Single Cell RNAseq data analysis protocol

Question

[Mike]

Hi all,

I am looking well accepted/widely used protocol for analysis Single Cell RNAseq data analysis.

How sc RNAseq analysis protocol are different from bulk RNAseq (in some steps, alignment, QC, normalisation. etc)?

So what kind of measures to be taken during scRNAseq analysis in alignment, QC, Cell-QC, normalization & finding differential expressed genes.

Thanks,

[GenoMax]

This tutorial was recently posted here: Analysis of single-cell RNA-seq data

[Mike]

Thanks, I have gone through all 25 chapters of above tutorial, its good compilation and well explained. I need some more inputs from Biostars experts.

[Sean Davis]

I started a list of single-cell analysis software, tutorials and workshops here:

https://github.com/seandavi/awesome-single-cell

Contributions welcome!

[Mike]

Thanks Sean Davis,

current issue of Genome Biology: Single-Cell Omics ( special issue)

Genome Biology highlights the emergence of this field with a special issue focused on single-cell methods and their applications.

http://www.biomedcentral.com/collections/singlecellomics

[Sean Davis]

Added to the awesome-single-cell list. Thx.

Answer 1

[Mike]

packages related to scRNAseq:

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

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

CellView – Interactive Exploration Of High Dimensional Single Cell RNA-Seq Data

https://mbolisetty.shinyapps.io/CellView/

SCANPY: large-scale single-cell gene expression data analysis

https://github.com/theislab/Scanpy

[Sean Davis]

Great list, Mike. These have been added to https://github.com/seandavi/awesome-single-cell/.

Answer 2

[Mike]

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

http://f1000research.com/articles/5-2122/v1

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

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

Power Analysis of Single Cell RNA‐Sequencing Experiments

http://biorxiv.org/content/early/2016/09/08/073692

Answer 3

[Mike]

Excellent Single Cell RNAseq data analysis Tutorial available in pdf format. (thanks vladimir.yu.kiselev and Geparada for sharing)

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

C: Analysis of single-cell RNA-seq data

Answer 4

[Friederike]

My feeling is that depending on the type of technology that was used for the generation of the single cell data (e.g., plate-sorted vs. droplet-based), the data may come with very differently magnified problems.

A good overview is given here, the image below is a great visualization of the variety of methods that generically pass as "single-cell transcriptomics". Does anyone know of an insightful discussion of the different (scales of) problems (and solutions)?

In addition, I guess it'd be good to know which packages were developed with what kind of data in mind. E.g., Seurat was developed in a lab that pushed Drop-seq technology. I think it'd at least be useful piece of information to have as it may explain why some specific methods/models may work better for some types of data than for others.

Answer 5

[Mike]

scRNA notes and Reviews:

Single-cell mRNA quantification and differential analysis with Census

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4150.html

Comparison of bulk and scRNA-seq analytical strategies

http://www.nature.com/nrg/journal/v16/n3/fig_tab/nrg3833_F1.html

Single-cell RNA-seq to decipher tumour architecture

http://www.nature.com/nrg/journal/vaop/ncurrent/full/nrg.2016.151.html

Single-Cell Transcriptomics Bioinformatics and Computational Challenges

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5030210/

Answer 6

[Mike]

Vedio tutorial: Analysis of single cell RNA-seq data - Lecture 1

Analysing Single-Cell RNA-Seq data with SeqMonk

single nucleus RNA-seq