Digital Cell Sorter (DCS): single cell RNA-seq analysis toolkit. Documentation:

Detecting and dissecting anomalous anatomic regions in spatial transcriptomics with STANDS

A similarity-assisted variational autoencoder (saVAE) is a new method that adopts similarity information in the framework of the VAE.

Batch Effect Correction framework for metagenomic data

Batman: Batch effect correction via minimum weight MAtchiNg

A Python tool for performing downstream analysis on Single-Cell RNA-seq datasets

Detecting and subtyping anomalous single cells with M2ASDA

Adversarial autoencoder for bulk RNA‑seq batch effect correction that removes batch signal while preserving biological variation, outputting a corrected logCPM matrix and latent embeddings with optional supervised label preservation.

Regularized scVI for complex multiome single-cell/nucleus datasets: ambient RNA correction, dispersion prior, and batch-free decoder act as structural inductive biases that make large-architecture VAEs well-behaved without per-dataset tuning.

Before/after PCA visualisation showing how batch correction removes technical variation while keeping biology.

Program pipelines described in the bioRxiv preprint "Finding stable clusterings of single-cell RNA-seq data" (doi:10.1101/2025.09.17.672302)