial specific setup library(tidyverse) -library("growthrates") +library(growthrates) + +# Helper functions +calc_blank <- function(dataframe, Media) { + blank_OD <- dataframe %>% + filter(!is.na(Time)) %>% + filter(blank == TRUE, media == Media ) %>% + summarise(blank_OD = min(OD600)) + + blank_OD[[1]] +} + +growthrate_model <- function(filtered_dataframe) { + growth_fit <-fit_easylinear(filtered_dataframe$Time, + filtered_dataframe$net_OD600) + + growth_fit@par[[3]] +} + +growthrate_plot <- function(filtered_dataframe) { + growth_fit <- fit_easylinear(filtered_dataframe$Time, + filtered_dataframe$net_OD600) + return(plot(growth_fit)) +} + +calc_growthrates <- function(dataframe) { + growth_rates <- dataframe %>% + distinct(interaction(well), .keep_all = TRUE) %>% + select(c(well, student, blank, media)) %>% + filter(blank == FALSE) + + growth_rates$growth_rate <- NA + for (w in 1:length(growth_rates$well)){ + growth_rate_data <- dataframe %>% + filter(well == growth_rates$well[w]) + + error_check <- is.error(fit_easylinear(growth_rate_data$Time, + growth_rate_data$net_OD600)) + if (error_check == FALSE) { + growth_fit <- fit_easylinear(growth_rate_data$Time, + growth_rate_data$net_OD600) + growth_rates$growth_rate[w] = growth_fit@par[[3]] + } + } + growth_rates +} + +# Setup for common variables +raw_growth_data <- read_csv(system.file("resources/data", + "growth_data.csv", + package = "educer"), + skip = 12) + + +well_data <- read_csv(system.file("resources/data", + "well_information.csv", + package = "educer")) %>% + filter(!is.na(media)) + +growth_data <- raw_growth_data %>% + pivot_longer(-c(Time, Temp), names_to = "well", values_to = "OD600") %>% + inner_join(well_data, .) + +blank_LB <- calc_blank(growth_data, "LB") +blank_MM <- calc_blank(growth_data, "MM") + +growth_data <- mutate(growth_data, net_OD600 = OD600 - ifelse(media == "LB", + blank_LB, + blank_MM)) + +growth_rates <- calc_growthrates(growth_data) ``` ## Introduction -*E. coli* cultures were innoculated in a 96-well plate in both rich media (LB) and minimal media (MM). Optical density (OD~600~) measurements were taken over approximately 24 hours, as a metric of cell number, using a microplate reader. -In this tutorial, we will import optical density (OD~600~) data and plot growth curves of the *E. coli* cultures. We will then use the `growthrates` package to calculate maximum growth rates, before determining whether there s a statistical difference between the growth rates of culture in LB and MM. +Microbes depend on nutrients in their environment for their growth. But how can we show the impact of growth media on the growth of microbes? Previously, we grew *E. coli* cultures in a 96-well plate in both rich media (LB) and minimal media (MM). Optical density (OD~600~) measurements of these cultures were taken over approximately 24 hours, as a metric of cell number, using a microplate reader. + +In this tutorial, we will import optical density (OD~600~) data and plot growth curves of the *E. coli* cultures. We will then use the growthrates package to calculate maximum growth rates, before determining whether there is a statistical difference in growth rates between LB and MM cultures. -## Environment Setup and Data Import -Necessary packages are first loaded into R, using the `library()` function. In this tutorial, we use the following packages: +The learning objectives for this tutorial are: -* `tidyverse` -* `"growthrates"` +- Use `filter()` to choose specific rows of a data table. +- Demonstrate how to reshape data. +- Demonstrate how to combine data. +- Demonstrate how to plot data using R. +- Explain the assumptions of a t-test. +- Calculate *t*-test. +- Explain what a *p*-value is and how it can be used. +- Describe 1 benefit to using R and RStudio to process data. -Custom function with the suffix `EDU` (`_EDU()`) were designed for this tutorial and are combine functions from the package above. For educational purposes details can be found in the "Helper Function" section of this tutorial. +## Setup and data import -Files `growth_data.csv` and `well_information.csv` are imported using the `tidyverse` function `read_csv()`. +Necessary packages are first loaded into R, using the `library()` function. In this tutorial, we use tidyverse and growthrates packages. -```{r import, message=TRUE} +```{r} library(tidyverse) -library("growthrates") +library(growthrates) +``` + +Custom function were designed for this tutorial. For