README.md

In dwassarman/cellpanelr: Analyze Data from Cell Line Panels

cellpanelr

Identify predictive biomarkers from cell line panels. Correlate your data with mutations, gene expression, and more. The goal of cellpanelr is to make “omics” level data analysis accessible and open-source for everyone.

If you want to know more, please see our preprint.

cellpanelr uses data sets adapted from DepMap (Broad Institute) under the CC BY 4.0 license. The current version of cellpanelr uses DepMap release 22Q1.

Web application

The interactive analysis tool is available at https://dwassarman.shinyapps.io/cellpanelr/

Package installation

To install cellpanelr from GitHub, enter the following command in an interactive R session

# install.packages("remotes")
remotes::install_github("dwassarman/cellpanelr")

• Note: if you get the following error Error in loadNamespace(x) : there is no package called ‘remotes’. Remove the # character from the command above to install the remotes package.

Usage

• run_app() runs a local instance of the interactive shiny app
• add_ids() matches cell line names with DepMap IDs for subsequent analysis
• cor_expression() and cor_mutations() correlate cell line response data with gene expression and gene mutations
• data_annotations(), data_expression(), and data_mutations() retrieve modified DepMap data sets for over 1,000 cell lines
• data_nutlin() provides an example data set containing cell line sensitivity to the drug nutlin-3. See the example in the publication folder for a more detailed analysis of this data set or read our preprint.

library(cellpanelr)
library(tidyverse) # Read in data, data joining, pipe operator

# Load data
data <- read_csv("cell_viability.csv")

# Add depmap_id column
# Example: the "Cell line" column contains the cell line names
data <- add_ids(data, cell_col = "Cell line")

# Add cell line annotations for each cell line
annotated <- data %>%
  left_join(
    data_annotations(),
    by = "depmap_id"
  )

# Correlate response column with gene expression
# Example: response values are in the "viability" column
exp_results <- cor_expression(data, response = "viability", ids = "depmap_ids")

# Correlate response column with mutations
# Example: response values are in the "viability" column
mut_results <- cor_mutations(data, response = "viability", ids = "depmap_ids")

Code of Conduct

Please note that the cellpanelr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

dwassarman/cellpanelr documentation built on Jan. 3, 2023, 8:27 a.m.