Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.
Pubmed ID: 31138121
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
Curated protein-protein and genetic interaction repository of raw protein and genetic interactions from major model organism species, with data compiled through comprehensive curation efforts.
View all literature mentionsSoftware collection of Bayesian approaches to infer hidden determinants and their effects from gene expression profiles using factor analysis methods. Applications of PEER have * detected batch effects and experimental confounders * increased the number of expression QTL findings by threefold * allowed inference of intermediate cellular traits, such as transcription factor or pathway activations This project offers an efficient and versatile C++ implementation of the underlying algorithms with user-friendly interfaces to R and python.
View all literature mentionsAdjusting batch effects in microarray expression data using Empirical Bayes methods.
View all literature mentions