Compared to bulk RNA-seq, scRNA-seq data are affected by higher noise deriving from both technical and biological factors. Technical variability mostly originates from the low amount of available mRNAs that need to be amplified in order to get the quantity suitable for sequencing. The high variability of scRNA-seq data, the presence of dropout events that leads to zero expression measurements, and the multimodality of expression of a number of transcripts, create some challenges for the detection of differentially expressed genes (DEGs), which is one of the main applications of scRNA-seq and the focus of the present work.
Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.
Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.
Investigates data from gene expression experiments. limma contains features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It can perform both differential expression and differential splicing analyses of RNA-seq data. This tool is useful for studying expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures.
Estimates, from RNA-seq experiments, the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. voom opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. The voom methodology is implemented in the voom function of the limma package available from the Bioconductor project repository.
Simplifies quantitative investigation of comparative RNA-seq data. DESeq2 employs shrinkage estimators for dispersion and fold change. It counts the total number of reads that can be uniquely assigned to a gene. It serves for improved gene ranking and visualization, hypothesis tests above and below a threshold, and the regularized logarithm transformation for quality evaluation and clustering of over-dispersed count data. This version of DESeq uses shrinkage estimators for dispersion and fold change to ease quantitative analysis of comparative RNA-seq data.
A package for differential abundance analysis in sparse high-throughput marker gene survey data. metagnomeSeq relies on a normalization technique and a statistical model that accounts for under-sampling: a common feature of large-scale marker gene studies. It provides a way to determine features (Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).