OUTRIDER - OUTRIDER - OUTlier in RNA-Seq fInDER
Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.
Last updated 5 months ago
immunooncologyrnaseqtranscriptomicsalignmentsequencinggeneexpressiongeneticscount-datadiagnosticsexpression-analysismendelian-geneticsoutlier-detectionrna-seq
48 stars 9.04 score 160 dependencies 1 dependentsFRASER - Find RAre Splicing Events in RNA-Seq Data
Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.
Last updated 5 months ago
rnaseqalternativesplicingsequencingsoftwaregeneticscoverageaberrant-splicingdiagnosticsoutlier-detectionrare-diseaserna-seqsplicing
36 stars 8.56 score 175 dependencies