LeonSong1995

gsMap

MeDuSA

MeDuSA is a fine-resolution cellular deconvolution method that leverages scRNA-seq data as a reference to estimate cell-state abundance in bulk RNA-seq data.

MeDuSAJ

GLUE

Graph-linked unified embedding for single-cell multi-omics data integration

STitch3D

Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices

SpaGCN

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

stereopy

A toolkit of spatial transcriptomic analysis.

MeDuSA_Analysis

SAW

ARTISTA

Axolotl Regenerative Telencehpalon Interpretation via Spatiotemporal Transcriptomic Atlas

AlleleSeq2

QTLEnrich

Assessing enrichment of complex disease or trait associations among QTLs

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.