Single Cell RNAseq data analysis tutorial !!
- 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:
Single Cell RNAseq data analysis Tutorial
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
SCell: single-cell RNA-seq analysis software
https://github.com/diazlab/SCell
Beta-Poisson model for single-cell RNA-seq data analyses
https://github.com/nghiavtr/BPSC
Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis
https://research.cchmc.org/pbge/sincera.html
SC3 – consensus clustering of single-cell RNA-Seq data
http://biorxiv.org/content/early/2016/09/02/036558
Citrus: A toolkit for single cell sequencing analysis
http://biorxiv.org/content/early/2016/09/14/045070
Single-Cell Resolution of Temporal Gene Expression during Heart Development
http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7
Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects
http://biorxiv.org/content/early/2016/11/15/087775
Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes
http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract
SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation
http://biorxiv.org/content/early/2016/11/21/088856
SCOUP is a probabilistic model to analyze single-cell expression data during differentiation
https://github.com/hmatsu1226/SCOUP
scLVM is a modelling framework for single-cell RNA-seq data
https://github.com/PMBio/scLVM
Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories
https://github.com/jw156605/SLICER
SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality
http://www.morgridge.net/SinQC.html
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
https://github.com/zji90/TSCAN
Visualization and cellular hierarchy inference of single-cell data using SPADE
http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html
OEFinder: Identify ordering effect genes in single cell RNA-seq data