educational purposes details can be found in the "Helper Function" section of this tutorial. + +We will work with two files. `well_information.csv` contains the metadata for each well, i.e. who set up the well, was it a blank, and what media did it contain. `growth_data.csv` contains the OD~600~ measurements themselves. We will import both files using the `tidyverse` function `read_csv()`. When we look at `growth_data.csv` using a text editor like Notepad (Windows) or TextEdit (macOS), we notice that the first 12 rows contain run-specific information. The actual data, beginning with column names, start in row 13. We can tell `read_csv()` to `skip` the first `12` lines. + +```{r import, message = FALSE} +raw_growth_data <- read_csv(system.file("resources/data", + "growth_data.csv", + package = "educer"), + skip = 12) -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +well_data <- read_csv(system.file("resources/data", + "well_information.csv", + package = "educer")) ``` -## Data Processing -When analyzing data, it is often important to reformat and combine data sets for analysis or visualizations using specific packages or functions. We will use function from the `tidyverse` package to reformat, combine and filter OD~600~ and well information data accordingly. +## Data processing + +When analyzing data, it is often important to reformat data sets so they can be combined. At the moment the metadata and OD~600~ measurements are separate from each other and we need to combine them. After we have the combined data set, we will then subtract the blank reading for use function from the `tidyverse` package to reformat, combine and filter and well information data accordingly. The key steps in this process are: -1. `raw_growth_data` reformatting - + Functions: `pivot_longer()` +1. `raw_growth_data` reformatting with `pivot_longer()`. -2. Joining `growth_data` and `well_data` - + Functions: `right_join()` and `filter()` +2. Remove empty wells from `well_data` using `filter()`. -3. Calculating OD~600~ in "blank" wells at T~0~ to generate `blank_**` values for different media types - + Functions: `calc_blank_EDU(, )` +2. Joining `growth_data` and `well_data` with `inner_join()`. -4. Calculating `net_OD` values (blank OD~600~ values subtracted from measured OD~600~) for each media type separately - + Functions: `filter()` and `mutate() ` - -5. Binding separated dataframes to make a single dataframe with `net_OD` values for all wells and timepoints - + Functions: `bind_rows()` +3. Calculating `net_OD600` values (blank OD~600~ values subtracted from measured OD~600~) for each media type separately using `calc_blank(, )`, `mutate()` and `ifelse()`. -```{r data_reformating_setup} -library(tidyverse) -library("growthrates") +### Transforming data frame from wide to long format -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +Before we can combine metadata and OD~600~ measurements, we have to make sure that they are both in the correct format. Comparing `raw_growth_data` and `well_data`, we can see that each well in `raw_growth_data` has its own column (the well data is "wide") while in `well_data` all wells are collected into a single column (the well data is "long"). For that reason we have to first transform the well data in raw_growth_data` from wide to long (animation by [Garrick Aden‑Buie](https://www.garrickadenbuie.com/project/tidyexplain/). -calc_blank_EDU <- function(dataframe, Media) { - blank_OD <- dataframe %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, media == Media ) %>% - summarise(blank_OD = min(OD600)) +![](/images/pivot_wider_longer.gif) - output <- blank_OD[[1]] - - return(output) -} +We can tranform the data using `pivot_longer()`. The first argument is the data frame, followed by the columns we want to pivot. In our case it is easier to define which columns we do NOT want to pivot. All the names of the well columns will be collected in a new column defined in the `name_to` argument, while all the values of the well columns will be collected in a new column defined in the `values_to` argument. -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') +```{r data_reformating1, exercise = TRUE, message = FALSE} +growth_data <- pivot_longer(raw_growth_data, + - c(Time, Temp), + names_to = "well", + values_to = "OD600") +``` + +### Removing empty wells + +When we explore the `well_data`, we see a lot of wells that were empty, i.e. they only contain `NA`s for all columns other than the well. We can `filter()` the rows by for each row that is not `NA`. `is.na()` checks for `NA`, while `!