jgilis

jgilis's Stars

• mikelove/awesome-multi-omics

  List of software packages for multi-omics analysis

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• dmcable/spacexr

  Spatial-eXpression-R: Cell type identification (including cell type mixtures) and cell type-specific differential expression for spatial transcriptomics

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• statOmics/tradeSeq

  TRAjectory-based Differential Expression analysis for SEQuencing data

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• iSEE/iSEE

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

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• HelenaLC/muscat

  Multi-sample multi-group scRNA-seq analysis tools

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• Bioconductor/BiocStickers

  Stickers for some Bioconductor packages - feel free to contribute and/or modify.

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• hrbrmstr/RSwitch

  🎛 A small menubar app that allows you to switch between R versions quickly (if you have multiple versions of R framework installed).

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• willtownes/glmpca

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• alexisvdb/singleCellHaystack

  Finding surprising needles (=genes) in haystacks (=single cell transcriptome data).

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• waldronlab/data-science-seminar

  Topics In Data Science and Bioinformatics

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• drisso/SingleCellExperiment

  Clone of the Bioconductor repository for the SingleCellExperiment package, see https://bioconductor.org/packages/devel/bioc/html/SingleCellExperiment.html for the official development version.

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• csoneson/countsimQC

  countsimQC - Compare characteristic features of count data sets

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• SimoneTiberi/BANDITS

  BANDITS: Bayesian ANalysis of DIfferenTial Splicing

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• SimoneTiberi/distinct

  distinct: a method for differential analyses via hierarchical permutation tests

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• kevinrue/BiocChallenges

  Challenges for the Biocoductor community.

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• kevinrue/SCEGallery

  Pretty plotting for SCE objects.

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• Bioconductor/BiocCheck

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• statOmics/satuRnPaper

  Github repository associated with the publication of *satuRn: Scalable Analysis of Differential Transcript Usage for Bulk and Single-cell RNA-sequencing Applications*

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• statOmics/pitfallsOfHumanPathologyAtlas

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• HelenaLC/BiocSpatialChallenges

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• jgilis/pitfallsOfHumanPathologyAtlas

  This repository accompanies our publication in which we revisit the statistical analyses of the human pathology atlas. In this re-analysis, we highlight several pitfalls in the original workflow and suggest how these pitfalls can be avoided in the future.

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