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InferCNV: Inferring copy number variation from tumor single cell RNA-Seq data

InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for large-scale chromosomal copy number variations, such as gains or deletions of entire chromosomes or large segments of chromosomes. This is done by exploring expression intensity of genes across positions of the genome in comparison to a set of reference 'normal' cells. A heatmap is generated illustrating the relative expression intensities across each chromosome, and it often becomes readily apparent as to which regions of the genome are over-abundant or less-abundant as compared to normal cells.

InferCNV is one component of the TrinityCTAT toolkit focused on leveraging the use of RNA-Seq to better understand cancer transcriptomes. To find out more about Trinity CTAT please visit TrinityCTAT.

Quick Start

See Installing inferCNV for installation instructions. You have several choices.

Then move into the inferCNV folder and call the following function:

cd inferCNV/example   

Rscript ./run.R
 

This will run inferCNV on test example data and should produce the figure at the bottom of this page.

Software Requirements

• Python (2.X or 3.X)
• R (tested in R version 3.5.1 (2018-07-02) -- "Feather Spray")
• R libraries required include: RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, Matrix, Seurat, binhf, fastcluster

Data requirements

inferCNV requires:

• a raw counts matrix of single-cell RNA-Seq expression
• an annotations file which indicates which cells are tumor vs. normal.
• a gene/chromosome positions file

See the above example to explore the example inputs provided. These required inputs are described in more detail below.

Raw Counts Matrix for Genes x Cells

InferCNV is compatible with both smart-seq2 and 10x single cell transcriptome data, and presumably other methods (not tested). The counts matrix can be generated using any conventional single cell transcriptome quantification pipeline, yielding a matrix of genes (rows) vs. cells (columns) containing assigned read counts.

The format might look like so:

	MGH54_P16_F12	MGH54_P12_C10	MGH54_P11_C11	MGH54_P15_D06	MGH54_P16_A03	...
A2M	0	0	0	0	0	...
A4GALT	0	0	0	0	0	...
AAAS	0	37	30	21	0	...
AACS	0	0	0	0	2	...
AADAT	0	0	0	0	0	...
...	...	...	...	...	...	...

Citation

Please use the following citation:

Anoop P. Patel, Itay Tirosh, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014 Jun 20: 1396-1401

This methodology was also used in:

Tirosh I et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016 Apr 8;352(6282):189-96

Tirosh I et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature. 2016 Nov 10;539(7628):309-313. PubMed PMID: 27806376; PubMed Central PMCID: PMC5465819.

Venteicher AS, Tirosh I, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science. 2017 Mar 31;355(6332).PubMed PMID: 28360267; PubMed Central PMCID: PMC5519096.

Puram SV, Tirosh I, et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 2017 Dec 14;171(7):1611-1624.e24. PubMed PMID: 29198524; PubMed Central PMCID: PMC5878932.

Demo Example Figure

The following figure should be produced by the Quick Start instructions. This figure shows scRNA-Seq expression of oligodendroglioma with hallmark chr 1p and 19q deletions.

Next steps

Now that you've gotten the example to work, use the menu in the upper right to navigate to the more detailed descriptions and instructions for exploring your own data.