cloudos

cloudos R package makes it easy to interact with Lifebit’s CloudOS platform in an R environment.

Installation

You can install the latest release of cloudos from:

• CRAN:

install.packages("cloudos")

• conda-forge:

 conda install -c conda-forge r-cloudos

• GitHub:

if (!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(".")'

Usage

Below is a demonstration of how the cloudos package can be used.

Load the library

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 pipe

Configure CloudOS

This 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:

1. From environment variables CLOUDOS_BASEURL, CLOUDOS_TOKEN, and CLOUDOS_TEAMID.
2. From a cloudos configuration file.

There are three ways to configure the package:

1. Add them to ~/.Renviron in the following way, which will load the environment variables on beginning of the R-session

CLOUDOS_BASEURL="xxx"
CLOUDOS_TOKEN="xxx"
CLOUDOS_TEAMID="xxx"

2. 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")

3. Use the function cloudos_configure(), which will create a ~/.cloudos/config that 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")

Application - Cohort Browser

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.

List Cohorts

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

Create a cohort

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:  v2

Get a cohort

Get 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:  v2

Explore available phenotypes

Search phenotypes

Search 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

Get distribution of cohort participants for a phenotype

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

Update a cohort with a new query

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.

Continuous phenotype

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

Categorical phenotype

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

Filtering cohorts using queries

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] 2524

Any 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] 2883

If 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] 2883

We 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] 1457

Now 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

Retreive the participant table

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 genotypic table

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()

Additional notes

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/

License

MIT © Lifebit