` means "not" and will give us only the rows that or not `NA`. We will filter out any wells that did not contain any `media`. -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) +```{r} +well_data <- filter(well_data, !is.na(media)) ``` -The code below shows how the functions described above can be use to reformat `raw_growth_data` into the dataframe that will be used for future analysis. +### Combining two data frames -```{r data_reformating1, exercise=TRUE, exercise.setup = "data_reformating_setup"} -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') +We are finally ready to combine the two data frames with `inner_join()`. `inner_join()` takes two data frames and looks for a column that is shared between them (in our case `well`). It then fuses the rows that share the same value in that column. In an `inner_join()` any non-mating row are discarded. For example, since we removed all empty wells in `well_data` those wells are also dropped from `growth_data`. -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) +![](/images/inner_join.gif) -blank_LB <- calc_blank_EDU(growth_data_info, 'LB') +```{r} +growth_data <- inner_join(well_data, growth_data) +``` -blank_MM <- calc_blank_EDU(growth_data_info, 'MM') +### Subtracting blank OD~600~ reading -growth_data_LB <- growth_data_info %>% - filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_LB) +Our data is almost ready for plotting. In our final step we have to first determine the blank OD~600~ value for both media. We have provided you with helper function `calc_blank(, )` that determines the lowest OD~600~ reading for a given medium across the whole experiment. -growth_data_MM <- growth_data_info %>% - filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_MM) +```{r} +blank_LB <- calc_blank(growth_data, "LB") +blank_MM <- calc_blank(growth_data, "MM") +``` -growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +Finally, we want to subtract the blank reading from the bacterial cultures. We can create a new variable using the `mutate(, = )` function. In our case we want to subtract `blank_LB` if the media is `"LB"` and `blank_MM` if media is `"MM"`. We can do so by using `ifelse(, , )`, which returns `` if the `` is `TRUE`, and `` if `FALSE`. + +```{r} +growth_data <- mutate(growth_data, net_OD600 = OD600 - ifelse(media == "LB", + blank_LB, + blank_MM)) ``` Use the interactive box below to calculate (as shown above) and display the values of `blank_LB` and `blank_MM` variables. -```{r data_reformating2, exercise=TRUE, exercise.setup = "data_reformating_setup"} +```{r data_reformating2, exercise = TRUE} blank_LB <- print(blank_LB) @@ -135,7 +216,7 @@ print(blank_MM) What is the value of `blank_LB`? -```{r blank_LB_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} +```{r blank_LB_check, exercise=TRUE} ``` @@ -147,7 +228,7 @@ grade_result( What is the value of `blank_MM`? -```{r blank_MM_check, exercise=TRUE, exercise.setup = "data_reformating_setup"} +```{r blank_MM_check, exercise=TRUE} ``` @@ -168,374 +249,188 @@ question("Does the relationship between `blank_LB` and `blank_MM` values make se ) ``` -## Plotting Growth Curves -_E. coli_ growth curves represent the optical density of cultures over time. The shape of these curves has been extensively characterized by the scientific community and plotting these curves can help identify irregularities or contamination. +## Plotting growth curves -We are generating these plots using the `ggplot2` package, which is part of the `tidyverse` library. +*E. coli* growth curves represent the optical density of cultures over time. The shape of these curves has been extensively characterized by the scientific community and plotting these curves can help identify irregularities or contamination. -```{r plotting_growth_curves_setup} -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) +### Graphics with ggplot2 -calc_blank_EDU <- function(dataframe, Media) { - blank_OD <- dataframe %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, media == Media ) %>% - summarise(blank_OD = min(OD600)) +ggplot2 is an add-on package to R aand is part of the tidyverse. It is an alternative to base graphics that has become very popular, to the point where it is recommended/preferred unless you have old code that already uses a different graphing package. - output <- blank_OD[[1]] - - return(output) -} +*ggplot2* documentation is available at +[docs.ggplot2.org](http://docs.ggplot2.org/current/) -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') +#### Why ggplot? -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) +- Wilkinson, *Grammar of Graphics* (1999) +- ggplot2 is an implementation of Grammar of Graphics for R +- Benefits: + - handsome default settings + - snap-together building block approach + - automatic legends, colors, facets + - statistical overlays: regressions lines and smoothers (with confidence intervals) +- Drawbacks: + - it can be hard to get it to look *exactly* the way you want + - requires having the input data in a certain format -blank_LB <- calc_blank_EDU(growth_data_info, 'LB') +### ggplot building blocks -blank_MM <- calc_blank_EDU(growth_data_info, 'MM') +- data: 2D table (`data.frame`) of *variables* +- *aesthetics*: map variables to visual attributes (*e.g.* position) +- *geoms*: graphical representation of data (points, lines, etc.) +- *stats*: statistical transformations to get from data to points in +the plot(binning, summarizing, smoothing) +- *scales*: control *how* to map a variable to an aesthetic +- *facets*: juxtapose mini-plots of data subsets, split by variable(s) +- *guides*: axes, legend, etc. reflect the variables and their values -growth_data_LB <- growth_data_info %>% - filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_LB) +Idea: independently specify and combine the blocks to create the plot you want. -growth_data_MM <- growth_data_info %>% - filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_MM) +There are at least three things we have to specify to create a plot: -growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +1. Data. +2. Aesthetic mappings from data variables to visual properties. +3. A layer describing how to draw those properties. -growthrate_model_EDU <- function(filtered_dataframe) { - growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - - output <- growth_fit@par[[3]] - return(output) -} +### geom_point -growthrate_plot_EDU <- function(filtered_dataframe) { - growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - return(plot(growth_fit)) -} - -calc_growthrates_EDU <- function(dataframe) { - growth_rates <- dataframe %>% - distinct(interaction(well), .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - - growth_rates$growth_rate <- NA - for (w in 1:length(growth_rates$well)){ - growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) - - error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) - if (error_check == FALSE) { - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] - } - } - output <- growth_rates - return(output) -} -``` +Let's begin by exploring scatterplots in ggplot using `geom_point()`. -The code below generates a graph showing all measured OD~600~ values over time. Using the `filter()` function, modify the code below to show only non-blank samples. +The first argument of ggplot is the data. We can either specify this argument within the ggplot function or pipe it in like so. -```{r plotting_growth_curves1, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -growth_data_full %>% - # filter(blank == FALSE) %>% - ggplot(aes(x=Time, y=net_OD)) + - geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') +```{r eval=FALSE} +ggplot(growth_data) ``` -```{r plotting_growth_curves1-solution} -growth_data_full %>% - filter(blank == FALSE) %>% - ggplot(aes(x=Time, y=net_OD)) + - geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') -``` +The second argument is the aesthetics `aes`, where we specify visual attributes of our plot like the x- and y-variables. -Now copy the code above into the box below and define the `colour` aesthetic as `media`, in order to differentiate between LB and MM. +```{r} +ggplot(growth_data, aes(x = Time, y = net_OD600)) +``` -```{r plotting_growth_curves2, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} +Finally we add the geom to specify how we want to map our data onto these axes. Note how we add the geom by using a `+`. +```{r} +ggplot(growth_data, aes(x = Time, y = net_OD600)) + + geom_point() ``` -```{r plotting_growth_curves2-solution} -growth_data_full %>% - filter(blank == FALSE) %>% - ggplot(aes(x=Time, y=net_OD, colour = media)) + +We can also add custom labels using `labs()`. + +```{r} +ggplot(growth_data, aes(x = Time, y = net_OD600)) + geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') + labs(x = "Time (h:m:s)", y = "OD600") ``` -Try modifying the code to show only blank samples and use the `colour` aesthetic as `media`, in order to differentiate between LB and MM. - -```{r plotting_growth_curves3, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} - -``` +The code below generates a graph showing all measured OD~600~ values over time. Using the `filter()` function, modify the code below to show only non-blank samples. -```{r plotting_growth_curves3-solution} -growth_data_full %>% - filter(blank == TRUE) %>% - ggplot(aes(x=Time, y=net_OD, colour = media)) + +```{r plotting_growth_curves1, exercise = TRUE} +ggplot(growth_data, aes(x = Time, y = net_OD600)) + geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') -``` - -```{r growth_data_question, echo=FALSE} -question("Are there any irregularities in the graphs above? (select ALL that apply)", - answer("Only two blank LB wells grew, we would expect to see more"), - answer("The first measurement seems to have given erroneous values", correct = TRUE), - answer("We don't expect to see any growth in the blank wells, but a number seem to have been contaminated", correct = TRUE), - answer("LB and MM growth curves should look the same, as both are E. coli"), - answer("The data looks as expected")) + labs(x = "Time (h:m:s)", y = "OD600") ``` -Use the `filter()` and `min()` functions to remove the first timepoint from the dataset. Define a new dataframe called `growth_data_trim` and plot the growth curves of non-blank cultures. - -```{r plotting_growth_curves4, exercise=TRUE, exercise.setup = "plotting_growth_curves_setup"} -growth_data_trim <- growth_data_full %>% - -growth_data_trim %>% - ggplot(aes(x = , y =)) + +```{r plotting_growth_curves1-solution} +ggplot(filter(growth_data, blank == FALSE), aes(x = Time, y = net_OD600)) + + geom_point() + + labs(x = "Time (h:m:s)", y = "OD600") ``` -```{r plotting_growth_curves4-hint-1} -Time != # Time is not equal to -``` +Now copy the code above into the box below and define the `colour` aesthetic as `media`, in order to differentiate between LB and MM. -```{r plotting_growth_curves4-hint-2} -Time != min(Time) # Time is not equal to the minimum (first) timepoint -``` +```{r plotting_growth_curves2, exercise=TRUE} -```{r plotting_growth_curves4-hint-3} -filter(blank == FALSE) ``` -```{r plotting_growth_curves4-solution} -growth_data_trim <- growth_data_full #%>% - # filter(Time != min(Time)) - -growth_data_trim %>% - filter(blank == FALSE) %>% - ggplot(aes(x=Time, y=net_OD, colour = media)) + +```{r plotting_growth_curves2-solution} +ggplot(filter(growth_data, blank == FALSE), + aes(x = Time, y = net_OD600, colour = media)) + geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') + labs(x = "Time (h:m:s)", y = "OD600") ``` -## Calculating Growth Rates - -Here, we use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` functions to model and plot the maximum growth rate of a single sample. To do this, first filter your dataframe down to a single `well`. - -```{r calculating_rates_setup} -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) - -calc_blank_EDU <- function(dataframe, Media) { - blank_OD <- dataframe %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, media == Media ) %>% - summarise(blank_OD = min(OD600)) - - output <- blank_OD[[1]] - - return(output) -} - -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') - -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) - -blank_LB <- calc_blank_EDU(growth_data_info, 'LB') - -blank_MM <- calc_blank_EDU(growth_data_info, 'MM') - -growth_data_LB <- growth_data_info %>% - filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_LB) - -growth_data_MM <- growth_data_info %>% - filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_MM) - -growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) - -growthrate_model_EDU <- function(filtered_dataframe) { - growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - - output <- growth_fit@par[[3]] - return(output) -} - -growthrate_plot_EDU <- function(filtered_dataframe) { - growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - return(plot(growth_fit)) -} +Try modifying the code to show only blank samples and use the `colour` aesthetic as `media`, in order to differentiate between LB and MM. -calc_growthrates_EDU <- function(dataframe) { - growth_rates <- dataframe %>% - distinct(interaction(well), .