pythologist

Read and analyze cell image data.

Intro

Pythologist 1) reads exports from InForm software or other sources into a common storage format, and 2) extracts basic analysis features from cell image data. This software is generally intended to be run from a jupyter notebook and provides hooks into the image data so that the user can have the flexability to execute analyses they design or find in the primary literature.

List of image analysis publications

Pythologist is based on IrisSpatialFeatures (C.D. Carey, ,D. Gusenleitner, M. Lipshitz, et al. Blood. 2017) https://doi.org/10.1182/blood-2017-03-770719, and is implemented in the python programming language.

Features Pythologist add are:

• An common CellProjectGeneric storage class, and classical inheritance conventions to organize the importation of different data types.
• A mutable CellDataFrame class that can be used for slicing, and combining projects.
• The ability to add binary features to cells based on cell-cell contacts or cell proximity.
• Customizable images based on the cell segmentation or heatmaps spaninng the cartesian coordinates.
• Specify cell populations through a SubsetLogic syntax for quick selection of mutually exclusive phenotypes or binary features
• A set of Quality Check functions to identify potential issues in imported data.

pythologist-reader

Read files from sources like inForm software by PerkinElmer.

Storage Object - Read the Docs

pythologist-image-utilities

For function details please Read The Docs

Functions for working with image data in python. This set of functions are used in the construction of pythologist-reader intermediate storage format, and in the analysis of image data .. finding neighbors .. or the generation of images.

pythologist-schemas

1. Check assumptions for input types prior to ingestion.
2. Define a universal intermediate storage format suitable for downstream analysis.
3. Define input parameters suitable custom for pipeline execution of feature extraction.

This package should facilitate the validation of inputs.

This section is in development.

Documentation

Primary Software

• pythologist This software package uses a CellDataFrame class, an extension of a Pandas DataFrame to modify data and execute analyses [Read the Docs] [source]
  • pythologist-schemas This submodule documents/defines the formats of inputs and outputs expected in this pipeline. [source]
  • pythologist-reader This submodule facillitates reading platform-specific data into a harmonized format. [Read the Docs] [source]
  • pythologist-test-images This submodule has some example data [source]
  • pythologist-image-utilities This submodule has helper functions to work with images [Read the Docs] [source]

Additional Analytics

• good-neighbors This package facilitates the analysis of cellular data based on their proximal "cellular neighborhoods" [Read the Docs] [source]

Quickstart

The docker is the fastest way to get working with the latest version of pytholgoist.

To start a jupyter lab notebook with the required software as your user in your current drectory you can use the following command

docker run -v "$(pwd)":"/work" --rm -p 8888:8888 --user $(id -u):$(id -g) vacation/pythologist:latest

This will start jupyter lab on port 8888 as your user and group.

Any of the test data examples should work fine in this environment.

Installation of the latest version

The pip install is not up-to-date with the current version of the package.

    git clone https://github.com/dfci/pythologist.git 

Common tasks

Reading in a project composed of InForm exports

Basic

The assumption here is that the exports are grouped so that sample folders contain one or more image exports, and that sample name can be inferred from the last folder name.

from pythologist_test_images import TestImages
from pythologist_reader.formats.inform.sets import CellProjectInForm
import matplotlib.pyplot as plt

# Get the path of the test dataset
path = TestImages().raw('IrisSpatialFeatures')
# Create the storage opbject where the project will be saved
cpi = CellProjectInForm('pythologist.h5',mode='w')
# Read the project data
cpi.read_path(path,require=False,verbose=True,microns_per_pixel=0.496,sample_name_index=-1)
# Display one of the cell map images
for f in cpi.frame_iter():
    break
print(f.frame_name)
plt.imshow(f.cell_map_image(),origin='upper')
plt.show()

MEL2_7

Custom region annotations from tumor and invasive margin image drawings

Another format supported for a project import is one with a custom tumor and invasive margin definition. Similar to above, the project is organized into sample folders, and each image within each sample folder has a tif file defining the tumor and invasive margin. These come in the form of a <image name prefix>_Tumor.tif and <image name prefix>_Invasive_Margin.tif for each image. The _Tumor.tif is an area filled in where the tumor is, and transparent elsewhere. The _Invasive_Margin.tif is a drawn line of a known width. steps is used to grow the margin out that many pixels in each direction to establish an invasive margin region. Here we also rename some markers during read-in to clean up the syntax of thresholding on binary features.

from pythologist_test_images import TestImages
from pythologist_reader.formats.inform.custom import CellProjectInFormLineArea

