Set root.dir

vignettes/cicero.Rmd

@@ -29,6 +29,11 @@ create the object from raw data.
 First we will load their dataset and perform some standard preprocessing using
 Signac.
 
+```{r init, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/bmmc")
+```
+
 ```{r include=FALSE}
 setRepositories(ind=1:3)
 if (!requireNamespace("remotes", quietly = TRUE))
@@ -57,7 +62,7 @@ library(patchwork)
 
 ```{r}
 # load the object created in the Monocle 3 vignette
-bone <- readRDS("../vignette_data/cd34.rds")
+bone <- readRDS("cd34.rds")
 ```
 
 ## Create the Cicero object
@@ -152,7 +157,7 @@ CoveragePlot(bone, region = "chr1-40189344-40252549")
 ```
 
 ```{r include=FALSE}
-saveRDS(object = bone, file = "../vignette_data/cd34.rds")
+saveRDS(object = bone, file = "cd34.rds")
 ```
 
 ## Acknowledgements

vignettes/footprint.Rmd

@@ -4,6 +4,12 @@ date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
 output: html_document
 ---
 
+
+```{r init, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/bmmc")
+```
+
 ```{r message=FALSE, warning=FALSE}
 library(Signac)
 library(Seurat)
@@ -15,7 +21,7 @@ For this vignette we'll use the dataset introduced and pre-processed in the
 [trajectory building vignette](monocle.html).
 
 ```{r}
-bone <- readRDS("../vignette_data/cd34.rds")
+bone <- readRDS("cd34.rds")
 DimPlot(bone, label = TRUE)
 ```
 

vignettes/integrate_atac.Rmd

@@ -6,6 +6,7 @@ output: html_document
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data")
 ```
 
 Here we demonstrate the integration of multiple single-cell chromatin datasets
@@ -52,10 +53,10 @@ library(Seurat)
 library(ggplot2)
 
 # load the pre-processed multiome data
-pbmc.multi <- readRDS("../vignette_data/pbmc_multiomic.rds")
+pbmc.multi <- readRDS("pbmc_multiome/pbmc_multiomic.rds")
 
 # load the pre-processed atac data
-pbmc.atac <- readRDS("../vignette_data/pbmc.rds")
+pbmc.atac <- readRDS("pbmc_vignette/pbmc.rds")
 ```
 
 An important first step in any integrative analysis of single-cell chromatin data
@@ -286,7 +287,7 @@ FeaturePlot(
 ```
 
 ```{r include=FALSE}
-saveRDS(object = pbmc.atac, file = "../vignette_data/pbmc_atac_integration.rds")
+saveRDS(object = pbmc.atac, file = "pbmc_atac_integration.rds")
 ```
 
 <details>

vignettes/merging.Rmd

@@ -6,6 +6,7 @@ output: html_document
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data")
 ```
 
 In this vignette we demonstrate how to merge multiple Seurat objects containing 
@@ -113,19 +114,19 @@ options(future.globals.maxSize = 50000 * 1024^2) # for 50 Gb RAM
 ```{r}
 # read in peak sets
 peaks.500 <- read.table(
-  file = "../vignette_data/pbmc500/atac_pbmc_500_nextgem_peaks.bed",
+  file = "pbmc500/atac_pbmc_500_nextgem_peaks.bed",
   col.names = c("chr", "start", "end")
 )
 peaks.1k <- read.table(
-  file = "../vignette_data/pbmc1k/atac_pbmc_1k_nextgem_peaks.bed",
+  file = "pbmc1k/atac_pbmc_1k_nextgem_peaks.bed",
   col.names = c("chr", "start", "end")
 )
 peaks.5k <- read.table(
-  file = "../vignette_data/pbmc5k/atac_pbmc_5k_nextgem_peaks.bed",
+  file = "pbmc5k/atac_pbmc_5k_nextgem_peaks.bed",
   col.names = c("chr", "start", "end")
 )
 peaks.10k <- read.table(
-  file = "../vignette_data/pbmc10k/atac_pbmc_10k_nextgem_peaks.bed",
+  file = "pbmc10k/atac_pbmc_10k_nextgem_peaks.bed",
   col.names = c("chr", "start", "end")
 )
 
