Fibers coverage

Plot regions coverage percentage in the spinal cord.

This showcases that any brainglobe atlases should be supported.

Here we're going to quantify the percentage of area of each spinal cord regions innervated by axons.

The "area µm^2" measurement for each annotations can be created in QuPath with a pixel classifier, using the Measure button.

We're going to consider that the "area µm^2" measurement generated by the pixel classifier is an object count.
histoquant computes a density, which is the count in each region divided by its aera.
Therefore, in this case, it will be actually the fraction of area covered by fibers in a given color.

The data was generated using QuPath with a pixel classifier on toy data.

In [1]:

import pandas as pd

import cuisto

import pandas as pd import cuisto

In [2]:

# Full path to your configuration file, edited according to your need beforehand
config_file = "../../resources/demo_config_fibers.toml"

# Full path to your configuration file, edited according to your need beforehand config_file = "../../resources/demo_config_fibers.toml"

In [3]:

# - Files
# not important if only one animal
animal = "animalid1-SC"
# set the full path to the annotations tsv file from QuPath
annotations_file = "../../resources/fibers_measurements_annotations.tsv"

# - Files # not important if only one animal animal = "animalid1-SC" # set the full path to the annotations tsv file from QuPath annotations_file = "../../resources/fibers_measurements_annotations.tsv"

In [4]:

# get configuration
cfg = cuisto.config.Config(config_file)

# get configuration cfg = cuisto.config.Config(config_file)

In [5]:

# read data
df_annotations = pd.read_csv(annotations_file, index_col="Object ID", sep="\t")
df_detections = pd.DataFrame()  # empty DataFrame

# remove annotations that are not brain regions
df_annotations = df_annotations[df_annotations["Classification"] != "Region*"]
df_annotations = df_annotations[df_annotations["ROI"] != "Rectangle"]

# have a look
display(df_annotations.head())

# read data df_annotations = pd.read_csv(annotations_file, index_col="Object ID", sep="\t") df_detections = pd.DataFrame() # empty DataFrame # remove annotations that are not brain regions df_annotations = df_annotations[df_annotations["Classification"] != "Region*"] df_annotations = df_annotations[df_annotations["ROI"] != "Rectangle"] # have a look display(df_annotations.head())

	Image	Object type	Name	Classification	Parent	ROI	Centroid X µm	Centroid Y µm	Fibers: EGFP area µm^2	Fibers: DsRed area µm^2	ID	Side	Parent ID	Area µm^2	Perimeter µm
Object ID
dcfe5196-4e8d-4126-b255-a9ea393c383a	animalid1-SC_s1.ome.tiff	Annotation	Root	NaN	Root object (Image)	Geometry	1353.70	1060.00	108993.1953	15533.3701	NaN	NaN	NaN	3172474.0	9853.3
acc74bc0-3dd0-4b3e-86e3-e6c7b681d544	animalid1-SC_s1.ome.tiff	Annotation	root	Right: root	Root	Polygon	864.44	989.95	39162.8906	5093.2798	250.0	0.0	NaN	1603335.7	4844.2
94571cf9-f22b-453f-860c-eb13d0e72440	animalid1-SC_s1.ome.tiff	Annotation	WM	Right: WM	root	Geometry	791.00	1094.60	20189.0469	2582.4824	130.0	0.0	250.0	884002.0	7927.8
473d65fb-fda4-4721-ba6f-cc659efc1d5a	animalid1-SC_s1.ome.tiff	Annotation	vf	Right: vf	WM	Polygon	984.31	1599.00	6298.3574	940.4100	70.0	0.0	130.0	281816.9	2719.5
449e2cd1-eca2-4708-83fe-651f378c3a14	animalid1-SC_s1.ome.tiff	Annotation	df	Right: df	WM	Polygon	1242.90	401.26	1545.0750	241.3800	74.0	0.0	130.0	152952.8	1694.4

In [6]:

# get distributions per regions, spatial distributions and coordinates
df_regions, dfs_distributions, df_coordinates = cuisto.process.process_animal(
    animal, df_annotations, df_detections, cfg, compute_distributions=False
)

# convert the "density µm^-2" column, which is actually the coverage fraction, to a percentage
df_regions["density µm^-2"] = df_regions["density µm^-2"] * 100

# have a look
display(df_regions.head())

# get distributions per regions, spatial distributions and coordinates df_regions, dfs_distributions, df_coordinates = cuisto.process.process_animal( animal, df_annotations, df_detections, cfg, compute_distributions=False ) # convert the "density µm^-2" column, which is actually the coverage fraction, to a percentage df_regions["density µm^-2"] = df_regions["density µm^-2"] * 100 # have a look display(df_regions.head())

	Name	hemisphere	Area µm^2	Area mm^2	area µm^2	area mm^2	density µm^-2	density mm^-2	coverage index	relative count	relative density	channel	animal
0	10Sp	Contra.	1749462.18	1.749462	53117.3701	53.11737	3.036211	30362.113973	1612.755645	0.036535	0.033062	Negative	animalid1-SC
0	10Sp	Contra.	1749462.18	1.749462	5257.1025	5.257103	0.300498	3004.98208	15.797499	0.030766	0.02085	Positive	animalid1-SC
1	10Sp	Ipsi.	1439105.93	1.439106	64182.9823	64.182982	4.459921	44599.206328	2862.51007	0.023524	0.023265	Negative	animalid1-SC
1	10Sp	Ipsi.	1439105.93	1.439106	8046.3375	8.046337	0.559121	5591.205854	44.988729	0.028911	0.022984	Positive	animalid1-SC
2	10Sp	both	3188568.11	3.188568	117300.3524	117.300352	3.678778	36787.783216	4315.219935	0.028047	0.025734	Negative	animalid1-SC

In [7]:

# plot distributions per regions
fig_regions = cuisto.display.plot_regions(df_regions, cfg)
# specify which regions to plot
# fig_regions = hq.display.plot_regions(df_regions, cfg, names_list=["Rh9", "Sr9", "8Sp"])

# save as svg
# fig_regions[0].savefig(r"C:\Users\glegoc\Downloads\nice_figure.svg")

# plot distributions per regions fig_regions = cuisto.display.plot_regions(df_regions, cfg) # specify which regions to plot # fig_regions = hq.display.plot_regions(df_regions, cfg, names_list=["Rh9", "Sr9", "8Sp"]) # save as svg # fig_regions[0].savefig(r"C:\Users\glegoc\Downloads\nice_figure.svg")