cloudos R package makes it easy to interact with Lifebit’s CloudOS platform in an R environment.
You can install the latest release of cloudos from:
- CRAN:
install.packages("cloudos") conda install -c conda-forge r-cloudosif (!require(remotes)) { install.packages("remotes") }
remotes::install_github("lifebit-ai/cloudos")Alternatively, you can install the latest development version of cloudos:
git clone https://github.com/lifebit-ai/cloudos
cd cloudos
git checkout origin/devel
Rscript -e 'devtools::install(".")'Below is a demonstration of how the cloudos package can be used.
library(cloudos)
#>
#> Welcome to Lifebit's CloudOS R client
#> For Documentation visit - https://lifebit-ai.github.io/cloudos/
#> This package is under active development. If you found any issues,
#> Please reach out here - https://github.com/lifebit-ai/cloudos/issues
library(knitr) # For better visualization of wide dataframes in this README examples
library(magrittr) # For pipeThis package is primarily a means of communicating with a CloudOS
instance using it’s API. Before it can communicate with the CloudOS
instance, the package must be configured with some key information: -
The CloudOS base URL. This is the URL in your browser when you navigate
to the Cohort Browser in CloudOS. Often of the form
https://my_instance.lifebit.ai/app/cohort-browser. - The CloudOS
token. Navigate to settings page in CloudOS to generate an API key you
can use as your token (see image below). - The CloudOS team ID. Also
found in the settings page in CloudOS labelled as the “Workspace ID”
(see image below).
The package will look for this information in the following locations in this order:
- From environment variables
CLOUDOS_BASEURL,CLOUDOS_TOKEN, andCLOUDOS_TEAMID. - From a cloudos configuration file.
There are three ways to configure the package:
- Add them to
~/.Renvironin the following way, which will load the environment variables on beginning of the R-session
CLOUDOS_BASEURL="xxx"
CLOUDOS_TOKEN="xxx"
CLOUDOS_TEAMID="xxx"- Add them during an R session using
Sys.setenv(ENV_VAR = "env_var_value")
Sys.setenv(CLOUDOS_BASEURL = "xxx")
Sys.setenv(CLOUDOS_TOKEN = "xxx")
Sys.setenv(CLOUDOS_TEAMID = "xxx")- Use the function
cloudos_configure(), which will create a~/.cloudos/configthat will persist between R sessions and be read from each time (Recommended way if you are using multiple cloudos clients).
cloudos_configure(base_url = "xxx",
token = "xxx",
team_id = "xxx")Below information is out of date, please refer to the latest function docs.
Cohort Browser is part of Lifebit’s CloudOS offering. Let’s explore how to interact with this in R environment.
To check list of available cohorts in a workspace.
cohorts <- cb_list_cohorts()
#> Total number of cohorts found: 3. Showing 10 by default. Change 'size' parameter to return more.
cohorts %>% head(n=5) %>% kable()| id | name | description | number_of_participants | number_of_filters | created_at | updated_at |
|---|---|---|---|---|---|---|
| 610d3004597aa12e251abdf2 | cohort-hms | This cohort is for testing purpose, created from R. | 20778 | 0 | 2021-08-06T12:50:12.242Z | 2021-08-06T13:25:00.192Z |
| 610ac00edb7c7a1d9d0c309f | il_test01 | NA | 415 | 2 | 2021-08-04T16:27:58.708Z | 2021-08-04T16:30:06.253Z |
| 60feab0767a6666b8bf9e11b | Manos Test | NA | 530 | 0 | 2021-07-26T12:31:03.458Z | 2021-08-04T13:02:46.731Z |
To create a new cohort.
my_cohort <- cb_create_cohort(cohort_name = "Cohort-R",
cohort_desc = "This cohort is for testing purpose, created from R.")
#> Cohort created successfully.
my_cohort
#> Cohort ID: 610d47d7597aa12e251abdf4
#> Cohort Name: Cohort-R
#> Cohort Description: This cohort is for testing purpose, created from R.
