{% extends "base_template.html" %} {% block title %}Correlation Heatmaps - PyCodon Analyzer Report{% endblock %} {% block content %}
This section displays heatmaps illustrating correlations between various calculated features. The correlation method used (e.g., Spearman) is indicated in the plot titles or filenames. Values range from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear correlation. The intensity and color of the cells represent the strength and direction of the correlation.
Feature Correlation Heatmap is not available.
{% endif %}This heatmap shows the pairwise correlations between various sequence metrics calculated across all valid sequences (e.g., GC content, ENC, CAI, RCDI, protein properties like GRAVY and Aromaticity, sequence length, etc.). It helps to identify potential relationships or co-variation between different measures of codon usage and sequence composition. For example, a strong positive correlation between CAI and GC3 might suggest that adaptation to preferred codons is linked to GC content at the third codon position. Annotations on the cells indicate the correlation coefficient.
CA Axes vs. Features Correlation Heatmap is not available.
{% endif %}This heatmap displays the correlation between the main axes (e.g., Dimension 1 and Dimension 2) derived from the combined Correspondence Analysis (CA) and various sequence features. These features include both the calculated metrics (like GC, ENC, CAI) and the RSCU values for each individual codon. Significant correlations (typically p < 0.05) are often marked with an asterisk (*).
Interpreting this plot can reveal which sequence characteristics or codon preferences drive the major trends in codon usage variation captured by the CA. For instance:
This helps to give a biological or mechanistic interpretation to the abstract axes of the CA.
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