Density map

Draw 2D heatmaps as density isolines.

This notebook does not actually use histoquant and relies only on brainglobe-heatmap to extract brain structures outlines.

Only the detections measurements with atlas coordinates exported from QuPath are used.

You need to select the range of data to be used, the regions outlines will be extracted at the centroid of that range. Therefore, a range that is too large will be misleading and irrelevant.

In [1]:

import brainglobe_heatmap as bgh
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

import brainglobe_heatmap as bgh import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns

In [2]:

# path to the exported measurements from QuPath
filename = "../../resources/cells_measurements_detections.tsv"

# path to the exported measurements from QuPath filename = "../../resources/cells_measurements_detections.tsv"

Settings

In [3]:

# atlas to use
atlas_name = "allen_mouse_10um"
# brain regions whose outlines will be plotted
regions = ["root", "CB", "MY", "GRN", "IRN"]
# range to include, in Allen coordinates, in microns
ap_lims = [9800, 10000]  # lims : [0, 13200] for coronal
ml_lims = [5600, 5800]  # lims : [0, 11400] for sagittal
dv_lims = [3900, 4100]  # lims : [0, 8000] for top
# number of isolines
nlevels = 5
# color mapping between classification and matplotlib color
palette = {"Cells: marker-": "#d8782f", "Cells: marker+": "#8ccb73"}

# atlas to use atlas_name = "allen_mouse_10um" # brain regions whose outlines will be plotted regions = ["root", "CB", "MY", "GRN", "IRN"] # range to include, in Allen coordinates, in microns ap_lims = [9800, 10000] # lims : [0, 13200] for coronal ml_lims = [5600, 5800] # lims : [0, 11400] for sagittal dv_lims = [3900, 4100] # lims : [0, 8000] for top # number of isolines nlevels = 5 # color mapping between classification and matplotlib color palette = {"Cells: marker-": "#d8782f", "Cells: marker+": "#8ccb73"}

In [4]:

df = pd.read_csv(filename, sep="\t")
display(df.head())

df = pd.read_csv(filename, sep="\t") display(df.head())

	Image	Object ID	Object type	Name	Classification	Parent	ROI	Atlas_X	Atlas_Y	Atlas_Z
0	animalid0_030.ome.tiff	5ff386a8-5abd-46d1-8e0d-f5c5365457c1	Detection	NaN	Cells: marker-	VeCB	Polygon	11.5230	4.2724	4.2767
1	animalid0_030.ome.tiff	9a2a9a8c-acbe-4308-bc5e-f3c9fd1754c0	Detection	NaN	Cells: marker-	VeCB	Polygon	11.5202	4.2784	4.4186
2	animalid0_030.ome.tiff	481a519b-8b40-4450-9ec6-725181807d72	Detection	NaN	Cells: marker-	VeCB	Polygon	11.5060	4.3172	4.3563
3	animalid0_030.ome.tiff	fd28e09c-2c64-4750-b026-cd99e3526a57	Detection	NaN	Cells: marker-	VeCB	Polygon	11.5284	4.2574	4.3364
4	animalid0_030.ome.tiff	3d9ce034-f2ed-4c73-99be-f782363cf323	Detection	NaN	Cells: marker-	VeCB	Polygon	11.5487	4.2033	4.2943

Here we can filter out classifications we don't wan't to display.

In [5]:

# select objects
# df = df[df["Classification"] == "example: classification"]

# select objects # df = df[df["Classification"] == "example: classification"]

In [6]:

# get outline coordinates in coronal (=frontal) orientation
coords_coronal = bgh.get_structures_slice_coords(
    regions,
    orientation="frontal",
    atlas_name=atlas_name,
    position=(np.mean(ap_lims), 0, 0),
)
# get outline coordinates in sagittal orientation
coords_sagittal = bgh.get_structures_slice_coords(
    regions,
    orientation="sagittal",
    atlas_name=atlas_name,
    position=(0, 0, np.mean(ml_lims)),
)
# get outline coordinates in top (=horizontal) orientation
coords_top = bgh.get_structures_slice_coords(
    regions,
    orientation="horizontal",
    atlas_name=atlas_name,
    position=(0, np.mean(dv_lims), 0),
)

# get outline coordinates in coronal (=frontal) orientation coords_coronal = bgh.get_structures_slice_coords( regions, orientation="frontal", atlas_name=atlas_name, position=(np.mean(ap_lims), 0, 0), ) # get outline coordinates in sagittal orientation coords_sagittal = bgh.get_structures_slice_coords( regions, orientation="sagittal", atlas_name=atlas_name, position=(0, 0, np.mean(ml_lims)), ) # get outline coordinates in top (=horizontal) orientation coords_top = bgh.get_structures_slice_coords( regions, orientation="horizontal", atlas_name=atlas_name, position=(0, np.mean(dv_lims), 0), )

In [7]:

# Coronal projection
# select objects within the rostro-caudal range
df_coronal = df[
    (df["Atlas_X"] >= ap_lims[0] / 1000) & (df["Atlas_X"] <= ap_lims[1] / 1000)
]

plt.figure()

for struct_name, contours in coords_coronal.items():
    for cont in contours:
        plt.fill(cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k")