#> Number of phenotypes in query: 1
#> Cohort Browser version: v2Get a available cohort in to a cohort R object. This cohort object can be used in many different other functions.
other_cohort <- cb_load_cohort(cohort_id = "610ac00edb7c7a1d9d0c309f")
other_cohort
#> Cohort ID: 610ac00edb7c7a1d9d0c309f
#> Cohort Name: il_test01
#> Cohort Description:
#> Number of phenotypes in query: 2
#> Cohort Browser version: v2Search for phenotypes based on a term. Searching with term = "" will
return all the available phenotypes.
disease_phenotypes <- cb_search_phenotypes(term = "disease")
#> Total number of phenotypic filters found - 18
disease_phenotypes %>% head(n=5) %>% kable()| id | name | description | array | type | valueType | units | bucket500 | bucket1000 | bucket2500 | bucket5000 | bucket300 | bucket10000 | categoryPathLevel1 | categoryPathLevel2 | instances | Sorting | coding | descriptionParticipantsNo | link | descriptionStability | descriptionCategoryID | descriptionItemType | descriptionStrata | descriptionSexed | orderPhenotype | instance0Name | instance1Name | instance2Name | instance3Name | instance4Name | instance5Name | instance6Name | instance7Name | instance8Name | instance9Name | instance10Name | instance11Name | instance12Name | instance13Name | instance14Name | instance15Name | instance16Name |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28 | Rare diseases family sk | Database identifier for a rare disease family | 1 | text_search | Text | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Basic characteristics | NA | 1 | 89132 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme | |||||||||||||||||||||||||
| 29 | Rare diseases family id | A locally-allocated family identifier assigned to the proband and their relatives. This should be unique to this duo or trio within the GMC and is necessary for linking related participants. | 1 | text_search | Text | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Basic characteristics | NA | 1 | 89132 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme | |||||||||||||||||||||||||
| 177 | Cancer disease sub type (HPO) | The subtype of the cancer in question, recorded against a limited set of supplied enumerations. | 4 | bars | Categorical multiple | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Cancer | Participant disease | 1 | 17404 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme | |||||||||||||||||||||||||
| 178 | Cancer disease type | The cancer type of the tumour sample submitted to Genomics England. | 4 | bars | Categorical multiple | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Cancer | Participant disease | 1 | 17404 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme | |||||||||||||||||||||||||
| 206 | Disease group | Top-level classification of rare diseases (project specific) | 5 | bars | Categorical multiple | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Rare disease | Participant disease | 1 | 39913 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme |
Let’s choose a phenotype from the above table. The “id” is the most important part as it will allow us to use this phenotype for cohort queries and other functions.
# get the first row/phenotype in the table
my_phenotype <- disease_phenotypes[5,]
my_phenotype %>% kable()| id | name | description | array | type | valueType | units | bucket500 | bucket1000 | bucket2500 | bucket5000 | bucket300 | bucket10000 | categoryPathLevel1 | categoryPathLevel2 | instances | Sorting | coding | descriptionParticipantsNo | link | descriptionStability | descriptionCategoryID | descriptionItemType | descriptionStrata | descriptionSexed | orderPhenotype | instance0Name | instance1Name | instance2Name | instance3Name | instance4Name | instance5Name | instance6Name | instance7Name | instance8Name | instance9Name | instance10Name | instance11Name | instance12Name | instance13Name | instance14Name | instance15Name | instance16Name |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 206 | Disease group | Top-level classification of rare diseases (project specific) | 5 | bars | Categorical multiple | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | Rare disease | Participant disease | 1 | 39913 | https://cnfl.extge.co.uk/pages/viewpage.action?pageId=147659370 | Main 100k Programme |
Let’s check the numbers of participants across the categories of this phenotype.
# phenotype
my_pheno_data <- cb_get_phenotype_statistics(cohort = my_cohort,
pheno_id = my_phenotype$id)
my_pheno_data %>% head(n=10) %>% kable()| _id | number | total |
|---|---|---|
| Metabolic disorders | 125 | 5090 |
| Ultra-rare disorders | 272 | 5090 |
| dysmorphic and congenital abnormality syndromes | 3 | 5090 |
| Skeletal disorders | 109 | 5090 |
| Respiratory disorders | 37 | 5090 |
| Endocrine disorders | 121 | 5090 |
| Dermatological disorders | 68 | 5090 |
| Tumour syndromes | 228 | 5090 |
| tumour syndromes | 3 | 5090 |
| Psychiatric disorders | 5 | 5090 |
A query defines what particpants are included in a cohort based on phenotypes.
Phenotypes can be continuous - in which case a selected range needs to be specified, or they can be categorical - in which case selected categories need to be specified.
For phenotype “Year of birth” (with id = 8)
# cb_get_phenotype_metadata(8)$name
# "Year of birth"
cb_get_phenotype_statistics(cohort = my_cohort, pheno_id = 8) %>% head(n=10) %>% kable()| _id | number | total |
|---|---|---|
| 1923 | 3 | 44667 |
| 1924 | 9 | 44667 |
| 1925 | 8 | 44667 |
| 1926 | 4 | 44667 |
| 1927 | 16 | 44667 |
| 1928 | 36 | 44667 |
| 1929 | 47 | 44667 |
| 1930 | 60 | 44667 |
| 1931 | 81 | 44667 |
| 1932 | 105 | 44667 |
For phenotype “Total full brothers” (with id = 48).
# cb_get_phenotype_metadata(48)$name
# "Total full brothers"
cb_get_phenotype_statistics(cohort = my_cohort, pheno_id = 48) %>% kable()| _id | number | total |
|---|---|---|
| 0 | 4248 | 13276 |
| 1 | 7791 | 13276 |
| 2 | 1237 | 13276 |
Now let’s restrict our cohort to a set of participants based on the phenotypes we explored above.
A single phenotype query can be defined using the phenotype function.
# total full brothers: 1
categorical_query <- phenotype(id = 48, value = 1)
# year of birth: 1965 - 1995
continuous_query <- phenotype(id = 8, from = 1965, to = 1995)To combine single phenotype queries, you can use &, | and ! operators.
query <- categorical_query & continuous_query
cb_participant_count(cohort = my_cohort, query = query, keep_query = F)
#> $total
#> [1] 44667
#>
#> $count
#> [1] 2524Any number of single phenotypes can be combined using any combination of operators. The order in which logic is resolved follows the usual rules and can be controlled using brackets.
categorical_query_2 <- phenotype(id = 48, value = 2)
query <- (categorical_query | categorical_query_2) & continuous_query
cb_participant_count(cohort = my_cohort, query = query, keep_query = F)
#> $total
#> [1] 44667
#>
#> $count
#> [1] 2883If we’re happy that this is a sensible query to apply, we can apply the
query to the cohort, making sure to override the previous query by
setting keep_query to FALSE. If we wanted to keep the criteria from
the pre-exisitng query and add our new phenotype-based criteria to them
we would leave keep_query set to the defualt value of TRUE.
# apply the query
cb_apply_query(cohort = my_cohort, query = query, keep_query = F)
#> Query applied sucessfully.
# update the local cohort object with info from the changed version on the server
my_cohort <- cb_load_cohort(my_cohort@id)
# double check that the cohort has th number of participants we expected
cb_participant_count(cohort = my_cohort)
#> $total
#> [1] 44667
#>
#> $count
#> [1] 2883We could now further restrict our cohort to include only females
(phenotype “Participant phenotypic sex”, id = 10) by using
keep_query = TRUE. In other words, this argument applies a query that
looks like “old query AND new query”.
new_query <- phenotype(id = 10, value = "Female")
# apply the query
cb_apply_query(cohort = my_cohort, query = new_query, keep_query = T)
#> Query applied sucessfully.
