fill_neu_segs

Fill neutral regions into consensus segments

internal

A computational method that infers copy number variations (CNVs) in cancer scRNA-seq data and reconstructs the tumor phylogeny. 'numbat' integrates signals from gene expression, allelic ratio, and population haplotype structures to accurately infer allele-specific CNVs in single cells and reconstruct their lineage relationship. 'numbat' can be used to: 1. detect allele-specific copy number variations from single-cells; 2. differentiate tumor versus normal cells in the tumor microenvironment; 3. infer the clonal architecture and evolutionary history of profiled tumors. 'numbat' does not require tumor/normal-paired DNA or genotype data, but operates solely on the donor scRNA-data data (for example, 10x Cell Ranger output). Additional examples and documentations are available at <https://kharchenkolab.github.io/numbat/>. For details on the method please see Gao et al. Nature Biotechnology (2022) <doi:10.1038/s41587-022-01468-y>.

Teng Gao

numbat

Haplotype-Aware CNV Analysis from scRNA-Seq

Ruslan Soldatov

Hirak Sarkar

Evan Biederstedt

Peter Kharchenko

fill_neu_segs function

<dl><dt>segs_consensus</dt>
<dd>dataframe CNV segments from multiple samples</dd>
<dt>segs_neu</dt>
<dd>dataframe Neutral segments</dd></dl>

Arguments

Fill neutral regions into consensus segments — fill_neu_segs

<dl>

<dt>segs_consensus</dt>
<dd>dataframe CNV segments from multiple samples</dd>


<dt>segs_neu</dt>
<dd>dataframe Neutral segments</dd>

</dl>

fill_neu_segs: Fill neutral regions into consensus segments

Description

Usage

Value

Arguments