mugpeng

mugpeng's Stars

• Teichlab/celltypist

  A tool for semi-automatic cell type classification

  Language:Python323812946

• jianhuupenn/SpaGCN

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

  Language:Python19928060

• ga4gh/benchmarking-tools

  Repository for the GA4GH Benchmarking Team work developing standardized benchmarking methods for germline small variant calls

  Language:HTML194531647

• icbi-lab/infercnvpy

  Infer copy number variation (CNV) from scRNA-seq data. Plays nicely with Scanpy.

  Language:Python14045927

• BGIResearch/stereopy

  A toolkit of spatial transcriptomic analysis.

  Language:Jupyter Notebook90113830

• vanvalenlab/deepcell-label

  Cloud-based data annotation tools for biological images

  Language:JavaScript77825215

• genome-in-a-bottle/genome-stratifications

  Stratification BED files from the Global Alliance for Genomics and Health (GA4GH) Benchmarking Team and the Genome in a Bottle Consortium. These files can be used as standard resource of BED files for use with GA4GH benchmarking tools such as hap.py to stratify true positive, false positive, and false negative variant calls based on genomic context.

  Language:HTML76626

• NCIP/ctat-mutations

  Mutation detection using GATK4 best practices and latest RNA editing filters resources. Works with both Hg38 and Hg19

  Language:HTML70126418

• cancerit/CaVEMan

  SNV expectation maximisation based mutation calling algorithm aimed at detecting somatic mutations in paired (tumour/normal) cancer samples. Supports both bam and cram format via htslib

  Language:C60196713

• aerickso/SpatialInferCNV

  Clone Calling from Visium Spatial Transcriptomics of Cancer samples

  Language:HTML5951214

• KrishnaswamyLab/Multiscale_PHATE

  Creating multi-resolution embeddings and clusters from high dimensional data

  Language:Jupyter Notebook4991011

• HorvathLab/NGS

  Next-Gen Sequencing tools from the Horvath Lab

  Language:Python3984216

• lanagarmire/SSrGE

  Language:Python334119

• Lifoof/MoGCN

  Multi-omics integration method using AE and GCN

  Language:Python331312

• nghiavtr/SCmut

  Language:R31499

• GWW/scsnv

  scSNV Mapping tool for 10X Single Cell Data

  Language:C++223354

• RicardoRamirez2020/GCN_Cancer

  Language:Python21128

• runpuchen/DeepType

  Language:Python20038

• zhangxiaoyu11/XOmiVAE

  An interpretable deep learning model for cancer classification using high-dimensional omics data

  Language:Jupyter Notebook15207

• fenglin0/benchmarking_variant_callers

  In this study, we perform systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets. We evaluate the performances of these tools in different read depths, genomic contexts, functional regions and variant allele frequencies.

  Language:Shell14107

• ThomasJamesMitchell/deSCeRNAMut

  Method for de novo mutation calling from droplet based single cell RNA seq data

  12131

• petervangalen/MAESTER-2021

  Scripts to reproduce the analysis of the MAESTER paper (https://www.nature.com/articles/s41587-022-01210-8).

  Language:R11355

• BIMIB-DISCo/oral_squamous_longitudinal

  Utilities repository to analyze single-cell data from https://www.nature.com/articles/s41467-018-07261-3.

  Language:R9502

• limin321/stmut

  Visualizing Somatic Alterations of 10X Spatial Transcriptomics Data

  Language:R9172

• raphael-group/scarlet

  SCARLET (Single-cell Algorithm for Reconstructing Loss-supported Evolution of Tumors) is an algorithm that reconstructs tumor phylogenies from single-cell DNA sequencing data. SCARLET uses a loss-supported model that constrains mutation losses based on observed copy-number data.

  Language:Limbo81024

• niklaslang/scRNAseqVariantCalling

  pipeline for variant calling at the single cell level

  Language:Shell6102

• WhirlFirst/somde

  Algorithm for finding gene spatial pattern based on Gaussian process accelerated by SOM

  Language:Jupyter Notebook6245

• melobio/Graphene

  Pre-trained graph neural network and downstream tasks

  Language:Python4202

• argymeg/precision-clonality-code

  Language:R24

• SayakaMiura/MOCA

  Language:R11