{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["42(8)"],"submitter":["Ilmjarv S"],"pubmed_abstract":["Regardless of the advent of high-throughput sequencing, microarrays remain central in current biomedical research. Conventional microarray analysis pipelines apply data reduction before the estimation of differential expression, which is likely to render the estimates susceptible to noise from signal summarization and reduce statistical power. We present a probe-level framework, which capitalizes on the high number of concurrent measurements to provide more robust differential expression estimates. The framework naturally extends to various experimental designs and target categories (e.g. transcripts, genes, genomic regions) as well as small sample sizes. Benchmarking in relation to popular microarray and RNA-sequencing data-analysis pipelines indicated high and stable performance on the M"],"journal":["Nucleic acids research"],"pagination":["e72"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4005682"],"repository":["biostudies-literature"],"pubmed_title":["Estimating differential expression from multiple indicators."],"pmcid":["PMC4005682"],"pubmed_authors":["Luuk H","Niitsoo M","Kolde R","Reimets R","Vilo J","Hundahl CA","Vasar E","Ilmjarv S"],"additional_accession":[]},"is_claimable":false,"name":"Estimating differential expression from multiple indicators.","description":"Regardless of the advent of high-throughput sequencing, microarrays remain central in current biomedical research. Conventional microarray analysis pipelines apply data reduction before the estimation of differential expression, which is likely to render the estimates susceptible to noise from signal summarization and reduce statistical power. We present a probe-level framework, which capitalizes on the high number of concurrent measurements to provide more robust differential expression estimates. The framework naturally extends to various experimental designs and target categories (e.g. transcripts, genes, genomic regions) as well as small sample sizes. Benchmarking in relation to popular microarray and RNA-sequencing data-analysis pipelines indicated high and stable performance on the M","dates":{"release":"2014-01-01T00:00:00Z","publication":"2014 Apr","modification":"2026-05-03T02:45:40.152Z","creation":"2019-03-27T01:27:22Z"},"accession":"S-EPMC4005682","cross_references":{"pubmed":["24586062"],"doi":["10.1093/nar/gku158"]}}