gene-expression

There are 392 repositories under gene-expression topic.

  • seandavi/awesome-single-cell

    Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

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  • COMBINE-lab/salmon

    🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment

    Language:C++74039800160

  • saeyslab/nichenetr

    NicheNet: predict active ligand-target links between interacting cells

    Language:R44215264113

  • lucidrains/enformer-pytorch

    Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

    Language:Python399173569

  • broadinstitute/Tangram

    Spatial alignment of single cell transcriptomic data.

    Language:Jupyter Notebook226128845

  • iSEE/iSEE

    R/shiny interface for interactive visualization of data in SummarizedExperiment objects

    Language:R2211719841

  • junjunlab/ClusterGVis

    One-step to Cluster and Visualize Gene Expression Matrix

    Language:R18546115

  • greenelab/tybalt

    Training and evaluating a variational autoencoder for pan-cancer gene expression data

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  • uci-cbcl/D-GEX

    Deep learning for gene expression inference

    Language:Python14622961

  • fbrundu/pymrmr

    Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

    Language:C++13823237

  • federicomarini/awesome-expression-browser

    😎 A curated list of software and resources for exploring and visualizing (browsing) expression data 😎

    12417237

  • saezlab/dorothea

    R package to access DoRothEA's regulons

    Language:R121154825

  • greenelab/pancancer

    Building classifiers using cancer transcriptomes across 33 different cancer-types

    Language:Jupyter Notebook119102058

  • bvieth/powsimR

    Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

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  • catavallejos/BASiCS

    BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version

    Language:R82717016

  • nunofonseca/irap

    integrated RNA-seq Analysis Pipeline

    Language:R82156633

  • lucidrains/tf-bind-transformer

    A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

    Language:Python78809

  • federicomarini/GeneTonic

    Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail

    Language:R756258

  • GfellerLab/EPIC

    Repository for the R package EPIC, to Estimate the Proportion of Immune and Cancer cells from bulk gene expression data.

    Language:R7051421

  • aertslab/SCope

    Fast visualization tool for large-scale and high dimensional single-cell data

    Language:Python68934514

  • greenelab/adage

    Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016

    Language:Python6315030

  • greenelab/BioBombe

    BioBombe: Sequentially compressed gene expression features enhances biological signatures

    Language:Jupyter Notebook6365423

  • AAlhendi1707/countToFPKM

    Convert Counts to Fragments per Kilobase of Transcript per Million (FPKM)

    Language:R6031215

  • COMBINE-lab/terminus

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  • JEFworks-Lab/MERINGUE

    characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities

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  • uc-bd2k/GREIN

    GREIN : GEO RNA-seq Experiments Interactive Navigator

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  • andymckenzie/DGCA

    Differential Gene Correlation Analysis

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  • pinellolab/haystack_bio

    Haystack: Epigenetic Variability and Transcription Factor Motifs Analysis Pipeline

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  • SUwonglab/PECA

    PECA is a software for inferring context specific gene regulatory network from paired gene expression and chromatin accessibility data

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  • jasdumas/shinyGEO

    Gene Expression Omnibus Analysis with Shiny :microscope:

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  • ezgisubasi/breast-cancer-gene-expression

    This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.

    Language:Jupyter Notebook38404

  • drostlab/myTAI

    Evolutionary Transcriptomics with R

    Language:R3661616

  • greenelab/RNAseq_titration_results

    Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously

    Language:HTML3463717

  • randel/MixRF

    A random-forest-based approach for imputing clustered incomplete data

    Language:R346814

  • gaolabtools/scNanoGPS

    Single cell Nanopore sequencing data for Genotype and Phenotype

    Language:Python334341

  • SBRG/cobrame

    A COBRApy extension for genome-scale models of metabolism and expression (ME-models)

    Language:Python33123117