@@ -161,31 +162,31 @@ expected cells are present in the file.
 ```{r}
 # load metadata
 md.500 <- read.table(
-  file = "../vignette_data/pbmc500/atac_pbmc_500_nextgem_singlecell.csv",
+  file = "pbmc500/atac_pbmc_500_nextgem_singlecell.csv",
   stringsAsFactors = FALSE,
   sep = ",",
   header = TRUE,
   row.names = 1
 )[-1, ] # remove the first row
 
 md.1k <- read.table(
-  file = "../vignette_data/pbmc1k/atac_pbmc_1k_nextgem_singlecell.csv",
+  file = "pbmc1k/atac_pbmc_1k_nextgem_singlecell.csv",
   stringsAsFactors = FALSE,
   sep = ",",
   header = TRUE,
   row.names = 1
 )[-1, ]
 
 md.5k <- read.table(
-  file = "../vignette_data/pbmc5k/atac_pbmc_5k_nextgem_singlecell.csv",
+  file = "pbmc5k/atac_pbmc_5k_nextgem_singlecell.csv",
   stringsAsFactors = FALSE,
   sep = ",",
   header = TRUE,
   row.names = 1
 )[-1, ]
 
 md.10k <- read.table(
-  file = "../vignette_data/pbmc10k/atac_pbmc_10k_nextgem_singlecell.csv",
+  file = "pbmc10k/atac_pbmc_10k_nextgem_singlecell.csv",
   stringsAsFactors = FALSE,
   sep = ",",
   header = TRUE,
@@ -200,22 +201,22 @@ md.10k <- md.10k[md.10k$passed_filters > 1000, ] # sequenced deeper so set highe
 
 # create fragment objects
 frags.500 <- CreateFragmentObject(
-  path = "../vignette_data/pbmc500/atac_pbmc_500_nextgem_fragments.tsv.gz",
+  path = "pbmc500/atac_pbmc_500_nextgem_fragments.tsv.gz",
   cells = rownames(md.500)
 )
 
 frags.1k <- CreateFragmentObject(
-  path = "../vignette_data/pbmc1k/atac_pbmc_1k_nextgem_fragments.tsv.gz",
+  path = "pbmc1k/atac_pbmc_1k_nextgem_fragments.tsv.gz",
   cells = rownames(md.1k)
 )
 
 frags.5k <- CreateFragmentObject(
-  path = "../vignette_data/pbmc5k/atac_pbmc_5k_nextgem_fragments.tsv.gz",
+  path = "pbmc5k/atac_pbmc_5k_nextgem_fragments.tsv.gz",
   cells = rownames(md.5k)
 )
 
 frags.10k <- CreateFragmentObject(
-  path = "../vignette_data/pbmc10k/atac_pbmc_10k_nextgem_fragments.tsv.gz",
+  path = "pbmc10k/atac_pbmc_10k_nextgem_fragments.tsv.gz",
   cells = rownames(md.10k)
 )
 ```
@@ -323,7 +324,7 @@ CoveragePlot(
 ```
 
 ```{r echo=FALSE, message=FALSE, warning=FALSE}
-saveRDS(object = combined, file = "../vignette_data/pbmc_combined.rds")
+saveRDS(object = combined, file = "pbmc_combined.rds")
 ```
 
 ## Merging without a common feature set
@@ -348,10 +349,10 @@ common feature set:
 
 ```{r message=FALSE, warning=FALSE}
 # load the count matrix for each object that was generated by cellranger
-counts.500 <- Read10X_h5("../vignette_data/pbmc500/atac_pbmc_500_nextgem_filtered_peak_bc_matrix.h5")
-counts.1k <- Read10X_h5("../vignette_data/pbmc1k/atac_pbmc_1k_nextgem_filtered_peak_bc_matrix.h5")
-counts.5k <- Read10X_h5("../vignette_data/pbmc5k/atac_pbmc_5k_nextgem_filtered_peak_bc_matrix.h5")
-counts.10k <- Read10X_h5("../vignette_data/pbmc10k/atac_pbmc_10k_nextgem_filtered_peak_bc_matrix.h5")
+counts.500 <- Read10X_h5("pbmc500/atac_pbmc_500_nextgem_filtered_peak_bc_matrix.h5")
+counts.1k <- Read10X_h5("pbmc1k/atac_pbmc_1k_nextgem_filtered_peak_bc_matrix.h5")
+counts.5k <- Read10X_h5("pbmc5k/atac_pbmc_5k_nextgem_filtered_peak_bc_matrix.h5")
+counts.10k <- Read10X_h5("pbmc10k/atac_pbmc_10k_nextgem_filtered_peak_bc_matrix.h5")
 
