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Single Cell RNAseq data analysis: Revision

The ability to derive genome-wide mRNA expression data from a population of cells has proven useful in thousands of studies over the past two decades. In spite of their utility, traditional expression experiments are limited to providing measurements that are averaged over thousands of cells, which can mask or even misrepresent signals of interest. Fortunately, recent technological advances now allow us to obtain transcriptome-wide data from individual cells. This development is not simply one more step toward better expression profiling, but rather a major advance that will enable fundamental insights into biology.

While the data obtained from single-cell RNA-sequencing (scRNA-seq) are often structurally identical to those from a bulk expression experiment (some K million mRNA transcripts are sequenced from n samples or cells), the relative paucity of starting material and increased resolution give rise to distinct features in scRNA-seq data, including an abundance of zeros (both biological and technical), increased variability, and complex expression distributions. These features, in turn, pose both opportunities and challenges for which novel statistical and computational methods are required. 

Followings are the list of useful software and tutorial for Single Cell RNAseq data analysis

Single Cell RNAseq data analysis Tutorial

http://hemberg-lab.github.io/scRNA.seq.course/scRNA-seq-course.pdf

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

https://f1000research.com/articles/5-2122/v2

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

https://www.bioconductor.org/help/workflows/simpleSingleCell/

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

https://github.com/lengning/OEFinder