1. Identifying And Characterizing Heterogeneity In Single Cell Rnaseq Data

    Identifying and Characterizing Heterogeneity in Single Cell RNA-seq Data In this tutorial, we will become familiar with a few computational techniques we can use to identify and characterize heterogeneity in single cell RNA-seq data. Pre-prepared data for this tutorial can be found as part of the Single Cell Genomics 2016 Workshop I did at Harvard Medical School. Getting started A single cell dataset from Camp et al. has been pre-prepared for you. The data is...


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  2. Signaling Network Reconstruction Using Bayesian Networks In R

    In in landmark paper “Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data”, Sachs et al. applied Bayesian Networks on multi-parameter flow cytometry data to reconstruct signaling relationships and predict novel interpathway network causalities. Following a tutorial by Marco Scutari, I attempt to reproduce to the best of my abilities the statistical analysis of the paper using Marco’s bnlearn R package. library(bnlearn) library(Rgraphviz) First, read in and process the data. Since this is flow cytometry data,...


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  3. Single Cell Genomics 2016

    Notes from the Single Cell Genomics Conference, September 14-16 2016, Wellcome Genome Campus, Hixton, Cambridge, UK (Website) Keynote Lecture: Genomic insights into human cortical development, lissencephaly, and Zika microcephaly; Arnold Kriegstein; University of California, San Francisco, USA Single cell genomics to look in depth at developmental human brain Clinical applications of identified cell types by genomic signature ie zika Human brain is 1000x bigger than mice mostly in cortex Elephants and proposes have bigger more...


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