Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny.

[1]  R. Irizarry,et al.  Missing data and technical variability in single‐cell RNA‐sequencing experiments , 2018, Biostatistics.

[2]  Aaron T. L. Lun,et al.  beachmat: a Bioconductor C++ API for accessing single-cell genomics data from a variety of R matrix types , 2017, bioRxiv.

[3]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[6]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[7]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.

[8]  Yan Guo,et al.  RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment , 2014, Cancer informatics.

[9]  Justin Guinney,et al.  GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.

[10]  Jeremy M. Chacón,et al.  Engineering species-like barriers to sexual reproduction , 2016, Nature Communications.

[11]  Aaron T. L. Lun,et al.  Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R , 2017, Bioinform..

[12]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[13]  Joseph L. Herman,et al.  Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis , 2015, Nature Methods.

[14]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[15]  Brent S. Pedersen,et al.  Combating subclonal evolution of resistant cancer phenotypes , 2017, Nature Communications.

[16]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[17]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[18]  David A. Knowles,et al.  Batch effects and the effective design of single-cell gene expression studies , 2016, Scientific Reports.

[19]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression data , 2015 .

[20]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[21]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[22]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[23]  Gabor T. Marth,et al.  Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression , 2013, Bioinform..

[24]  Kazuki Kurimoto,et al.  SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression , 2015, Nucleic acids research.

[25]  S. Teichmann,et al.  Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.