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| author | rnateam |
|---|---|
| date | Mon, 18 Dec 2023 19:03:27 +0000 |
| parents | 7dd2835ce566 |
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<p><head> <title>RBPBench - Search Report</title></p> <script src="/home/uhlm/Programme/miniconda3/envs/rbpbench/lib/python3.11/site-packages/rbpbench/content/sorttable.js" type="text/javascript"></script> <p></head></p> <h1>Search report</h1> <p>List of available statistics and plots generated by RBPBench (rbpbench search --report):</p> <ul> <li><a href="#rbp-enrich-stats">RBP motif enrichment statistics</a></li> <li><a href="#cooc-heat-map">RBP co-occurrences heat map</a></li> <li><a href="#corr-heat-map">RBP correlations heat map</a> </li> </ul> <h2 id="rbp-enrich-stats">RBP motif enrichment statistics</h2> <p><strong>Table:</strong> RBP motif enrichment statistics. Given a score for each genomic region (# input regions = 1), RBPbench checks whether motifs are enriched in higher-scoring regions (using Wilcoxon rank-sum test). A low Wilcoxon rank-sum test p-value for a given RBP thus indicates that higher-scoring regions are more likely to contain motif hits of the respective RBP. NOTE that if scores associated to input genomic regions are all the same, p-values become meaningless (i.e., they result in p-values of 1.0).</p> <table class="sortable"> <thead> <tr> <th style="text-align: center;">RBP ID</th> <th style="text-align: center;"># hit regions</th> <th style="text-align: center;">% hit regions</th> <th style="text-align: center;"># motif hits</th> <th style="text-align: center;">p-value</th> </tr> </thead> <tbody> <tr> <td style="text-align: center;">PUM1</td> <td style="text-align: center;">1</td> <td style="text-align: center;">100.00</td> <td style="text-align: center;">1</td> <td style="text-align: center;">1.0</td> </tr> <tr> <td style="text-align: center;">PUM2</td> <td style="text-align: center;">1</td> <td style="text-align: center;">100.00</td> <td style="text-align: center;">4</td> <td style="text-align: center;">1.0</td> </tr> </tbody> </table> <p> </p> <p>Column IDs have the following meanings: <strong>RBP ID</strong> -> RBP ID from database or user-defined (typically RBP name), <strong># hit regions</strong> -> number of input genomic regions with motif hits (after filtering and optional extension), <strong>% hit regions</strong> -> percentage of hit regions over all regions (i.e. how many input regions contain >= 1 RBP binding motif), <strong># motif hits</strong> -> number of unique motif hits in input regions (removed double counts), <strong>p-value</strong> -> Wilcoxon rank-sum test p-value.</p> <h2 id="cooc-heat-map">RBP co-occurrences heat map</h2> <p>RBP co-occurrences heat map.</p> <div class=class="container-fluid" style="margin-top:40px"> <iframe src="html_report_plots/co-occurrence_plot.plotly.html" width="1200" height="1200"></iframe> </div> <p><strong>Figure:</strong> Heat map of co-occurrences (Fisher's exact test p-values) between RBPs. Legend color: negative logarithm (base 10) of Fisher's exact test p-value. Hover box: 1) RBP1. 2) RBP2. 3) p-value: Fisher's exact test p-value (calculated based on contingency table between RBP1 and RBP2). 4) RBPs compaired. 5) Counts[]: Contingency table of co-occurrence counts (i.e., number of genomic regions with/without shared motif hits) between compaired RBPs, with format [[A, B], [C, D]], where A: RBP1 AND RBP2, B: NOT RBP1 AND RBP2 C: RBP1 AND NOT RBP2 D: NOT RBP1 AND NOT RBP2. </p> <p> </p> <h2 id="corr-heat-map">RBP correlations heat map</h2> <p>RBP correlations heat map.</p> <div class=class="container-fluid" style="margin-top:40px"> <iframe src="html_report_plots/correlation_plot.plotly.html" width="1200" height="1200"></iframe> </div> <p><strong>Figure:</strong> Heat map of correlations (Pearson correlation coefficients) between RBPs. Genomic regions are labelled 1 or 0 (RBP motif present or not), resulting in a vector of 1s and 0s for each RBP. Correlations are then calculated by comparing vectors for every pair of RBPs. Legend color: Pearson correlation coefficient. Hover box: 1) RBP1. 2) RBP2. 3) RBPs compaired. 5) Counts[]: Contingency table of co-occurrence counts (i.e., number of genomic regions with/without shared motif hits) between compaired RBPs, with format [[A, B], [C, D]], where A: RBP1 AND RBP2, B: NOT RBP1 AND RBP2 C: RBP1 AND NOT RBP2 D: NOT RBP1 AND NOT RBP2. </p> <p> </p>