Unlock your biological data

?

Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 7 of 7 results
filter_list Filters
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 7 of 7 results
HoneyBADGER
New
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.
scploid / Single Cell RNA-seq Aneuploidy Caller
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.
nbCNV / nbCopy Number Variants
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.
SSrGE
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
0 - 0 of 0 results
0 - 0 of 0 results