epiout.plot
Module Contents
Classes
Plot coverage of a given region for samples. |
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QQ plot for p-values. |
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Plot observed vs expected counts. |
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Rank plot for samples for a given statistic. |
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Volcano plot for p-values and z-scores. |
Functions
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Calculate coverage of a bigwig file for a given peak. |
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Plot coverage of a bigwig files for a given peak. |
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Plot coverage of a given region for samples. |
|
QQ plot for p-values. |
|
Plot observed vs expected counts. |
|
Rank plot for samples for a given statistic. |
|
Volcano plot for p-values and z-scores. |
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Plot umap plot for counts. |
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Plot correlation heatmap between samples from counts. |
- epiout.plot.coverage_bigwig(bw, peak: epiout.dataclasses.Peak, rolling_percent=0.1)
Calculate coverage of a bigwig file for a given peak.
- Parameters
bw – Path to bigwig file.
peak – Peak object.
rolling_percent – Percent of peak width for rolling mean.
- Returns
Pandas dataframe with columns position and coverage.
- epiout.plot.plot_coverage_line(df_bigwig, sample, peak: epiout.dataclasses.Peak, rolling_percent=0.1)
Plot coverage of a bigwig files for a given peak.
- Parameters
df_bigwig – Pandas dataframe with columns sample and bigwig.
sample – Sample name.
peak – Peak object for the region.
rolling_percent – Percent of peak width for rolling mean.
- class epiout.plot.CoveragePlot(samples, region, highlight_samples=None, color='#5a5a83', highlight_color='#b1615c', label=True, aspect=1)
Plot coverage of a given region for samples.
- Parameters
samples – Dictionary of sample names and bigwig paths.
region – Region string.
highlight_samples – List of sample names to highlight.
color – Color of the coverage plot.
highlight_color – Color of the highlighted coverage plot.
color – Color of the coverage plot.
highlight_color – Color of the highlighted coverage plot.
label – Whether to label the samples.
aspect – Aspect ratio of the plot.
- data(self)
- _label(self, data, color)
- _fill(self, data, color)
- plot(self)
- epiout.plot.plot_coverage(samples, region, highlight_samples=None, aspect=1, color='#5a5a83', highlight_color='#b1615c', label=True)
Plot coverage of a given region for samples.
- Parameters
samples – Dictionary of sample names and bigwig paths.
region – Region string.
highlight_samples – List of sample names to highlight.
color – Color of the coverage plot.
highlight_color – Color of the highlighted coverage plot.
color – Color of the coverage plot.
highlight_color – Color of the highlighted coverage plot.
label – Whether to label the samples.
aspect – Aspect ratio of the plot.
- class epiout.plot.QQPlot(pvalues, highlight=None, ci=0.95, eps=1e-14)
QQ plot for p-values.
- Parameters
pvalues – as dataframe where each row is a sample and each column is a p-value.
highlight – List of sample names to highlight.
ci – Confidence interval for the expected p-values.
eps – Epsilon value to avoid zero p-values.
- data(self)
- plot(self)
- epiout.plot.qq_plot(pvalues, highlight=None, ci=0.95, eps=1e-14)
QQ plot for p-values.
- Parameters
pvalues – as dataframe where each row is a sample and each column is a p-value.
highlight – List of sample names to highlight.
ci – Confidence interval for the expected p-values.
eps – Epsilon value to avoid zero p-values.
- class epiout.plot.CountsPlot(counts, counts_mean, highlight=None)
Plot observed vs expected counts.
- Parameters
counts – Observed counts as dataframe where each row is a sample and columns are peaks.
counts_mean – Expected counts as dataframe where each row is a sample and columns are peaks.
highlight – List of sample names to highlight.
- data(self)
- plot(self)
- epiout.plot.plot_counts(counts, counts_mean, highlight=None)
Plot observed vs expected counts.
- Parameters
counts – Observed counts as dataframe where each row is a sample and columns are peaks.
counts_mean – Expected counts as dataframe where each row is a sample and columns are peaks.
highlight – List of sample names to highlight.
- class epiout.plot.RankPlot(stats, stats_name='score', highlight=None)
Rank plot for samples for a given statistic.
- Parameters
stats – Statistics as a list.
stats_name – Name of the statistic.
highlight – List of sample names to highlight.
- data(self)
- plot(self)
- epiout.plot.plot_rank(stats, stats_name='score', highlight=None)
Rank plot for samples for a given statistic.
- Parameters
stats – Statistics as a list.
stats_name – Name of the statistic.
highlight – List of sample names to highlight.
- class epiout.plot.VolconaPlot(pval, zscore, pval_cutoff=0.05, zscore_cutoff=(- 1, 1))
Volcano plot for p-values and z-scores.
- Parameters
pval – P-values as a list.
zscore – Z-scores as a list.
pval_cutoff – P-value cutoff for significance.
zscore_cutoff – Z-score cutoff for significance.
- data(self)
- plot(self)
- epiout.plot.plot_volcona(pval, zscore, pval_cutoff=0.05, zscore_cutoff=(- 1, 1))
Volcano plot for p-values and z-scores.
- Parameters
pval – P-values as a list.
zscore – Z-scores as a list.
pval_cutoff – P-value cutoff for significance.
zscore_cutoff – Z-score cutoff for significance.
- epiout.plot.plot_umap(df_counts, color=None, legend_title='color')
Plot umap plot for counts.
- Parameters
df_counts – Counts as dataframe where each row is a sample and columns are peaks.
color – Color of the points.
legend_title – Title of the legend.
- epiout.plot.plot_corr_heatmap(df_counts, row_var=None, cbar_pos=(- 0.06, 0.45, 0.03, 0.2), cmap='twilight_shifted', vmin=- 1, vmax=1)
Plot correlation heatmap between samples from counts.
- Parameters
df_counts – Counts as dataframe where each row is a sample and columns are peaks.
row_var – Variable to color the rows.
cbar_pos – Position of the colorbar.
cmap – Colormap.
vmin – Minimum value for the colorbar.
vmax – Maximum value for the colorbar.