# Get the path of the test dataset
path = TestImages().raw('IrisSpatialFeatures')
# Specify where the data read-in will be stored as an h5 object
cpi = CellProjectInFormLineArea('test.h5',mode='w')
# Read in the data (gets stored on the fly into the h5 object)
cpi.read_path(path,
              sample_name_index=-1,
              verbose=True,
              steps=76,
              project_name='IrisSpatialFeatures',
              microns_per_pixel=0.496)
for f in cpi.frame_iter():
    break
print(f.frame_name)
print('hand drawn margin')
plt.imshow(f.get_image(f.get_data('custom_images').\
    set_index('custom_label').loc['Drawn','image_id']),origin='upper')
plt.show()
print('hand drawn tumor area')
plt.imshow(f.get_image(f.get_data('custom_images').\
    set_index('custom_label').loc['Area','image_id']),origin='upper')
plt.show()
print('Mutually exclusive Margin, Tumor, and Stroma')
plt.imshow(f.get_image(f.get_data('regions').\
    set_index('region_label').loc['Margin','image_id']),origin='upper')
plt.show()
plt.imshow(f.get_image(f.get_data('regions').\
    set_index('region_label').loc['Tumor','image_id']),origin='upper')
plt.show()
plt.imshow(f.get_image(f.get_data('regions').\
    set_index('region_label').loc['Stroma','image_id']),origin='upper')
plt.show()

MEL2_2

hand drawn margin

hand drawn tumor area

Mutually exclusive Margin, Tumor, and Stroma

Read a project with a custom tumor mask (but no margin line)

Here we will use the mask, but not expand or subtract from it.

from pythologist_test_images import TestImages
from pythologist_reader.formats.inform.custom import CellProjectInFormCustomMask
import matplotlib.pyplot as plt
path = TestImages().raw('IrisSpatialFeatures')
cpi = CellProjectInFormCustomMask('test.h5',mode='w')
cpi.read_path(path,
              microns_per_pixel=0.496,
              sample_name_index=-1,
              verbose=True,
              custom_mask_name='Tumor',
              other_mask_name='Not-Tumor')
for f in cpi.frame_iter():
    rs = f.get_data('regions').set_index('region_label')
    for r in rs.index:
        print(r)
        plt.imshow(f.get_image(rs.loc[r]['image_id']),origin='upper')
        plt.show()
    break

MEL2_2

Tumor

Not-Tumor

Quality check samples

Check general status of the CellDataFrame

cdf = cpi.cdf
cdf.db = cpi
cdf.qc(verbose=True).print_results()

prints the following QC metrics to stdout

==========
Check microns per pixel attribute
PASS
Microns per pixel is 0.496
==========
Check storage object is set
PASS
h5 object is set
==========
Is there a 1:1 correspondence between sample_name and sample_id?
PASS
Good concordance.
Issue count: 0/2
==========
Is there a 1:1 correspondence between frame_name and frame_id?
PASS
Good concordance.
Issue count: 0/4
==========
Is there a 1:1 correspondence between project_name and project_id?
PASS
Good concordance.
Issue count: 0/1
==========
Is the same frame name present in multiple samples?
PASS
frame_name's are all in their own samples
Issue count: 0/4
==========
Are the same phenotypes listed and following rules for mutual exclusion?
PASS
phenotype_calls and phenotype_label follows expected rules
==========
Are the same phenotypes included on all images?
PASS
Consistent phenotypes
Issue count: 0/4
==========
Are the same scored names included on all images?
PASS
Consistent scored_names
Issue count: 0/4
==========
Are the same regions represented the same with an image and across images?
PASS
Consistent regions
Issue count: 0/5
==========
Are the same regions listed matching a valid region_label
PASS
regions and region_label follows expected rules
==========
Do we have any region sizes so small they should consider being excluded?
WARNING
[
    "Very small non-zero regions are included in the data['IrisSpatialFeatures', 'MEL2', 'MEL2_7', {'Margin': 495640, 'Tumor': 947369, 'Stroma': 116}]"
]
Issue count: 1/2

View density plots based on cell phenotype frequencies.

The cell phenotypes set prior to calling cartesian are the phenotypes available to plot.

from pythologist_test_images import TestImages
from plotnine import *
proj = TestImages().project('IrisSpatialFeatures')
cdf = TestImages().celldataframe('IrisSpatialFeatures')
cdf.db = proj
cart = cdf.cartesian(verbose=True,step_pixels=50,max_distance_pixels=75)
df,cols,rngtop = cart.rgb_dataframe(red='CD8+',green='SOX10+')
shape = cdf.iloc[0]['frame_shape']
(ggplot(df,aes(x='frame_x',y='frame_y',fill='color_str'))
 + geom_point(shape='h',size=4.5,color='#777777',stroke=0.2)
 + geom_vline(xintercept=-1,color="#555555")
 + geom_vline(xintercept=shape[1],color="#555555")
 + geom_hline(yintercept=-1,color="#555555")
 + geom_hline(yintercept=shape[0],color="#555555")
 + facet_wrap('frame_name')
 + scale_fill_manual(cols,guide=False)
 + theme_bw()
 + theme(figure_size=(8,8))
 + theme(aspect_ratio=shape[0]/shape[1])
 + scale_y_reverse()
)

View histograms of pixel intensity and the scoring of binary markers on each image

from pythologist_test_images import TestImages
from plotnine import *
proj = TestImages().project('IrisSpatialFeatures')
cdf = TestImages().celldataframe('IrisSpatialFeatures')
cdf.db = proj
ch = cdf.db.qc().channel_histograms()
sub = ch.loc[(~ch['threshold_value'].isna())&(ch['channel_label']=='PDL1')]
(ggplot(sub,aes(x='bins',y='counts'))
 + geom_bar(stat='identity')
 + facet_wrap('frame_name')
 + geom_vline(aes(xintercept='threshold_value'),color='red')
 + theme_bw()
 + ggtitle('Thresholding of PDL1\ngiven image pixel intensities')
)

The original component images were not available for IrisSpatialFeatures example, so pixel intensities are simulated and don't necessarily match the those which would have been used to set the original threshold values.

