R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

single cell and bulk RNASeq analysis scripts

an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references

Data analysis for the paper "Single cell transcriptomics of human epidermis identifies basal stem cell transition states" in Nature Communications.

Taller de la CDSB 2021 sobre análisis de datos de scRNA-seq con Bioconductor

scBubbletree: quantitative tool for visual exploration of scRNA-seq data

Code and access to data related to the SIFT-seq publication by Chandran et al., Nature Medicine (2022)

2021-05-03 presentation for the Human Cell Atlas - Latin America workshop on snRNA_seq

Gene expression on C. elegans single cells

Repo linked to our recent publication in Science Advances

Mapping Ambiguity Overdispersion Correction for scRNA-seq

Single cell data analysis tutorials

1Cellbio file to Seurat/SingleCellExperiment/Scanpy

Distance Metrics for scRNA

This repository is dedicated to curating, organizing, and annotating metadata for scRNA-seq datasets of human breast cancer, with a focus on datasets that include myeloid cells. This work is tied to the Mye-InfoBank project, aiming to enhance data integration, analysis, and reproducibility in breast cancer

Tutorial for performing scRNA-seq analysis using cellCounts and Seurat

Instructions, and protocols for ChronoSeq technology

A guide to using the Python scRNA-seq analysis package Scanpy from R