In AntonioDeFalco/SCEVAN: SCEVAN

knitr::opts_chunk$set(echo = TRUE)

Load data

We load the scRNA data of MGH106 sample from the public dataset of Gliobastoma (GSE131928)

load(url("https://www.dropbox.com/s/b9udpvhnc2ez9pc/MGH106_data.RData?raw=1"))

Run pipeline

We run the pipeline that performs the classification of malignant and non-malignant cells of the MGH106 sample using 20 cores and requesting analysis of possible subpopulations.

library(SCEVAN)
results <- SCEVAN::pipelineCNA(count_mtx, sample = "MGH106", par_cores = 20, SUBCLONES = TRUE, plotTree = TRUE)

Output

The pipeline returns in results a data frame containing for each cell its classification (tumor/normal), whether it has been used as a normal confident cell, and to which clonal subpopulation it belongs.

head(results)

And creates an output folder containing all segmentation output files and all the plots generated by the analysis:

list.files(path = "./output", pattern = "MGH106")

Heatmap classification of tumor cells (heatmap.png)

Heatmap of the Copy Number Alteration matrix with classification of non-malignant and malignant cells.

Heatmap of tumour cell subclones (heatmap_subclones.png)

Heatmap of the Copy Number Alteration matrix with the clonal subpopulations found.

Clonal tree (CloneTree.png)

Clonal tree inferred from subsclone profiles.

Consensus plot (consensus.png)

Compact plot of the alterations present in each subpopulation.

OncoPrint-like plot (OncoHeat.png)

OncoPrint-like plot that highlighting specific alteration, shared alteration between subclones, or clonal alteration.

DE analysis in specific alterations (DEchr*_subclones.png)

Vulcano plot obtained from differential expression analysis of the genes belonging to the specific alterations found.

Pathway Analysis of subclones (pathwayAnalysis_subclones*.png)

REACTOME pathways activity obtained with GSEA for each subclone in contrast to the others for each subclone.

AntonioDeFalco/SCEVAN documentation built on April 21, 2024, 7:52 p.m.