LIGER

scvi-tools — probabilistic deep learning framework for single-cell omics analysis built on PyTorch and AnnData. Provides variational autoencoders for dimensionality reduction and batch integration (sc

3 shared topics

SEACells — single-cell metacell identification using kernel archetypal analysis. Aggregates highly similar single cells into metacells that preserve transcriptional and epigenomic heterogeneity while

3 shared topics

stLearn — spatial transcriptomics analysis in Python integrating gene expression with tissue morphology. Provides SME (spatial morphological gene expression) normalization, spatial clustering, spatial

2 shared topics • 1 shared operation

Seurat — comprehensive R toolkit for single-cell genomics enabling QC, normalization (LogNormalize, SCTransform), feature selection, dimensionality reduction (PCA, UMAP, t-SNE), graph-based clustering

2 shared topics

STARmap (Spatially-resolved Transcript Amplicon Readout mapping) — in situ spatial transcriptomics method that combines hydrogel-tissue chemistry with sequencing-by-hybridization (SEDAL) for multiplex

2 shared topics