<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Mucaki EJ</submitter><funding>Natural Sciences and Engineering Research Council of Canada</funding><funding>Canadian Institutes of Health Research (CA)</funding><funding>Canada Foundation for Innovation</funding><funding>Canadian Institutes of Health Research</funding><pagination>1023</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12486589</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>23(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Formalin-fixed paraffin embedded (FFPE) samples suffer from the degradation of nucleic acids, a problem that becomes particularly acute with samples stored for extended periods. It remains challenging to profile FFPE using high-throughput sequencing technologies including RNA-sequencing, and the resulting FFPE RNA-seq (fRNA-seq) data has a high rate of transcript dropout, a property shared with single cell RNA-seq. Transcript counts also have high variance and are prone to extreme values, together making downstream analyses extremely challenging.&lt;h4>Methods&lt;/h4>We introduce the PaRaffin Embedded Formalin-FixEd Cleaning Tool (PREFFECT), a probabilistic framework for the analysis of fRNA-seq data. PREFFECT uses generative models to fit distributions to observed expression </pubmed_abstract><journal>Journal of translational medicine</journal><pubmed_title>Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material.</pubmed_title><pmcid>PMC12486589</pmcid><funding_grant_id>RGPIN-2018-050</funding_grant_id><funding_grant_id>391682</funding_grant_id><funding_grant_id>43481</funding_grant_id><pubmed_authors>Mucaki EJ</pubmed_authors><pubmed_authors>Zhang W</pubmed_authors><pubmed_authors>Saha A</pubmed_authors><pubmed_authors>Dumeaux V</pubmed_authors><pubmed_authors>Trebinjac S</pubmed_authors><pubmed_authors>Rakovitch E</pubmed_authors><pubmed_authors>Hallett MT</pubmed_authors><pubmed_authors>Nofech-Mozes S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Generative and integrative modeling for transcriptomics with formalin fixed paraffin embedded material.</name><description>&lt;h4>Background&lt;/h4>Formalin-fixed paraffin embedded (FFPE) samples suffer from the degradation of nucleic acids, a problem that becomes particularly acute with samples stored for extended periods. It remains challenging to profile FFPE using high-throughput sequencing technologies including RNA-sequencing, and the resulting FFPE RNA-seq (fRNA-seq) data has a high rate of transcript dropout, a property shared with single cell RNA-seq. Transcript counts also have high variance and are prone to extreme values, together making downstream analyses extremely challenging.&lt;h4>Methods&lt;/h4>We introduce the PaRaffin Embedded Formalin-FixEd Cleaning Tool (PREFFECT), a probabilistic framework for the analysis of fRNA-seq data. PREFFECT uses generative models to fit distributions to observed expression </description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Sep</publication><modification>2026-06-04T01:27:03.089Z</modification><creation>2026-05-03T03:14:12.241Z</creation></dates><accession>S-EPMC12486589</accession><cross_references><pubmed>41029822</pubmed><doi>10.1186/s12967-025-07031-y</doi></cross_references></HashMap>