Showing 6 of total 6 results (show query)
bioc
Nebulosa:Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation
This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa.
Maintained by Jose Alquicira-Hernandez. Last updated 5 months ago.
softwaregeneexpressionsinglecellvisualizationdimensionreductionsingle-cellsingle-cell-analysissingle-cell-multiomicssingle-cell-rna-seq
99 stars 9.52 score 494 scripts
immunomind
immunarch:Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires
A comprehensive framework for bioinformatics exploratory analysis of bulk and single-cell T-cell receptor and antibody repertoires. It provides seamless data loading, analysis and visualisation for AIRR (Adaptive Immune Receptor Repertoire) data, both bulk immunosequencing (RepSeq) and single-cell sequencing (scRNAseq). Immunarch implements most of the widely used AIRR analysis methods, such as: clonality analysis, estimation of repertoire similarities in distribution of clonotypes and gene segments, repertoire diversity analysis, annotation of clonotypes using external immune receptor databases and clonotype tracking in vaccination and cancer studies. A successor to our previously published 'tcR' immunoinformatics package (Nazarov 2015) <doi:10.1186/s12859-015-0613-1>.
Maintained by Vadim I. Nazarov. Last updated 1 years ago.
airr-analysisb-cell-receptorbcrbcr-repertoirebioinformaticsigig-repertoireimmune-repertoireimmune-repertoire-analysisimmune-repertoire-dataimmunoglobulinimmunoinformaticsimmunologyrep-seqrepertoire-analysissingle-cellsingle-cell-analysist-cell-receptortcrtcr-repertoirecpp
316 stars 9.49 score 203 scripts
dynverse
dyngen:A Multi-Modal Simulator for Spearheading Single-Cell Omics Analyses
A novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. 'dyngen' is more flexible than current single-cell simulation engines. It allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods. Cannoodt et al. (2021) <doi:10.1038/s41467-021-24152-2>.
Maintained by Robrecht Cannoodt. Last updated 2 years ago.
benchmarkingsingle-cellsingle-cell-analysissingle-cell-omics
74 stars 7.53 score 57 scripts
kharchenkolab
numbat:Haplotype-Aware CNV Analysis from scRNA-Seq
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>.
Maintained by Teng Gao. Last updated 10 days ago.
cancer-genomicscnv-detectionlineage-tracingphylogenysingle-cellsingle-cell-analysissingle-cell-rna-seqspatial-transcriptomicscpp
180 stars 7.48 score 120 scriptsbioc
raer:RNA editing tools in R
Toolkit for identification and statistical testing of RNA editing signals from within R. Provides support for identifying sites from bulk-RNA and single cell RNA-seq datasets, and general methods for extraction of allelic read counts from alignment files. Facilitates annotation and exploratory analysis of editing signals using Bioconductor packages and resources.
Maintained by Kent Riemondy. Last updated 5 months ago.
multiplecomparisonrnaseqsinglecellsequencingcoverageepitranscriptomicsfeatureextractionannotationalignmentbioconductor-packagerna-seq-analysissingle-cell-analysissingle-cell-rna-seqcurlbzip2xz-utilszlib
8 stars 5.98 score 6 scripts
ocbe-uio
DIscBIO:A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.
Maintained by Waldir Leoncio. Last updated 1 years ago.
biomarker-discoveryjupyter-notebookscrna-seqsingle-cell-analysistranscriptomicsopenjdk
12 stars 4.38 score 5 scripts