<p>The <strong>Differential Analysis</strong> tab provides users with the ability to analyze differences in gene or protein expression between different experimental conditions. This analysis is crucial for identifying biomarkers or understanding biological pathways that are significantly affected. Below is a comprehensive guide to using the tab effectively, including data uploading, setting parameters, and visualizing the results interactively.</p>
<h4>1. Sidebar Panel: Uploading and Configuring Data</h4>
<p>The <strong>Sidebar Panel</strong> contains various controls for uploading expression data and metadata, configuring preprocessing options, and starting the analysis. Here's a detailed breakdown of each control:</p>
<ul>
<li><strong>Expression Data Upload:</strong>
<p>Use the <code>Expression table</code> input to upload your expression dataset. This file should have gene/protein identifiers in the first column, with expression values in the remaining columns. Accepted formats include <code>.csv</code> and <code>.tsv</code>.</p>
</li>
<li><strong>Metadata Upload:</strong>
<p>The <code>Metadata</code> input is used to upload sample information, such as experimental conditions. Metadata should include columns like sample identifiers and group labels (e.g., control vs. treatment). Proper metadata is crucial for accurate differential analysis.</p>
</li>
<li><strong>Format Selection:</strong>
<p>Select the appropriate file format (CSV or TSV) using the <code>Format</code> dropdown to ensure that your data is properly interpreted by the system.</p>
</li>
<li><strong>Load CPTAC data:</strong>
<p>If you want to explore the features without uploading your own data, use the <code>Load CPTAC data</code> button. You can specify the type of data (e.g., Proteome or Phosphoproteome) and the cancer type (e.g., Lung Adenocarcinoma). This is helpful for understanding the workflow.</p>
</li>
<li><strong>Gene Filtering:</strong>
<p>Choose a gene filtering method to refine your dataset before running the differential analysis:
<ul>
<li><strong>Variance:</strong> Retains features with high variability across samples, as they are likely to be biologically significant.</li>
<li><strong>minExpression:</strong> Filters out genes or proteins that are not expressed above a minimum level in a certain proportion of samples.</li>
<li><strong>None:</strong> No filtering is applied, and all features are retained.</li>
</ul>
</p>
</li>
<li><strong>Cutoff Parameters:</strong>
<p>Adjust the thresholds for filtering using the provided sliders:
<ul>
<li><strong>Variance Cutoff:</strong> Set the minimum variance level to retain highly variable features.</li>
<li><strong>Minimum Expression:</strong> Set the minimum expression level and the proportion of samples required for a gene/protein to be retained.</li>
</ul>
</p>
</li>
<li><strong>Conversion Options:</strong>
<p>Use the <code>Ensembl to symbol</code> option to convert Ensembl IDs to gene symbols for easier interpretation.</p>
</li>
<li><strong>Preprocess Button:</strong>
<p>After configuring all the settings, click the <code>Process</code> button to apply filtering, normalization, and other preprocessing steps to your data.</p>
<h4>3. DEG Analysis and Functional Exploration</h4>
<p>Generate tables and plots to analyze differentially expressed genes and proteins. Leverage enrichment analysis tools to explore pathways and protein-protein interactions.</p>