# see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize
ax = sns.kdeplot(
    df_coronal,
    x="Atlas_Z",
    y="Atlas_Y",
    hue="Classification",
    levels=nlevels,
    common_norm=False,
    palette=palette,
)
ax.invert_yaxis()
sns.despine(left=True, bottom=True)
plt.axis("equal")
plt.xlabel(None)
plt.ylabel(None)
plt.xticks([])
plt.yticks([])
plt.plot([2, 3], [8, 8], "k", linewidth=3)
plt.text(2, 7.9, "1 mm")

# Coronal projection # select objects within the rostro-caudal range df_coronal = df[ (df["Atlas_X"] >= ap_lims[0] / 1000) & (df["Atlas_X"] <= ap_lims[1] / 1000) ] plt.figure() for struct_name, contours in coords_coronal.items(): for cont in contours: plt.fill(cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k") # see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize ax = sns.kdeplot( df_coronal, x="Atlas_Z", y="Atlas_Y", hue="Classification", levels=nlevels, common_norm=False, palette=palette, ) ax.invert_yaxis() sns.despine(left=True, bottom=True) plt.axis("equal") plt.xlabel(None) plt.ylabel(None) plt.xticks([]) plt.yticks([]) plt.plot([2, 3], [8, 8], "k", linewidth=3) plt.text(2, 7.9, "1 mm")

Out[7]:

Text(2, 7.9, '1 mm')

In [8]:

# Sagittal projection
# select objects within the medio-lateral range
df_sagittal = df[
    (df["Atlas_Z"] >= ml_lims[0] / 1000) & (df["Atlas_Z"] <= ml_lims[1] / 1000)
]

plt.figure()

for struct_name, contours in coords_sagittal.items():
    for cont in contours:
        plt.fill(cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k")

# see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize
ax = sns.kdeplot(
    df_sagittal,
    x="Atlas_X",
    y="Atlas_Y",
    hue="Classification",
    levels=nlevels,
    common_norm=False,
    palette=palette,
)
ax.invert_yaxis()
sns.despine(left=True, bottom=True)
plt.axis("equal")
plt.xlabel(None)
plt.ylabel(None)
plt.xticks([])
plt.yticks([])
plt.plot([2, 3], [7.1, 7.1], "k", linewidth=3)
plt.text(2, 7, "1 mm")

# Sagittal projection # select objects within the medio-lateral range df_sagittal = df[ (df["Atlas_Z"] >= ml_lims[0] / 1000) & (df["Atlas_Z"] <= ml_lims[1] / 1000) ] plt.figure() for struct_name, contours in coords_sagittal.items(): for cont in contours: plt.fill(cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k") # see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize ax = sns.kdeplot( df_sagittal, x="Atlas_X", y="Atlas_Y", hue="Classification", levels=nlevels, common_norm=False, palette=palette, ) ax.invert_yaxis() sns.despine(left=True, bottom=True) plt.axis("equal") plt.xlabel(None) plt.ylabel(None) plt.xticks([]) plt.yticks([]) plt.plot([2, 3], [7.1, 7.1], "k", linewidth=3) plt.text(2, 7, "1 mm")

Out[8]:

Text(2, 7, '1 mm')

In [9]:

# Top projection
# select objects within the dorso-ventral range
df_top = df[(df["Atlas_Y"] >= dv_lims[0] / 1000) & (df["Atlas_Y"] <= dv_lims[1] / 1000)]

plt.figure()

for struct_name, contours in coords_top.items():
    for cont in contours:
        plt.fill(-cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k")

# see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize
ax = sns.kdeplot(
    df_top,
    x="Atlas_Z",
    y="Atlas_X",
    hue="Classification",
    levels=nlevels,
    common_norm=False,
    palette=palette,
)
ax.invert_yaxis()
sns.despine(left=True, bottom=True)
plt.axis("equal")
plt.xlabel(None)
plt.ylabel(None)
plt.xticks([])
plt.yticks([])
plt.plot([0.5, 1.5], [0.5, 0.5], "k", linewidth=3)
plt.text(0.5, 0.4, "1 mm")

# Top projection # select objects within the dorso-ventral range df_top = df[(df["Atlas_Y"] >= dv_lims[0] / 1000) & (df["Atlas_Y"] <= dv_lims[1] / 1000)] plt.figure() for struct_name, contours in coords_top.items(): for cont in contours: plt.fill(-cont[:, 0] / 1000, cont[:, 1] / 1000, lw=1, fc="none", ec="k") # see https://seaborn.pydata.org/generated/seaborn.kdeplot.html to customize ax = sns.kdeplot( df_top, x="Atlas_Z", y="Atlas_X", hue="Classification", levels=nlevels, common_norm=False, palette=palette, ) ax.invert_yaxis() sns.despine(left=True, bottom=True) plt.axis("equal") plt.xlabel(None) plt.ylabel(None) plt.xticks([]) plt.yticks([]) plt.plot([0.5, 1.5], [0.5, 0.5], "k", linewidth=3) plt.text(0.5, 0.4, "1 mm")

Out[9]:

Text(0.5, 0.4, '1 mm')

In [ ]: