Deep Sense Files

DEEP SENSE 1.5
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This is the BETA release of the Deep Sense analysis web application for single-cell RNA-Seq experiments. It is a collection of 3 web-interfaces: Depth Sense, Call Sense and Run Sense (in their respective folders) which are designed for fast and convenient comparison of DE results from RNA-seq studies through 7 popular Bioconductor pipelines: edgeR [1], DESeq [2], DESeq2 [3], NOISeq [4], baySeq [5], EBSeq [6] and SAMseq [7]. For each respective pipelines, Deep Sense also supports up to 4 possible normalization procedures: Reads per Kilobase per Million mapped reads (RPKM) [8], Trimmed Mean of M-values (TMM) [9], Relative Log Expression (RLE) [2] and Upper Quartile [10].

To use this package, you will need the R statistical computing environment (version 3.1.2 or later) and several packages available through Bioconductor and CRAN. Most of its features are built from Shiny, a web application framework for R designed from RStudio.

This package is in the BETA stage of development, meaning features will continue to be added, though the interfaces are to be kept as consistent as possible as newer versions are to be released subsequently.  

Any updates to Deep Sense will be distributed through the Deep Sense website under files at:

http://sourceforge.net/projects/deepsense/

This release supports Windows. Mac OS X may support this build but Deep Sense has not been tested on Mac OS X yet.

*The release of Deep Sense 1.5 was updated as of 16/7/2015.

References:

[1]: MD Robinson, DJ McCarthy, GK Smyth. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010; 26:139-40.

[2]: S Anders and W Huber. Differential expression analysis for sequence count data. Genome Biol 2010;11:R106.

[3]: MI Love, W Huber and S Ander. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014, 15:550.

[4]: S Tarazona, F Garcia-Alcalde, J Dopazo, A Ferrer and A Conesa. Differential expression in RNA-seq: a matter of depth. Genome research, 21(12), pp. 4436. 2011.

[5]: TJ Hardcastle. baySeq: Empirical Bayesian analysis of patterns of differential expression in count data. 2012

[6]: N Leng and C Kendziorski. EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data. 2015
 
[7]: V Tusher, R Tibshirani, and G. Chu. Significance analysis of microarrays applied to transcriptional responses to ionizing radiation. Proc. Natl. Acad. Sci. USA., 98:5116–5121, 2001.

[8]: A Mortazavi, BA Williams, K McCue. Mapping and quantifying 
mammalian transcriptomes by RNA-seq. Nature Methods 2008;5:621-8.

[9]: MD Robinson, A Oshlack. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 2010, 11:R25.

[10]: JH Bullard, E Purdom, KD Hansen, Sandrine Dudoit. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94. doi:10.1186/1471-2105-11-94.