# update the local cohort object with info from the changed version on the server
my_cohort <- cb_load_cohort(my_cohort@id)
# check the number of participants
cb_participant_count(my_cohort)
#> $total
#> [1] 44667
#>
#> $count
#> [1] 1457Now that the query has been applied to our cohort, let's inspect the distribution of our phenotype of interest in the cohort.
# view the distribution of disease groups in our cohort
cb_get_phenotype_statistics(cohort = my_cohort, pheno_id = 206) %>% head(n=10) %>% kable()| _id | number | total |
|---|---|---|
| Hearing and ear disorders | 33 | 1731 |
| Growth disorders | 14 | 1731 |
| Endocrine disorders | 34 | 1731 |
| Dermatological disorders | 31 | 1731 |
| Respiratory disorders | 8 | 1731 |
| dysmorphic and congenital abnormality syndromes | 2 | 1731 |
| Skeletal disorders | 37 | 1731 |
| Ophthalmological disorders | 148 | 1731 |
| neurology and neurodevelopmental disorders | 4 | 1731 |
| Tumour syndromes | 76 | 1731 |
Now lets get a participant phenotype table with the columns of interest for our cohort.
First we have to update the cohort on the cohort browser server to set
what columns will be in the table. Currently the best way to do this is
to use (counterintuitively) cb_apply_query to add the IDs of the
phenotypes of interest as columns.
cb_apply_query(my_cohort, column_ids = c(208, 10, 8, 48), keep_columns = T)
#> Query applied sucessfully.
my_cohort <- cb_load_cohort(my_cohort@id)Now we can fetch the participant phenotype table which includes these columns.
pheno_df <- cb_get_participants_table(cohort = my_cohort,
page_size = cb_participant_count(my_cohort)$count)
pheno_df %>% head(n=10) %>% kable()| EID | Programme | Handling gmc | Year of birth | Participant ethnic category | Participant karyotypic sex | Participant type | Specific disease | Participant phenotypic sex | Total full brothers |
|---|---|---|---|---|---|---|---|---|---|
| 1000020 | Rare Diseases | North Thames | 1970 | Not Stated | Unknown | Relative | NA | Female | NA |
| 1000397 | Rare Diseases | North Thames | 1999 | Mixed: White and Asian | Not Supplied | Proband | NA | Female | 2 |
| 1000411 | Cancer | Yorkshire and Humber | 1966 | White: British | NA | NA | NA | Female | NA |
| 1000673 | Rare Diseases | Genomics Network Alliance | 2012 | Asian or Asian British: Pakistani | Not Supplied | Proband | Osteogenesis imperfecta | Female | 1 |
| 1001010 | Rare Diseases | West Midlands | 1970 | White: British | Not Supplied | Proband | NA | Female | 0 |
| 1001033 | Rare Diseases | North East and Cumbria | 1986 | Not Stated | Not Supplied | Proband | Intellectual disability | Female | NA |
| 1001429 | Rare Diseases | West Midlands | 1981 | White: British | Not Supplied | Relative | NA | Female | NA |
| 1001667 | Rare Diseases | Genomics Network Alliance | 1986 | White: British | Not Supplied | Relative | NA | Female | NA |
| 1001712 | Rare Diseases | Genomics Network Alliance | 1983 | White: British | Not Supplied | Relative | NA | Female | NA |
| 1001749 | Cancer | West London | 1965 | Not Known | NA | NA | NA | Female | NA |
Get the genotypic table for a cohort (currently only cohort browser version 1 is supported).
cohort_genotype <- cb_get_genotypic_table(cohort = my_cohort)
cohort_genotype %>% head(n=2) %>% kable()This package is under active development. If you find any issues, please reach out here - https://github.com/lifebit-ai/cloudos/issues
For documentation visit - https://lifebit-ai.github.io/cloudos/
MIT © Lifebit