library(recount3)
#
# ## Download all available projects in human in recount3
# human_projects <- available_projects()
#
# ## Download gene expression data and metadata from
# proj_info <- subset(
# human_projects,
# project == "SRP107565" & project_type == "data_sources"
# )
#
#
# rse_gene_SRP009615 <- create_rse(project_info)Understading coefficients in linear models fits for RNA-seq data
coefficient
beta
interaction terms
contrast
multi-level variable
⚠️ This page is under development.
Introduction
What you’ll learn here
- How to interpret the coefficients in the design matrices.
- Understand the syntaxis of interaction coefficients and learn how to interpret and utilize them.
An example data set
We’ll use recount3 package to download real gene expression data from a study, including sample metadata and QC metrics that will be helpful to illustrate variable associations with gene expression data.
The chosen study can be found in the Sequence Read Archive (SRA) with project ID SRP107565, and is titled “Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity”.