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - - growth_rates$growth_rate <- NA - for (w in 1:length(growth_rates$well)){ - growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) - - error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) - if (error_check == FALSE) { - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] - } - } - output <- growth_rates - return(output) -} +```{r plotting_growth_curves3, exercise = TRUE} -growth_data_trim <- growth_data_full #%>% - # filter(Time != min(Time)) ``` -```{r test_growth_rates, exercise=TRUE, exercise.setup = "calculating_rates_setup"} -single_well_data <- growth_data_trim %>% - filter(well == 'A10') - -growthrate_model_EDU(single_well_data) - -growthrate_plot_EDU(single_well_data) +```{r plotting_growth_curves3-solution} +ggplot(filter(growth_data, blank == TRUE), + aes(x = Time, y = net_OD600, colour = media)) + + geom_point() + + labs(x = "Time (h:m:s)", y = "OD600") ``` -Use the `growthrate_model_EDU()` and `growthrate_plot_EDU()` functions to calculate and plot maximum growth rates for the following cultures: -```{r table1, echo = FALSE, results = 'asis'} -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) - -calc_blank_EDU <- function(dataframe, Media) { - blank_OD <- dataframe %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, media == Media ) %>% - summarise(blank_OD = min(OD600)) - - output <- blank_OD[[1]] - - return(output) -} - -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') - -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) - -blank_LB <- calc_blank_EDU(growth_data_info, 'LB') - -blank_MM <- calc_blank_EDU(growth_data_info, 'MM') - -growth_data_LB <- growth_data_info %>% - filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_LB) +## Calculating growth rates -growth_data_MM <- growth_data_info %>% - filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_MM) +Here, we use the `growthrate_model()` and `growthrate_plot()` functions to model and plot the maximum growth rate of a single sample. To do this, first filter your data frame down to a single `well`. -growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +```{r test_growth_rates, exercise=TRUE} +single_well_data <- filter(growth_data, well == "A10") -growthrate_model_EDU <- function(filtered_dataframe) { - growth_fit <-fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - - output <- growth_fit@par[[3]] - return(output) -} +growthrate_model(single_well_data) -growthrate_plot_EDU <- function(filtered_dataframe) { - growth_fit <- fit_easylinear(filtered_dataframe$Time, filtered_dataframe$net_OD) - return(plot(growth_fit)) -} +growthrate_plot(single_well_data) +``` -calc_growthrates_EDU <- function(dataframe) { - growth_rates <- dataframe %>% - distinct(interaction(well), .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - - growth_rates$growth_rate <- NA - for (w in 1:length(growth_rates$well)){ - growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) - - error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) - if (error_check == FALSE) { - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] - } - } - output <- growth_rates - return(output) -} +Use the `growthrate_model()` and `growthrate_plot()` functions to calculate and plot maximum growth rates for the following cultures: -table <- growth_data_full %>% - filter(well %in% c('A2', 'A4', 'A8', 'A10')) %>% +```{r table1, echo = FALSE, results = "asis"} +table <- growth_data %>% + filter(well %in% c("A2", "A4", "A8", "A10")) %>% filter(Time == min(Time)) %>% select(c(well, student, blank, media)) -kable(table, caption = '_Table 1:_ Wells for `growthrate` calculations') +kable(table, caption = "*Table 1*: Wells for `growthrate` calculations") ``` -Using what you've learned, plot the growth curves for all of the wells identified in _Table 1_ +Using what you've learned, plot the growth curves for all of the wells identified in *Table 1*. -```{r test_growth_rates1, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +```{r test_growth_rates1, exercise = TRUE} ``` ```{r test_growth_rates1-hint-1} -well == '***' # plot one well at a time filter for all 4 using the OR operator ' | ' +well == "***" # plot one well at a time filter for all 4 using the OR operator `|` ``` ```{r test_growth_rates1-hint-2} -filter(well == 'A2' | well == 'A4'...) +filter(well == "A2" | well == "A4"...) ``` ```{r test_growth_rates1-solution} -growth_data_trim %>% - filter(well == 'A2' | well == 'A4' | well == 'A8' | well == 'A10') %>% - ggplot(aes(x=Time, y=net_OD, colour = media)) + +growth_data %>% + filter(well == "A2" | well == "A4" | well == "A8" | well == "A10") %>% + ggplot(aes(x=Time, y=net_OD600, colour = media)) + geom_point() + - labs(x = 'Time (h:m:s)', y = 'OD600') + labs(x = "Time (h:m:s)", y = "OD600") ``` -Using the functions described above, calculate and plot growthrates for all non-blank wells identified in _Table 1_ (the `growthrate_model_EDU()` is not dsigned to handle growth rates of near 0) +Using the functions described above, calculate and plot growthrates for all non-blank wells identified in _Table 1_ (the `growthrate_model()` is not designed to handle growth rates of near 0) -```{r test_growth_rates2, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +```{r test_growth_rates2, exercise = TRUE} ``` ```{r test_growth_rates2-hint-1} -single_well_data <- growth_data_trim %>% # dataframe must be filtered to 1 well - filter(well == 'A4') +single_well_data <- growth_data %>% # dataframe must be filtered to 1 well + filter(well == "A4") ``` ```{r test_growth_rates2-solution} -single_well_data <- growth_data_trim %>% - filter(well == 'A4') +single_well_data <- growth_data %>% + filter(well == "A4") -growthrate_model_EDU(single_well_data) +growthrate_model(single_well_data) -growthrate_plot_EDU(single_well_data) +growthrate_plot(single_well_data) ``` What is the growth rate of well A4? -```{r C4_check, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +```{r C4_check, exercise = TRUE} ``` @@ -547,7 +442,7 @@ grade_result( What is the growth rate of well A10? -```{r C10_check, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +```{r C10_check, exercise = TRUE} ``` @@ -557,7 +452,7 @@ grade_result( ) ``` -```{r growth_rates_question, echo=FALSE} +```{r growth_rates_question, echo = FALSE} question("Is the relationship between _E. coli_ growth rates in LB and MM as expected?", answer("The max growth rate in LB is more than 1.25 times the max growth rate in MM. We would expect it to be slightly higher.", correct = TRUE), answer("The max growth rate in LB is more than 10 times the max growth rate in MM. We would expect it to be higher."), @@ -571,130 +466,101 @@ question("Is the relationship between _E. coli_ growth rates in LB and MM as exp Use the `calc_growthrate()` function to calculate growth rates for all the wells in a your dataframe, outputting a new dataframe. -```{r calculating_growth_rates, exercise=TRUE, exercise.setup = "calculating_rates_setup"} +```{r calculating_growth_rates, exercise = TRUE} growth_rates <- growth_rates ``` ```{r calculating_growth_rates-solution} -growth_rates <- calc_growthrates_EDU(growth_data_trim) +growth_rates <- calc_growthrates(growth_data) growth_rates ``` -## Data Distribution -Understanding the distribution of data and the relationship between data sets is important in determining what kind of statistical analyses should be performed. - -In the following section, we plot the distribution of the calculated growth rates using the `geom_density()` function from `ggplot2` - -```{r plotting_growth_rates_setup} -raw_growth_data <- read_csv(system.file("resources/data", "growth_data.csv", package = "educer"), col_names = TRUE, skip = 12) -well_data <- read_csv(system.file("resources/data", "well_information.csv", package = "educer"), col_names = TRUE) - -calc_blank_EDU <- function(dataframe, Media) { - blank_OD <- dataframe %>% - filter(!is.na(Time)) %>% - filter(blank == TRUE, media == Media ) %>% - summarise(blank_OD = min(OD600)) - - output <- blank_OD[[1]] - - return(output) -} -growth_data <- raw_growth_data %>% - pivot_longer(-c(Time, Temp), names_to = 'well', values_to = 'OD600') #%>% - # filter(Time != min(Time)) +## Statistical analysis -growth_data_info <- right_join(growth_data, well_data, by = 'well') %>% - filter(!is.na(student), !is.na(Time)) +In order to determine whether there is a significant difference between growth rates in LB and MM media, we will employ the `t.test()` function. Growth rate distributions are also plotted using the `geom_boxplot()` function, from the `ggplot2` package. -blank_LB <- calc_blank_EDU(growth_data_info, 'LB') +### *t*-test -blank_MM <- calc_blank_EDU(growth_data_info, 'MM') +- Compares 2 sample means. +- Null hypothesis H~0~: the population means are not different. +- Alternate hypothesis H~1~: the population means are different. +- Hypotheses are about the underlying **populations** even though we only have data from samples. +- The alternate