Neural Data Science - Lecture 11 - Single Cell Transcriptomics

Neural Data Science — Lecture 11 — Single cell transcriptomics

Tübingen Machine Learning

Catalina Vallejos - Statistical challenges in the analysis of single-cell transcriptomics data

Open Data Science Initiative

New Advances in Single-Cell and Spatial Genomics (2019)

LSU / LBRN Transcriptomics Training - Bulk and Single Cell Transcriptomics

Scoring Transcript Variation in Single Cell RNA-seq Data

Simons Institute

Detecting Activated Cell Populations Using Single-Cell RNA-Seq

Single-Cell RNA-Seq and cell heterogeneity in the central nervous system

Serafim Batzoglou, Stanford - Stanford Medicine Big Data | Precision Health 2016

Current Practice, Challenges and Perspectives on Single Cell Data Science

The Foundations of Biomedical Data Science

MPG Primer: scRNA-seq analyses: challenges, opportunities, and best practices part 2 (2020)

Broad Institute

Quantitative single-molecule, single-cell spatial data with subcellular resolution

Algorithms for Single Cell RNAseq Analysis

Simons Institute

Mapping Gene Regulatory Dependencies with Single-Cell Resolution

Simons Institute

MIA: Mehrtash Babadi, Amplification artifacts in scRNA-seq; Stephen Fleming, Background removal

Broad Institute

Analysis Methods for Single Cell RNA-seq with Application to T-cell Function

Simons Institute

Single Cell RNA Sequencing and Multiple Myeloma | Nicholas Banovich

Evolutionary Medicine

Workshop "Single Cell Genomics meets Data Science" - Session 3 "Regulation"

Single Cell Omics Germany

Introduction to single-cell sequencing, day 2. Maryna Korshevniuk.

Геноміка ЮА | Genomics UA

Machine learning for single cell analysis

Machine Learning Department at CMU