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ccRemover - Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data

Implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.

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3.81 score 4 stars 32 scripts 320 downloads

path2path - Directional Pathway-by-Pathway Interaction Maps from Genome-Scale Perturb-Seq

Builds a directional pathway-by-pathway interaction map from genome-scale Perturb-seq differential-expression statistics by two stacked runs of preranked gene set enrichment analysis: once per perturbation over genes, then once per readout pathway over perturbations. Entry (j,k) of the resulting matrix measures whether perturbations targeting pathway j's members produce a coordinated transcriptional response in pathway k. The package includes target exclusion, effective-size filters, overlap masking, Stage 2 p-values with false-discovery-rate control, and a whole-pipeline permutation calibration with generalized-Pareto tail estimation.

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softwaregenesetenrichmentpathwayscrisprsinglecelldifferentialexpressiontranscriptomicsnetworkinferencegeneexpressiongenetargetkeggwholegenome

3.00 score

ClussCluster - Simultaneous Detection of Clusters and Cluster-Specific Genes in High-Throughput Transcriptome Data

Implements a new method 'ClussCluster' descried in Ge Jiang and Jun Li, "Simultaneous Detection of Clusters and Cluster-Specific Genes in High-throughput Transcriptome Data" (Unpublished). Simultaneously perform clustering analysis and signature gene selection on high-dimensional transcriptome data sets. To do so, 'ClussCluster' incorporates a Lasso-type regularization penalty term to the objective function of K- means so that cell-type-specific signature genes can be identified while clustering the cells.

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2.70 score 5 scripts 353 downloads

SparseMDC - Implementation of SparseMDC Algorithm

Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

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2.00 score 7 scripts 251 downloads

SparseDC - Implementation of SparseDC Algorithm

Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

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2.00 score 10 scripts 283 downloads

DiPhiSeq - Robust Tests for Differential Dispersion and Differential Expression in RNA-Sequencing Data

Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function.

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1.00 score 8 scripts 316 downloads