Co-expression trans-eQTL analysis data

<p><strong>Sample metadata:</strong></p> <ul> <li>sample_metadata.tsv - tab separated file with metadata for samples used in this analysis.</li> </ul> <p><strong>Gene expression matrix:</strong></p> <ul> <li>Merged_ENSG_expression.tsv - integrated gene expression matrix used in the co-expression analysis (Ensembl IDs in rows and sample IDs in columns). The matrix can be split into sub matrices using the metadata from sample_metadata.tsv for separate analysis of cell types.</li> </ul> <p><strong>Genes in the modules:</strong></p> <ul> <li>ICA_modules.tsv - data for assigning genes to modules from ICA method. Column &#39;loading&#39; shows the contribution value of the gene to the module. Column &#39;approach&#39; indicates if the module was detected from integrated or separate data. Column &#39;qtl_group&#39; is an identifier for the cell type/condition.</li> <li>PEER_modules.tsv -&nbsp;data for assigning genes to modules from PEER&nbsp;method. Column &#39;loading&#39; shows the contribution value of the gene to the module.&nbsp;</li> <li>PLIER_modules.tsv -&nbsp;data for assigning genes to modules from PLIER method. Column &#39;loading&#39; shows the contribution value of the gene to the module.</li> <li>WGCNA_modules.tsv -&nbsp;data for assigning genes to modules from WGCNA method. Column &#39;loading&#39; shows the contribution value of the gene to the module.&nbsp;Column &#39;loading&#39; has value 1 as the method assigns genes to modules without defining their contributions.&nbsp;</li> <li>funcExplorer_modules.tsv -&nbsp;data for assigning genes to modules from funcExplorer&nbsp;method. Column &#39;loading&#39; has value 1 as the method assigns genes to modules without defining their contributions.&nbsp;</li> </ul> <p><strong>Module eigenvectors used for the trans-eQTL analysis</strong></p> <ul> <li>eigenvectors_integrated.tsv - module eigenvectors for all the co-expression analysis methods applied to integrated data</li> <li>eigenvectors_separate.tsv-&nbsp;module eigenvectors for all the co-expression analysis methods applied to cell types separately</li> </ul> <p><strong>SuSiE fine mapping credible sets&nbsp;</strong></p> <ul> <li>SuSiE_credible_sets_integrated.tsv</li> <li>SuSiE_credible_sets_separate.tsv</li> </ul>