Because of the current repository containing large files (i.e. using git-lfs), we first need to clone the repository using git and install git-lfs (instructions below are specific for macOS, please visit the main git-lfs page here to install git-lfs accordingly depending on your OS. Finally, within the repository directory we need to run git lfs pull to replace the text pointers with the actual data files.
Open Terminal and run this command:
git clone https://github.com/olapuentesantana/easier_manuscript.git
brew install git-lfs
cd easier_manuscript/
git lfs pull
Then, in R:
library(devtools)
devtools::install("path/to/easier_manuscript/")
library(easier)
For the essentials on how EaSIeR was developed, we recommend reading the reference paper article.
| Quantitative Descriptors | Descriptor conception | Prior knowledge |
|---|---|---|
| Pathway activity | Holland et al., BBAGRM, 2019; Schubert et al., Nat Commun, 2018 | Holland et al., BBAGRM, 2019; Schubert et al., Nat Commun, 2018 |
| Immune cell quantification | Finotello et al., Genome Med, 2019 | Finotello et al., Genome Med, 2019 |
| Transcription factor activity | Garcia-Alonso et al., Genome Res, 2019 | Garcia-Alonso et al., Genome Res, 2019 |
| Ligand-Receptor pairs | Lapuente-Santana et al., Patterns, 2021 | Ramilowski et al., Nat Commun, 2015 |
| Cell-cell interaction | Lapuente-Santana et al., Patterns, 2021 | Ramilowski et al., Nat Commun, 2015 |
# Computation of cell fractions
cell_fractions <- compute_cell_fractions(RNA.tpm=tpm)
# Computation of pathway activity
pathways_activity <- compute_pathways_scores(RNA.counts=counts, remove.genes.ICB_proxies=TRUE)
# Computation of TF activity
tf_activity <- compute_TF_activity(RNA.tpm=tpm, remove.genes.ICB_proxies=FALSE)
# Computation of LR pairs weights
lrpairs_weights <- compute_LR_pairs(RNA.tpm=tpm, remove.genes.ICB_proxies=FALSE, compute.cytokines.pairs=FALSE, cancertype="pancan")
# Computation of Cell-Cell scores
ccpairs_scores <- compute_CC_pairs_grouped(lrpairs=lrpairs_weights$LRpairs, cancertype="pancan")| Hallmark of the immune response | Original study |
|---|---|
| Cytolytic activity (CYT) | Rooney et al, Cell, 2015 |
| Roh immune score (Roh_IS) | Roh et al., Sci. Transl. Med., 2017 |
| Chemokine signature (chemokines) | Messina et al., Nat. Sci. Rep., 2012 |
| Davoli immune signature (Davoli_IS) | Davoli et al., Science 2017 |
| IFNy signature (IFNy) | Ayers et al., JCI, 2017 |
| Expanded immune signature (Ayers_expIS) | Ayers et al., JCI, 2017 |
| T-cell inflamed signature (Tcell_inflamed) | Ayers et al., JCI, 2017 |
| Repressed immune resistance (RIR) | Jerby-Arnon et al., Cell, 2018 |
| Tertiary lymphoid structures signature (TLS) | Cabrita et al., Nature, 2020 |
tasks <- c("CYT", "Roh_IS", "chemokines", "Davoli_IS", "IFNy", "Ayers_expIS", "Tcell_inflamed", "RIR", "TLS")
immune_response <- compute_gold_standards(RNA.tpm=tpm, list_gold_standards=tasks, cancertype=cancer_type, output_file_path=tmp_file_path)predictions_immune_response <- predict_immune_response(pathways = pathways_activity$scores,
immunecells = cell_fractions,
lrpairs = lrpairs_weights$LRpairs,
tfs = tf_activity$scores,
ccpairs = ccpairs_scores$score,
cancertype = cancertype)
