Fibers length in multi animals¶

This example uses synthetic data to showcase how histoquant can be used in a pipeline.

Annotations measurements should be exported from QuPath, following the required directory structure.

Alternatively, you can merge all your CSV files yourself, one per animal, adding an animal ID to each table. Those can be processed with the histoquant.process.process_animal() function, in a loop, collecting the results at each iteration and finally concatenating the results. Finally, those can be used with display module. See the API reference for the process module.

In [1]:

import cuisto

import cuisto

In [2]:

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

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

In [3]:

# Files
wdir = "../../resources/multi"
animals = ["mouse0", "mouse1"]

# Files wdir = "../../resources/multi" animals = ["mouse0", "mouse1"]

In [4]:

# get configuration
cfg = cuisto.Config(config_file)

# get configuration cfg = cuisto.Config(config_file)

In [5]:

# get distributions per regions
df_regions, _, _ = cuisto.process.process_animals(
    wdir, animals, cfg, compute_distributions=False
)

# have a look
display(df_regions.head(10))

# get distributions per regions df_regions, _, _ = cuisto.process.process_animals( wdir, animals, cfg, compute_distributions=False ) # have a look display(df_regions.head(10))

Processing mouse1: 100%|██████████| 2/2 [00:00<00:00, 15.66it/s]

	Name	hemisphere	Area µm^2	Area mm^2	length µm	length mm	density µm^-1	density mm^-1	coverage index	relative count	relative density	channel	animal
0	ACVII	Contra.	9099.04	0.009099	468.0381	0.468038	0.051438	51438.184688	24.07503	0.00064	0.022168	marker3	mouse0
1	ACVII	Contra.	9099.04	0.009099	4260.4844	4.260484	0.468234	468234.495068	1994.905762	0.0019	0.056502	marker2	mouse0
2	ACVII	Contra.	9099.04	0.009099	5337.7103	5.33771	0.586623	586623.45698	3131.226069	0.010104	0.242734	marker1	mouse0
3	ACVII	Ipsi.	4609.90	0.004610	0.0	0.0	0.0	0.0	0.0	0.0	0.0	marker3	mouse0
4	ACVII	Ipsi.	4609.90	0.004610	0.0	0.0	0.0	0.0	0.0	0.0	0.0	marker2	mouse0
5	ACVII	Ipsi.	4609.90	0.004610	0.0	0.0	0.0	0.0	0.0	0.0	0.0	marker1	mouse0
6	ACVII	both	13708.94	0.013709	468.0381	0.468038	0.034141	34141.086036	15.979329	0.000284	0.011001	marker3	mouse0
7	ACVII	both	13708.94	0.013709	4260.4844	4.260484	0.310781	310781.460857	1324.079566	0.000934	0.030688	marker2	mouse0
8	ACVII	both	13708.94	0.013709	5337.7103	5.33771	0.38936	389359.811918	2078.289878	0.00534	0.142623	marker1	mouse0
9	AMB	Contra.	122463.80	0.122464	30482.7815	30.482782	0.248913	248912.588863	7587.548059	0.041712	0.107271	marker3	mouse0

In [6]:

figs_regions = cuisto.display.plot_regions(df_regions, cfg)

figs_regions = cuisto.display.plot_regions(df_regions, cfg)