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Welcome to the ImmuneResistance wiki!
The purpose of this resource is to enable you to: (1) reproduce the key results of our recent study of immune resistance in melanoma (Jerby-Arnon et al.); and (2) apply our approach to other single-cell cohorts to explore cell-cell interactions in cancer.
- R (tested in R version 3.4.0 (2017-04-21) -- "You Stupid Darkness").
- R libraries: scde, matrixStats, plotrix, plyr, ppcor, survival, ROCR, Hmisc, rms, mixtools, lme4, lmerTest
The data that is required to run the code is provided through the Single Cell Portal (ImmRes_Rfiles.zip).
In the Portal you will also find the processed single-cell gene expression of the clinical cohort and experimental data, as well as interactive views.
To reproduce the results reported in Jerby-Arnon et al. download ImmRes_Rfiles.zip from the Single Cell Portal. Unzip the file and move the resulting Data directory to the ImmuneResistance directory.
In R go to the Code directory and run master.code() which is provided in ImmRes_master.R.
The master.code() will walk you through the different stages of the study, divided into six main analyses:
(1-2) First, analyzing the single-cell data to generate various gene signatures that characterize different cell subtypes and immune resistant cell states. For more information see I. Mapping immune resistance in melanoma
(3-5) Next, analyzing independent cohorts obtained from bulk melanoma tumors to explore and test the immune resistance program. For more details see II. Predicting immunotherapy resistance.
(6) Lastly, performing a pan-cancer analysis to identify drugs that could repress the immune resistance program in cancer cells.
Please use the following citation:
Jerby-Arnon L et al. Single-cell RNA-seq of melanoma ecosystems reveals sources of T cell exclusion linked to immunotherapy clinical outcomes.