says *nothing* about greater than / less than. -growth_data_LB <- growth_data_info %>% - filter(media == 'LB') %>% - mutate(net_OD = OD600 - blank_LB) +### *t*-test assumptions -growth_data_MM <- growth_data_info %>% - filter(media == 'MM') %>% - mutate(net_OD = OD600 - blank_MM) +1. Simple random sample. +2. Approximately normal distributions of sample means. +3. Reasonably large sample size. +4. Equal sample sizes\*. +5. Equal sample variances\*. -growth_data_full <- bind_rows(growth_data_LB, growth_data_MM) +\* Different equations exist if these are not true. -calc_growthrates_EDU <- function(dataframe) { - growth_rates <- dataframe %>% - distinct(interaction(well), .keep_all = TRUE) %>% - select(c(well, student, blank, media)) %>% - filter(blank == FALSE) - - growth_rates$growth_rate <- NA - for (w in 1:length(growth_rates$well)){ - growth_rate_data <- dataframe %>% - filter(well == growth_rates$well[w]) - - error_check <- is.error(fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD)) - if (error_check == FALSE) { - growth_fit <- fit_easylinear(growth_rate_data$Time, growth_rate_data$net_OD) - growth_rates$growth_rate[w] = growth_fit@par[[3]] - } - } - output <- growth_rates - return(output) -} +### Data distribution -growth_data_trim <- growth_data_full #%>% - # filter(Time != min(Time)) +Understanding the distribution of data and the relationship between data sets is important in determining what kind of statistical analyses should be performed. Since we want to apply a *t*-test, we have to ensure that our data is normally distributed. -growth_rates <- calc_growthrates_EDU(growth_data_trim) -``` +In the following section, we plot the distribution of the calculated growth rates using the `geom_density()` function from `ggplot2` -Use what you learned previously about plotting with `ggplot` the distributions of __growthrate__ in the `growth_rates` dataframe you made earlier _(Note that only the `x = growthrate` and `color = media` aesthetics need to be defined for the `geom_density` function)_ +Use what you learned previously about plotting with `ggplot()` the distributions of **growthrate** in the `growth_rates` data frame you made earlier. *(Note that only the `x = growthrate` and `color = media` aesthetics need to be defined for the `geom_density` function.)* -```{r data_distribution, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} +```{r data_distribution, exercise = TRUE} ``` ```{r data_distribution-hint-1} growth_rates %>% - ggplot(***) + + ggplot() + + ``` ```{r data_distribution-hint-2} growth_rates %>% - ggplot(aes(***)) + + ggplot(aes()) + geom_density() ``` ```{r data_distribution-solution} growth_rates %>% - ggplot(aes(x=growth_rate, color = media)) + + ggplot(aes(x = growth_rate, color = media)) + geom_density() ``` -```{r data_distribution_shapiro, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} +```{r data_distribution_shapiro, exercise = TRUE} LB_rates <- growth_rates %>% - filter(media == 'LB') + filter(media == "LB") MM_rates <- growth_rates %>% - filter(media == 'MM') + filter(media == "MM") shapiro.test(LB_rates$growth_rate) shapiro.test(MM_rates$growth_rate) ``` -## Statistical Analysis -In order to determine whether there is a significant difference between growth rates in LB and MM media, we will employ the `t.test()` function. Growth rate distributions are also plotted using the `geom_boxplot()` function, from the `ggplot2` package. +### *t*-test -```{r staistical_analysis, exercise=TRUE, exercise.setup = "plotting_growth_rates_setup"} -growth_rates %>% - ggplot(aes(x=media, y = growth_rate, fill = media)) + +Now that we have established that the growth rates for each sample are normally distributed, we can perform the *t*-test. + +```{r staistical_analysis, exercise = TRUE} +ggplot(growth_rates, aes(x = media, y = growth_rate, fill = media)) + geom_boxplot() -growth_rate_ttest <- t.test(growth_rate ~ media, data = growth_rates, paired = FALSE) +growth_rate_ttest <- t.test(growth_rate ~ media, + data = growth_rates, + paired = FALSE) growth_rate_ttest ``` + +### The *p*-value + +- Probability value. +- Measure of significance. +- Probability that the difference in means would be $\geq$ the observed results if the true population means are not different (H~0~). +- Probability of a false positive (i.e. you conclude that the population means are different when, in reality, they're actually not).

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