Single Cell RNAseq data analysis tutorial !!

Robert M Willioms — Mon, 27 Nov 2017 16:24:29 -0600

A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
Measures the average expression level for each gene across a large population of input cells
Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
Useful for quantifying expression signatures from ensembles, e.g. in disease studies
Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
Does not provide insights into the stochastic nature of gene expression

Following are the useful links:

SCell: single-cell RNA-seq analysis software

Beta-Poisson model for single-cell RNA-seq data analyses

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

SC3 – consensus clustering of single-cell RNA-Seq data

Citrus: A toolkit for single cell sequencing analysis

Single-Cell Resolution of Temporal Gene Expression during Heart Development

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

scLVM is a modelling framework for single-cell RNA-seq data

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

Visualization and cellular hierarchy inference of single-cell data using SPADE

OEFinder: Identify ordering effect genes in single cell RNA-seq data

BOL: Single Cell RNAseq data analysis tutorial !!