Plot all channels for one site

This notebook demonstrates how to retrieve and plot all channels for one site using the jump_portrait library.

import matplotlib.colors as mpl  # noqa: CPY001
import numpy as np
import polars as pl
from jump_portrait.fetch import get_item_location_info, get_jump_image
from matplotlib import pyplot as plt

First, we need to get location information telling us where all images corresponding to a specific perturbation can be found. We will use the “get_item_location” function from jump_portrait for this. Here we retrieve image locations for the “RAB30” gene:

gene_info = get_item_location_info("RAB30")
gene_info.shape

Downloading data from 'https://github.com/jump-cellpainting/datasets/raw/c68deb2babc83747e6b14d8a77e5655138a6086a/metadata/well.csv.gz' to file '/home/runner/.cache/pooch/4efbf4dd3dd9aaecc8ccb9fc3c6b4122-well.csv.gz'.
Downloading data from 'https://github.com/jump-cellpainting/datasets/raw/c68deb2babc83747e6b14d8a77e5655138a6086a/metadata/plate.csv.gz' to file '/home/runner/.cache/pooch/a530bb82de29e39332bdef6f29397769-plate.csv.gz'.

(90, 28)

There are 90 images: 9 sites/well X 5 replicate wells X 2 data types (CRISPR & ORF). We can also retrieve locations for compound data. By default, the function assumes a query by INCHI key. We can also query by JCP ID by specifying the query column:

cmpd_info_byinchi = get_item_location_info("CLETVKMYAXARPO-UHFFFAOYSA-N")
cmpd_info_byjcp = get_item_location_info("JCP2022_011844", input_column="JCP2022")

print(cmpd_info_byinchi.shape)
print(cmpd_info_byjcp.shape)

(34, 28)
(34, 28)

There are 34 sites corresponding to this compound. We’ve written a function to display all channels for a specific image. Note that this is just one possible way to display images - we’ve included the function here so that you can modify it to suit your own needs.

def display_site(
    source: str,
    batch: str,
    plate: str,
    well: str,
    site: str,
    label: str,
    int_percentile: float,
) -> None:
    """Plot all channels from one image.

    Parameters
    ----------
    source : String
        Source ID for image of interest.
    batch : String
        Batch ID for image of interest.
    plate : String
        Plate ID for image of interest.
    well : String
        Well ID for image of interest.
    site : String
        Site ID for image of interest.
    label : String
        Label to display in lower left corner.
    int_percentile: float
        Rescale the image from 0 - this percentile of intensity values.

    """
    n_rows = 2
    n_cols = 3

    # Make images
    axes = plt.subplots(n_rows, n_cols, figsize=(2.6 * n_cols, 2.6 * n_rows))[1]
    axes = axes.ravel()
    counter = 0

    channel_rgb = {
        "DNA": "#FF0000",  # Blue
        "ER": "#65fe08",  # Green
        "Mito": "#FFFF00",  # Red
        "AGP": "#FFFF00",  # Yellow
        "RNA": "#FFA500",  # Orange
    }

    for ax, (channel, rgb) in zip(axes, channel_rgb.items()):
        cmap = mpl.LinearSegmentedColormap.from_list(channel, ("#000", rgb))

        img = get_jump_image(source, batch, plate, well, channel, site, None)

        ax.imshow(img, vmin=0, vmax=np.percentile(img, int_percentile), cmap=cmap)
        ax.axis("off")

        # Add channel name label in the top left corner
        ax.text(
            0.05,
            0.95,
            channel,
            horizontalalignment="left",
            verticalalignment="top",
            fontsize=18,
            color="black",
            bbox=dict(
                facecolor="white", alpha=0.8, edgecolor="none", boxstyle="round,pad=0.3"
            ),
            transform=ax.transAxes,
        )

    # put label in last subplot
    ax = axes[-1]
    ax.text(
        0.5,
        0.5,
        label,
        horizontalalignment="center",
        verticalalignment="center",
        fontsize=20,
        color="black",
        transform=ax.transAxes,
    )
    ax.axis("off")

    # show plot
    plt.tight_layout()

We can get the required location parameters from the location info that we retrieved earlier. Here we get parameters for the first site in the JCP compound results:

source, batch, plate, well, site, *rest = cmpd_info_byjcp.row(0)

Next, we define the label and make the plot:

label = "{}\n\nplate:\n{}\nwell: {}\nsite: {}"

display_site(
    source,
    batch,
    plate,
    well,
    site,
    label.format("JCP2022_011844", plate, well, site),
    99.5,
)

Here, we plot one of the RAB30 ORF images:

source, batch, plate, well, site, *rest = gene_info.filter(
    pl.col("Metadata_PlateType") == "ORF"
).row(0)
display_site(
    source,
    batch,
    plate,
    well,
    site,
    label.format("RAB30 (ORF)", plate, well, site),
    99.5,
)

And for CRISPR:

source, batch, plate, well, site, *rest = gene_info.filter(
    pl.col("Metadata_PlateType") == "CRISPR"
).row(0)
display_site(
    source,
    batch,
    plate,
    well,
    site,
    label.format("RAB30 (CRISPR)", plate, well, site),
    99.5,
)

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