Analytical visualization functions for dimensionality reduction, quality control, information theory, statistical diagnostics, and time series.
| File |
Purpose |
dimred.py |
PCA, UMAP, t-SNE scatter plots and biplots |
information.py |
Entropy profiles, mutual information matrices, Renyi spectra |
quality.py |
FASTQ quality metrics, GC distribution, adapter content plots |
quality_assessment.py |
Coverage uniformity, error profiles, batch effect QC |
quality_omics.py |
VCF, single-cell, protein structure, multi-omics quality plots |
quality_sequencing.py |
Per-base quality, duplication levels, k-mer profiles |
statistical.py |
Histogram, boxplot, violin, QQ, ROC, correlation heatmap |
timeseries.py |
Time series, autocorrelation, seasonal decomposition, forecast |
| Function |
Description |
plot_pca() |
PCA scatter plot with optional grouping |
plot_umap() |
UMAP embedding visualization |
plot_entropy_profile() |
Positional entropy across sequence or features |
plot_mutual_information_matrix() |
Pairwise MI heatmap |
plot_quality_metrics() |
Multi-panel FASTQ quality summary |
histogram() |
Statistical histogram with optional density overlay |
violin_plot() |
Violin plot for distribution comparison |
plot_time_series() |
Time series line plot with annotations |
from metainformant.visualization.analysis.dimred import plot_pca
from metainformant.visualization.analysis.statistical import histogram
plot_pca(data, color_by="group", output_path="output/pca.png")
histogram(values, bins=50, output_path="output/hist.png")