Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.
|Author||Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut]|
|Date of publication||None|
|Maintainer||Joseph N Paulson <email@example.com>|
annotateFromBiomart: Annotate your Expression Set with biomaRt
bladder: Bladder RNA-seq data from the GTEx consortium
checkMisAnnotation: Check for wrong annotation of a sample using classical MDS...
checkTissuesToMerge: Check tissues to merge based on gene expression profile
downloadGTEx: Download GTEx files and turn them into ExpressionSet object
extractMatrix: Extract the appropriate matrix
filterGenes: Filter specific genes
filterLowGenes: Filter genes that have less than a minimum threshold CPM for...
filterMissingGenes: Filter genes not expressed in any sample
filterSamples: Filter samples
normalizeTissueAware: Normalize in a tissue aware context
plotCMDS: Plot classical MDS of dataset
plotDensity: Density plots of columns in a matrix
plotHeatmap: Plot heatmap of most variable genes
qsmooth: Quantile shrinkage normalization
qstats: Compute quantile statistics
skin: Skin RNA-seq data from the GTEx consortium