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Analysis methods for analysing single cell RNA-seq data; particularly with the goal of checking if tentative clusters of cells are significantly different to one another in terms of their gene expr…
In-depth analysis of normalization and transformation methods for building coexpression networks from RNA-seq data.
Genotype-free demultiplexing of pooled single-cell RNA-Seq, using a hidden state model for identifying genetically distinct samples within a mixed population.
epiTCR: a highly sensitive predictor for TCR–peptide binding
Methods for clustering and analyzing high-throughput single cell immune cell repertoires (RepSeq)
Tutorial for STAR-Fusion, FusionInspector, and de novo reconstruction of fusion transcripts using Trinity
SCalable Inference of Regulatory Activity in Single Cell RNA-Seq data
R package for Protein activity analysis of single-cell RNAseq.
A tool for detecting somatic variants in single cell data
GREAT Analysis - Functional Enrichment on Genomic Regions
TCRconv is a deep learning model for predicting recognition between T cell receptors and epitopes. It uses protBERT embeddings for the TCRs and convolutional neural networks for the prediction.
ERGO-II, an updated version of ERGO including more features for TCR-peptide binding prediction
⚙️ Matching T-cell repertoire against a database of TCR antigen specificities
Code for "T Cell Receptor Specificity Prediction with Bimodal Attention Networks" (https://doi.org/10.1093/bioinformatics/btab294, ISMB 2021)
Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data
T/B cell receptor sequencing analysis notes
louzounlab / ERGO
Forked from IdoSpringer/ERGOERGO is a deep learing based model for predicting TCR-peptide binding.
R toolkit for inference, visualization and analysis of cell-cell communication from single-cell data
Code repository for the Tumor Immune Cell Atlas (TICA) project
Interfaces for HDF5-based Single Cell File Formats