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Single Cell RNAseq data analysis

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


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


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