 # create objects
 pbmc500_assay <- CreateChromatinAssay(counts = counts.500, sep = c(":", "-"), min.features = 500)

vignettes/mito.Rmd

@@ -12,6 +12,11 @@ if (!requireNamespace(package = "EnsDb.Hsapiens.v75", quietly = TRUE)) {
 }
 ```
 
+```{r init, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/mito")
+```
+
 ```{r packages, message=FALSE, warning=FALSE, echo=TRUE}
 library(Signac)
 library(Seurat)
@@ -72,9 +77,9 @@ standard workflow for scATAC-seq data.
 
 ```{r importData, message=FALSE, warning=FALSE}
 # load counts and metadata from cellranger-atac
-counts <- Read10X_h5(filename = "../vignette_data/mito/CRC_v12-mtMask_mgatk.filtered_peak_bc_matrix.h5")
+counts <- Read10X_h5(filename = "CRC_v12-mtMask_mgatk.filtered_peak_bc_matrix.h5")
 metadata <- read.csv(
-  file = "../vignette_data/mito/CRC_v12-mtMask_mgatk.singlecell.csv",
+  file = "CRC_v12-mtMask_mgatk.singlecell.csv",
   header = TRUE,
   row.names = 1
 )
@@ -92,7 +97,7 @@ crc_assay <- CreateChromatinAssay(
   sep = c(":", "-"),
   annotation = annotations,
   min.cells = 10,
-  fragments = '../vignette_data/mito/CRC_v12-mtMask_mgatk.fragments.tsv.gz'
+  fragments = 'CRC_v12-mtMask_mgatk.fragments.tsv.gz'
 )
 crc <- CreateSeuratObject(
   counts = crc_assay,
@@ -148,7 +153,7 @@ and add it to the Seurat object as a new assay.
 
 ```{r process_mito, message=FALSE, warning=FALSE, echo=TRUE}
 # load mgatk output
-mito.data <- ReadMGATK(dir = "../vignette_data/mito/crc/")
+mito.data <- ReadMGATK(dir = "crc/")
 
 # create an assay
 mito <- CreateAssayObject(counts = mito.data$counts)
@@ -404,7 +409,7 @@ wget https://zenodo.org/record/3977808/files/TF1_filtered.depthTable.txt.gz
 
 ```{r}
 # read the mitochondrial data
-tf1.data <- ReadMGATK(dir = "../vignette_data/mito/tf1/")
+tf1.data <- ReadMGATK(dir = "tf1/")
 
 # create a Seurat object
 tf1 <- CreateSeuratObject(
@@ -415,15 +420,15 @@ tf1 <- CreateSeuratObject(
 
 # load the peak set
 peaks <- read.table(
-  file = "../vignette_data/mito/TF1.filtered.narrowPeak.gz",
+  file = "TF1.filtered.narrowPeak.gz",
   sep = "\t",
   col.names = c("chrom", "start", "end", "peak", "width", "strand", "x", "y", "z", "w")
 )
 peaks <- makeGRangesFromDataFrame(peaks)
 
 # create fragment object
 frags <- CreateFragmentObject(
-  path = "../vignette_data/mito/TF1.filtered.fragments.tsv.gz",
+  path = "TF1.filtered.fragments.tsv.gz",
   cells = colnames(tf1)
 )
 

vignettes/monocle.Rmd

@@ -31,6 +31,11 @@ yourself using [tabix](https://www.htslib.org/doc/tabix.html), for example:
 First we will load the dataset and perform some standard preprocessing using
 Signac.
 
+```{r init, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/bmmc")
+```
+
 ```{r include=FALSE}
 setRepositories(ind=1:3)
 
