scallops.visualize.histogram.in_situ_barcode_hist_plot

scallops.visualize.histogram.in_situ_barcode_hist_plot(reads_df, counts=(0, 1, 2, 3, 4, 5), hue=None, normalize=True, **fig_kw)

Generate a histogram plot depicting the percentage of cells containing barcode reads. This function takes a DataFrame with read tables and plots a histogram showing the distribution of cells based on the number of barcode reads.

Parameters:

• reads_df (DataFrame) – DataFrame containing read tables from the output reads folder.

• counts (Sequence[int] | None) – Optional list of histogram bins.

• hue (str) – Grouping variable for the second layer of grouping.

• normalize (bool) – Whether to normalize counts.

• fig_kw – Additional keyword arguments passed to .pyplot.figure call.

Returns:

Returns the Axes object with the plot drawn onto it.

Example:

from scallops.visualize import in_situ_barcode_hist_plot
import pandas as pd
import numpy as np

data = {
    "well": np.random.choice(["A", "B", "C", "D"], size=100),
    "label": np.random.choice(range(1, 10), size=100),
    "barcode_match": np.random.choice([True, False], size=100),
}
reads_df = pd.DataFrame(data)
in_situ_barcode_hist_plot(
    reads_df, counts=[0, 1, 2, 3], hue="well", normalize=True
)

Return type:

Axes