View cell-cell contacts

from pythologist_test_images import TestImages
from pythologist_reader.formats.inform.custom import CellProjectInFormCustomMask
from pythologist import SubsetLogic as SL
cpi = TestImages().project('IrisSpatialFeatures')
cdf = cpi.cdf
cdf.db = cpi
sub = cdf.loc[cdf['frame_name']=='MEL2_7'].dropna()
cont = sub.contacts().threshold('CD8+','CD8+/contact').contacts().threshold('SOX10+','SOX10+/contact')
cont = cont.threshold('CD8+','SOX10+/contact',
                      positive_label='CD8+ contact',
                      negative_label='CD8+').\
    threshold('SOX10+','CD8+/contact',
              positive_label='SOX10+ contact',
              negative_label='SOX10+')
schema = [
    {'subset_logic':SL(phenotypes=['OTHER']),
     'edge_color':(50,50,50,255),
     'watershed_steps':0,
     'fill_color':(0,0,0,255)
    },
    {'subset_logic':SL(phenotypes=['SOX10+']),
     'edge_color':(166,206,227,255),
     'watershed_steps':0,
     'fill_color':(0,0,0,0)
    },
    {'subset_logic':SL(phenotypes=['CD8+']),
     'edge_color':(253,191,111,255),
     'watershed_steps':0,
     'fill_color':(0,0,0,0)
    },
    {'subset_logic':SL(phenotypes=['CD8+ contact']),
     'edge_color':(253,191,111,255),
     'watershed_steps':0,
     'fill_color':(255,127,0,255)
    },
    {'subset_logic':SL(phenotypes=['SOX10+ contact']),
     'edge_color':(166,206,227,255),
     'watershed_steps':0,
     'fill_color':(31,120,180,255)
    }
]
sio = cont.segmentation_images().build_segmentation_image(schema,background=(0,0,0,255))
sio.write_to_path('test_edges',overwrite=True)

MEL2_7

Image is zoomed-in and cropped to show the contours better.

Merge CellDataFrames that have the same image segmentations but different scored calls

This happens frequently because current InForm exports only permit two features to be scored per export

merged,fail = cdf1.merge_scores(cdf2,on=['sample_name','frame_name','x','y'])

Show names of the binary 'scored_calls'

cdf.scored_names

['PD1', 'PDL1']

Show phenotypes

cdf.phenotypes

['CD8+', 'OTHER', 'SOX10+']

Show regions

cdf.regions

['Margin', 'Stroma', 'Tumor']

Combine two or more phenotypes into one or rename a phenotype

collapsed = cdf.collapse_phenotypes(['CD8+','OTHER'],'non-Tumor')
collapsed.phenotypes

['SOX10+', 'non-Tumor']

Rename a region

Rename TUMOR to Tumor

renamed = cdf.rename_region('TUMOR','Tumor')

Rename scored phenotypes

renamed = cdf.rename_scored_calls({'PDL1 (Opal 520)':'PDL1'})

Threshold a phenotype

Make CYTOK into CYTOK PDL1+ and CYTOK PDL1-

raw_thresh = raw.threshold('CYTOK','PDL1')

Double threshold

CD68_CD163 = raw.threshold('CD68','CD163').\
    threshold('CD68 CD163+','PDL1').\
    threshold('CD68 CD163-','PDL1')

Get per frame counts

generate counts and fractions of the current phenotypes and export them to a csv

cdf.counts().frame_counts().to_csv('my_frame_counts.csv')

Get per sample counts

generate counts and fractions of the current phenotypes and export them to a csv

cdf.counts().sample_counts().to_csv('my_sample_counts.csv')

Identify cells that are in contact with a phenotype

The follow command creates a new CellDataFrame that has an additional binary feature representative of the contact with 'T cell' phenotype cells.

cdf = cdf.contacts().threshold('T cell')

Identify cells that are within some proximity of a phenotype of interest

The follow command creates a new CellDataFrame that has an additional binary feature representative of being inside or outisde 75 microns of 'T cell' phenotype cells.

cdf = cdf.nearestneighbors().threshold('T cell','T cell/within 75um',distance_um=75)

Create an image of cell-cell contacts between features of interest

Check outputs against IrisSpatialFeatures outputs

To ensure we are generating expected outs we can check against the outputs of IrisSpatialFeatures [github].

• Jupyter Notebook: Test against IrisSpatialFeatures outputs