Covarying neighborhood analysis (CNA) is a method for finding structure in- and conducting association analysis with multi-sample single-cell datasets.

An interface for Nested Stochastic Block Model for single cell analysis

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

Interactive software tool for the assignment of cell types in single-cell studies.

Decombinator v5: fast, error-correcting analysis of TCR repertoires

MetaLogo: a heterogeneity-aware sequence logo generator and aligner

QuPath documentation

dalmatian is a collection of high-level companion functions for Firecloud and FISS.

Python library for immune repertoire analysis

Highly informative TCR representation model

The code for running the analysis component of CSI-Microbe

Quantification module for mcmicro

Recover transcript counts from log-normalized UMI counts data.

Mixed-effect model to test differences in cell type proportions from single-cell data, in Python

scPCA is a versatile matrix factorisation framework designed to analyze single-cell data across multiple conditions.

IDEIS is a software for CD45 isoform detection in single-cell data.

weight of evidence of HLA allele expression based on bulk TCR beta-chain repertoires

CELL ABUNDANCE AWARE DEEP LEARNING FOR CELL DETECTION ON HIGHLY IMBALANCED PATHOLOGICAL DATA

Modified Version of Decombinator to Allow Single Tag Search for Short Read Data