@@ -61,7 +66,7 @@ library(patchwork)
 ```
 
 ```{r}
-filepath <- "../vignette_data/GSE129785/GSE129785_scATAC-Hematopoiesis-CD34"
+filepath <- "GSE129785_scATAC-Hematopoiesis-CD34"
 
 peaks <- read.table(paste0(filepath, ".peaks.txt.gz"), header = TRUE)
 cells <- read.table(paste0(filepath, ".cell_barcodes.txt.gz"), header = TRUE, stringsAsFactors = FALSE)
@@ -77,7 +82,7 @@ rownames(mtx) <- peaks$Feature
 bone_assay <- CreateChromatinAssay(
   counts = mtx,
   min.cells = 5,
-  fragments = "../vignette_data/GSE129785/GSM3722029_CD34_Progenitors_Rep1_fragments.tsv.gz",
+  fragments = "GSM3722029_CD34_Progenitors_Rep1_fragments.tsv.gz",
   sep = c("_", "_"),
   genome = "hg19"
 )
@@ -238,7 +243,7 @@ reproducibility. This file can be downloaded [here](https://www.dropbox.com/s/w5
 
 ```{r}
 # load the pre-selected HSCs
-hsc <- readLines("../vignette_data/hsc_cells.txt")
+hsc <- readLines("hsc_cells.txt")
 ```
 
 ```{r message=FALSE, warning=FALSE}
@@ -281,7 +286,7 @@ FeaturePlot(bone, c("Erythroid", "Lymphoid"), pt.size = 0.1) & scale_color_virid
 ```
 
 ```{r include=FALSE}
-saveRDS(object = bone, file = "../vignette_data/cd34.rds")
+saveRDS(object = bone, file = "cd34.rds")
 ```
 
 ## Acknowledgements

vignettes/motif_vignette.Rmd

@@ -6,6 +6,7 @@ output: html_document
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/mouse_brain")
 ```
 
 In this tutorial, we will perform DNA sequence motif analysis in Signac. We will
@@ -42,7 +43,7 @@ library(patchwork)
 ```
 
 ```{r message=FALSE, warning=FALSE}
-mouse_brain <- readRDS("../vignette_data/adult_mouse_brain.rds")
+mouse_brain <- readRDS("adult_mouse_brain.rds")
 mouse_brain
 ```
 

vignettes/mouse_brain_vignette.Rmd

@@ -6,6 +6,7 @@ date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
 
 ```{r init, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_knit$set(root.dir = "../vignette_data/mouse_brain")
 ```
 
 For this tutorial, we will be analyzing a single-cell ATAC-seq dataset of adult
@@ -51,9 +52,9 @@ library(patchwork)
 ## Pre-processing workflow
 
 ```{r}
-counts <- Read10X_h5("../vignette_data/atac_v1_adult_brain_fresh_5k_filtered_peak_bc_matrix.h5")
+counts <- Read10X_h5("atac_v1_adult_brain_fresh_5k_filtered_peak_bc_matrix.h5")
 metadata <- read.csv(
-  file = "../vignette_data/atac_v1_adult_brain_fresh_5k_singlecell.csv",
+  file = "atac_v1_adult_brain_fresh_5k_singlecell.csv",
   header = TRUE,
   row.names = 1
 )
@@ -62,7 +63,7 @@ brain_assay <- CreateChromatinAssay(
   counts = counts,
   sep = c(":", "-"),
   genome = "mm10",
-  fragments = '../vignette_data/atac_v1_adult_brain_fresh_5k_fragments.tsv.gz',
+  fragments = 'atac_v1_adult_brain_fresh_5k_fragments.tsv.gz',
   min.cells = 1
 )
 brain <- CreateSeuratObject(
@@ -248,7 +249,7 @@ Alternatively, you can download the pre-processed Seurat object
 
 ```{r warning=FALSE, message=FALSE}
 # Load the pre-processed scRNA-seq data
-allen_rna <- readRDS("../vignette_data/allen_brain.rds")
+allen_rna <- readRDS("allen_brain.rds")
 allen_rna <- UpdateSeuratObject(allen_rna)
 allen_rna <- FindVariableFeatures(
   object = allen_rna,
@@ -354,7 +355,7 @@ CoveragePlot(
 ```
 
 ```{r echo=FALSE, message=FALSE, warning=FALSE}
-saveRDS(object = brain, file = "../vignette_data/adult_mouse_brain.rds")
+saveRDS(object = brain, file = "adult_mouse_brain.rds")
 ```
 
 <details>