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Brian Haas edited this page Jan 12, 2023 · 68 revisions

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

InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for somatic large-scale chromosomal copy number alterations, 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 tumor 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 tumor genome are over-abundant or less-abundant as compared to that of normal cells.

InferCNV provides access to several residual expression filters to explore minimizing noise and further revealing the signal supporting CNA. Additionally, inferCNV includes methods to predict CNA regions and define cell clusters according to patterns of heterogeneity.

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.

If you have installed infercnv from BioConductor, you can run the example data with:

library(infercnv)

infercnv_obj = CreateInfercnvObject(raw_counts_matrix=system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package = "infercnv"),
                                    annotations_file=system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv"),
                                    delim="\t",
                                    gene_order_file=system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv"),
                                    ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)")) 

infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=tempfile(), 
                             cluster_by_groups=TRUE, 
                             denoise=TRUE,
                             HMM=TRUE)

If you installed infercnv from github, move into the infercnv/example folder and call the run script:

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

  • JAGS
  • R (tested in R version 3.6.0 (2019-04-26) -- "Planting of a Tree")
  • R libraries required include: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, Matrix, fastcluster, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, reshape, rjags, fitdistrplus, future, foreach, doParallel, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse
  • Python 3 (optional, for some input preparation scripts)

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 here.

Citation

Please use the following citation:

inferCNV of the Trinity CTAT Project.  https://github.com/broadinstitute/inferCNV

This methodology was leveraged in these earlier studies, which inspired the development of the inferCNV software:

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

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. infercnv_image

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.

Questions, comments, etc? Contact us

Please connect with us on our Trinity-CTAT forum at https://groups.google.com/forum/#!forum/trinity_ctat_users

Funding

Funding for the inferCNV project is provided by NCI ITCR as part of the Trinity CTAT Project.

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