Harnesses genetic variation to determine the genetic identity of each droplet containing a single cell. Demuxlet can detect droplets containing two cells from different individuals (doublets). It implements a statistical model for evaluating the likelihood of observing RNA-seq reads overlapping a set of single nucleotide polymorphisms (SNPs) from each cell- containing droplet.
Deduces large-scale copy number alterations for each cell by averaging relative expression levels over large genomic regions. inferCNV can delete individual gene-specific expression patterns and emphasize the signal of large-scale copy number variations (CNVs). It runs unsupervised transcriptional investigation in parallel to discover outlier cells with increased expression of mature oligodendrocyte genes.
Identifies subclone-specific focal copy number variation (CNV) and loss of heterozygosity (LOH) events in individual cells, using allele and expression information from Single-cell RNA-sequencing (scRNA-seq) data. HoneyBADGER can find deletion, amplifications, and copy neutral LOH events. The software is also able to detect subclonal focal alterations as small as 10 megabases. It can assist in unraveling the impact of genetic and transcriptional heterogeneity and their interplay in cancer progression.
Provides an approach for aneuploidies calling in single-cell RNA-sequencing. scploid is an R package performing for each cell, the identification of chromosomes including genes with potentially detected deviant expression, by applying a statistical method. It aims to supplies a straightforward and easy to interpret method for stem cell and embryonic research as well as assists users in determining genes possibly associated with copy number aberrations.
Detect copy number variations (CNVs) from over-dispersed sequencing data such as single-cell sequencing (SCS) data. The nbCNV method uses two constraints-sparsity and smoothness to fit the CNV patterns under the assumption that the read signals are negatively binomially distributed. The problem of CNV detection was formulated as a quadratic optimization problem, and was solved by an efficient numerical solution based on the classical alternating direction minimization method. Extensive experiments to compare nbCNV with existing benchmark models were conducted. The results of those experiments demonstrate that nbCNV achieves superior performance and high robustness for the detection of CNVs in SCS data.
Provides a single-cell-specific variant caller that combines single-cell genotyping with reconstruction of the cell lineage tree. SCIPhI leverages the fact that somatic cells of an organism are related via a phylogenetic tree where mutations are propagated along tree branches. It can also identify single-nucleotide variants (SNVs) in single cells with low or even no variant allele support.
A linear modeling framework that correlates genotype and phenotype information in scRNA-seq data. SSrGE uses an accumulative ranking approach to select expressed nucleotide variations linked to the expression of a particular gene. SSrGE infers a sparse linear model for each gene and keeps the non-null inferred coefficients. SSrGE can be used as a dimension reduction/feature selection procedure or as a feature ranking. In all the cancer datasets tested, effective and expressed nucleotide variations (eeSNVs) achieve better accuracies and visualization than gene expression for identifying subpopulations
Allows users to investigate single-cell RNA-seq data for detecting copy number alterations and loss of heterozygosity events. badger is a R package which provides a set of statistical methods based on a hierarchical Bayesian approach.
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