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https://github.com/AlexandrovLab/SigProfilerAssignment
Software tool for assignment of known mutational signatures to individual samples and individual somatic mutations.
Proper citation: SigProfilerAssignment (RRID:SCR_026899) Copy
https://github.com/rbundschuh/CLEAR
Software workflow that identifies reliably quantifiable transcripts in limiting-cell RNA-seq (lcRNA-seq) data for differentially expressed genes (DEG) analysis. Coverage-based Limiting-cell Experiment Analysis for RNA-seq.
Proper citation: CLEAR (RRID:SCR_027171) Copy
https://bioconductor.org/packages/RAIDS/
Software R package to enable genetic ancestry inference from various cancer sequence sources (RNA, Exome, and Whole-Genome sequences). This package also implements simulation algorithm that generates synthetic cancer-derived data. Used for accurate and robust inference of genetic ancestry from cancer-derived molecular data across genomic platforms
Proper citation: RAIDS (Robust Ancestry Inference using Data Synthesis) (RRID:SCR_027265) Copy
https://seahorse.networkmedicine.org
Web-based database and search tool for exploratory data analysis in which we have pre-computed statistical associations between available data elements. Large-scale, open-access data sets such as the Genotype Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) include multi-omic data on large numbers of samples along with extensive clinical and phenotypic information. Allows users to explore significant associations using tabulated summary statistics, data visualizations, and functional enrichment analyses (using RNA-seq data) for identified sets of genes.
Proper citation: SEAHORSE (RRID:SCR_027399) Copy
https://github.com/Danko-Lab/BayesPrism
Software R package for fully Bayesian inference of tumor microenvironment composition and gene expression deconvolution. Used to analyze bulk RNA-seq data and estimate cell type-specific expression profiles.
Proper citation: BayesPrism (RRID:SCR_027499) Copy
https://cytospace.stanford.edu/
Software tool for assigning single cells from scRNA-seq to spatial transcriptomics coordinates via optimization framework. Supports high-resolution cell/spot alignment, capacity-constrained/domain-aware placement, and outputs per-cell/per-spot assignments and probabilities for downstream visualization and analysis. Used for optimal mapping of scRNA-seq data to spatial transcriptomics data.
Proper citation: CytoSPACE (RRID:SCR_027634) Copy
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