Tools

Single-cell RNA-Seq Data is Unique, use Appropriate Tools

Single-cell RNA-Seq data is different than population based RNA-Seq data. This data has more sparsity, contains differently behaved distributions, and has resolution bringing to focus new signal (like cell-cycle, potentially as a batch effect...yeah cool :-) ). With great power comes great responsibility.

Here are tools specifically highlighted or mentioned in the workshop. Other tools can be found in the awesome list..

2014

Monocle(2) (Website)
Differential expression and time-series analysis.
Trapnell C., Cacchiarelli D., Grimsby J., Pokharel P., Li S., Morse M., Lennon NJ., Livak KJ., Mikkelsen TS. and Rinn JL. (2014). “The dynamics and regulators of cell fate decisions are revealed by pseudo-temporal ordering of single cells.” Nature Biotechnology.

SCDE (Website)
Statistical analysis for single-cell RNA-Seq data including differential expression.
Kharchenko PV., Silberstein L., Scadden DT. (2014) Bayesian approach to single-cell differential expression analysis. Nature Methods, 11, 740–742. doi:10.1038/nmeth.2967

SCONE - (Single-Cell Overview of Normalized Expression) (Website)
Data drive framework for evaluating and performing normalization.
Risso, D., Ngai, J., Speed, T.P., and Dudoit, S. (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotech., 32, 896–902.

2015

RaceID (Website)
Method to identify rare populations of cells.
Grün D., Lyubimova A., Kester L., Wiebrands K., Basak O., Sasaki N., Clevers H. and van Oudenaarden A. (2015) Single-cell mRNA sequencing reveals rare intestinal cell types. Nature DOI:10.1038/nature14966

Seurat (Website)
R package for exploration, QC, statistical analysis, and spatial analysis. A great place to start!
Satija R., Farrell JA., Gennert D., Schier AF., Regev A. (2015) Spatial reconstruction of single-cell gene expression data. Nature Biotechnology 33, 495–502 doi:10.1038/nbt.3192

2016

SCATER (Website)
Quality control, preprocessing, and visualization of single-cell RNA-Seq data.
McCarthy DJ, Campbell KR, Lun ATL, Wills QF. (2016) scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R bioRXiv Epub ahead of print.

Pagoda (Website)
Pathway and gene set overdispersion analysis.
Fan J., Salathia N., Liu R., Kaeser G., Yung Y., Herman J., Kaper F., Fan JB., Zhang K., Chun J., and Kharchenko PV. (2016) Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature Methods, 13, 241–244 doi:10.1038/nmeth.3734

Estimating Study Sizes A interactive calculator app created by the Satija lab.