Package index • SCEVAN

Main Pipeline

Core functions for CNA analysis

SCEVAN-package SCEVAN: SCEVAN: R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.

pipelineCNA(): pipelineCNA Executes the entire SCEVAN pipeline that classifies tumour and normal cells from the raw count matrix, infer the clonal profile of cancer cells and looks for possible sub-clones in the tumour cell matrix automatically analysing the specific and shared alterations of each subclone and a differential analysis of pathways and genes expressed in each subclone.

classifyTumorCells(): classifyTumorCells Classify tumour and normal cells from the raw count matrix, using normal cells in the matrix or by subtracting a synthetic baseline from the matrix if there are no normal cells in the matrix.

preprocessingMtx(): preprocessingMtx Pre-processing steps: Cells with less than 200 genes and the genes expressed in less than 1 according to genomic coordinates. Highly confident normal cells are sought in the matrix. Genes involved in the cell cycle pathway are removed. Log-Freeman–Tukey transformation to stabilize variance and a polynomial dynamic linear modeling (DLM) to smooth out the outliers.

Multi-Sample Analysis

Functions for comparing multiple samples

multiSampleComparisonClonalCN(): multiSampleComparisonClonalCN Compare the clonal Copy Number of multiple samples.

plotAllClonalCN(): Title plotAllClonalCN

plotAllSubclonalCN(): plotAllSubclonalCN Plot the copy number of each subclone of a sample.

Visualization

Plotting functions

plotCNA_withAnnotCells(): plotCNA_withAnnotCells allows generating a heatmap of the copy number profile of each cell, adding cell annotations as tracks on the heatmap.

annoteBandOncoHeat(): annoteBandOncoHeat Annotate with chromosome bands the data frame with difference copy number alterations between subclones

Utilities

Helper functions

annotateGenes(): annotateGenes Annotate genes with genomic coordinates with reference to hg38 using Ensembl based annotation package

getBreaksVegaMC(): getBreaksVegaMC Get SCEVAN segmentation of the matrix.

getConfidentNormalCells(): getConfidentNormalCells Get at most top 30 confident normal cells from count matrix.

getCountMtxFromSeurat(): getCountMtxFromSeurat Extract count matrix from Seurat object (V4 and V5 compatible)

top30classification(): Get at most top 30 confident normal cells

Internal Functions

Internal helper functions

classifyCluster(): classifyCluster Classify the two major clusters of CNA matrix on the basis of confident normal cells

computeCNAmtx(): computeCNAmtx computed the CNA matrix using the break points obtained from segmentation

removeSyntheticBaseline(): removeSyntheticBaseline Removes a synthetic baseline from a tumour pure matrix

sortData(): This function sorts a dataset file by the genomic position of the probes.