WJPina

WJPina's Stars

• WWXkenmo/ICAnet

  Independent Component Analysis decipher functional modules for better cell clustering and annotation

  Language:R151

• MobleyLab/Lomap

  Alchemical mutation scoring map

  Language:Python3717

• urmi-21/COVID-19-RNA-Seq-datasets

  A repository for sharing information on available COVID-19 RNA-Seq datasets

  Language:Python7822

• ventolab/CellphoneDB

  CellPhoneDB can be used to search for a particular ligand/receptor, or interrogate your own HUMAN single-cell transcriptomics data.

  Language:Jupyter Notebook31751

• zktuong/ktplots

  Some tools for plotting single-cell data

  Language:R16131

• charlesq34/pointnet2

  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

  Language:Python3.1k895

• bayesian-optimization/BayesianOptimization

  A Python implementation of global optimization with gaussian processes.

  Language:Python7.8k1.5k

• Jiam1ng/COVID-19_Lung_Atlas

  Language:R76

• mahmoudibrahim/KidneyMap

  Language:R115

• martinjzhang/scDRS

  Single-cell disease relevance score (scDRS)

  Language:Jupyter Notebook10613

• linnarsson-lab/FISHscale

  Spatial analysis of FISH data

  Language:Jupyter Notebook279

• maaskola/st_viewer

  The ST viewer is a tool to visualize spatially resolved gene expression data on top of cell tissue figures with the correct location.

  Language:C++1

• jianhuupenn/SpaGCN

  SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

  Language:Python18958

• jcao89757/SCINA

  SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples

  Language:R5718

• romain-lopez/DestVI-reproducibility

  Reproducing the experiments of the DestVI paper

  Language:Jupyter Notebook176

• JEFworks-Lab/STdeconvolve

  Reference-free cell-type deconvolution of multi-cellular spatially resolved transcriptomics data

  Language:R10011

• Nanostring-Biostats/SpatialDecon-manuscript-analyses

  Holds the code and data to reproduce the analyses of Danaher & Kim et a;, "Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data"

  Language:R122

• klarman-cell-observatory/covid19-autopsy

  Language:Jupyter Notebook113

• yanailab/knn-smoothing

  K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

  Language:Python5011

• flo-compbio/singlecell

  SingleCell: A Python/Cython Package for Processing Single-Cell RNA-Seq Data.

  Language:Python115

• yanailab/celseq2

  Generate the UMI count matrix from CEL-Seq2 sequencing data

  Language:Python1913

• cozygene/glint

  Language:Python228

• cozygene/TCA

  Language:R1711

• WWXkenmo/ENIGMA

  A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)

  Language:HTML266

• single-cell-genetics/limix_qtl

  Language:Python1813

• chongjin/CARseq

  Language:R61

• mkrdonovan/gtex_deconvolution

  Language:Jupyter Notebook176

• Danko-Lab/TED

  a fully Bayesian approach to deconvolve tumor microenvironment

  Language:R6010

• tgac-vumc/BLADE

  BLADE: Bayesian Log-normAl DEconvolution for enhanced in silico microdissection of bulk gene expression data

  Language:HTML244

• randel/MIND

  Using Bulk Gene Expression to Estimate Cell-Type-Specific Gene Expression via Deconvolution

  Language:HTML439