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Detecting copy number alterations based on RNA-seq data
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20 months ago
igor 7.7k
United States

I've seen a few papers use RNA-seq data to detect CNVs. However, it's always using "custom scripts" in my experience. Is there a publicly available tool for performing that analysis?

I found inferCNV, but that is designed for single-cell data which will have a very different profile from bulk RNA-seq and have a lot more replicates.

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20 months ago
igor 7.7k
United States

I just found HoneyBADGER, another method for single-cell RNA-seq:

HoneyBADGER (hidden Markov model integrated Bayesian approach for detecting CNV and LOH events from single-cell RNA-seq data) identifies and infers the presence of CNV and LOH events in single cells and reconstructs subclonal architecture using allele and expression information from single-cell RNA-sequencing data.

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There is also CONICS:

CONICSmat is an R package that can be used to identify CNVs in single cell RNA-seq data from a gene expression table, without the need of an explicit normal control dataset. CONICSmat works with either full transcript (e.g. Fluidigm C1) or 5'/3' tagged (e.g. 10X Genomics) data.

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Yet another option is CaSpER:

CaSpER is an algorithm for identification, visualization and integrative analysis of CNV events in multiscale resolution using single-cell or bulk RNA sequencing data.

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18 months ago
bhaas • 110
United States

The InferCNV software aims to make the Tirosh & Regev method available as a toolkit:

https://github.com/broadinstitute/inferCNV/wiki

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13 months ago
Washington University in St. Louis, MO

To my amazement, Aviv Regev's lab did it, but I cannot find the paper at the moment (please link it here if you find it!). Suffice to say, it's a complex process, and one which produces only very rough outlines of the CN landscape. You will probably only pick up very large events (multi-Mb), and it will likely depend on having a large cohort with which to average out the noise inherent in expression.

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Is this the paper you are referring to?

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Yeah, I saw Aviv talk at a conference, but that seems like the right paper. Even more flabbergasted that it works with single-cell RNAseq. Amazing stuff!

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