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bioc
dar:Differential Abundance Analysis by Consensus
Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.
Maintained by Francesc Catala-Moll. Last updated 14 days ago.
softwaresequencingmicrobiomemetagenomicsmultiplecomparisonnormalizationbioconductorbiomarker-discoverydifferential-abundance-analysisfeature-selectionmicrobiologyphyloseq
2 stars 5.98 score 8 scriptsbioc
biotmle:Targeted Learning with Moderated Statistics for Biomarker Discovery
Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.
Maintained by Nima Hejazi. Last updated 5 months ago.
regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncologybioconductorbioconductor-packagebioconductor-packagesbioinformaticsbiomarker-discoverybiostatisticscausal-inferencecomputational-biologymachine-learningstatisticstargeted-learning
5 stars 5.30 score 5 scripts
ocbe-uio
DIscBIO:A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.
Maintained by Waldir Leoncio. Last updated 1 years ago.
biomarker-discoveryjupyter-notebookscrna-seqsingle-cell-analysistranscriptomicsopenjdk
12 stars 4.38 score 5 scripts