smooth_segs

Smooth the segments after HMM decoding

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

smooth_segs function

<dl><dt>bulk</dt>
<dd>dataframe Pseudobulk profile</dd>
<dt>min_genes</dt>
<dd>integer Minimum number of genes to call a segment</dd></dl>

Arguments

Smooth the segments after HMM decoding — smooth_segs

<dl>

<dt>bulk</dt>
<dd>dataframe Pseudobulk profile</dd>


<dt>min_genes</dt>
<dd>integer Minimum number of genes to call a segment</dd>

</dl>

smooth_segs: Smooth the segments after HMM decoding

Description

Usage

Value

Arguments