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🎯 CSPOT Helper Functions

Export the results to .csv

Once the CSPOT pipeline has been executed, all the output is stored within the csObject. An efficient way to export the results of csScore, cspotOutput, and the rescaled data is by using a function that saves them as a CSV file. This allows for easy sharing and analysis of the data in other programs.

# import packages
import cspot as cs

# path to files needed for csExport
projectDir = '/Users/aj/Documents/cspotExampleData'
csObject = projectDir + '/CSPOT/csObject/exampleImage_cspotPredict.ome.h5ad'

cs.csExport(csObject,
               projectDir,
               fileName=None,
               raw=False,
               CellID='CellID',
               verbose=True)

Contents of the csObject have been exported to "/Users/aj/Documents/cspotExampleData/CSPOT/csExport"

Same function if the user wants to run it via Command Line Interface 

python csExport.py \
            --csObject /Users/aj/Documents/cspotExampleData/CSPOT/cspotOutput/exampleImage_cspotPredict.ome.h5ad \
            --projectDir /Users/aj/Documents/cspotExampleData

We also provide a helper functions to vizualize the identified postive and negative cells for each marker.

The addPredictions function serves as a link between cspot and scimap package. It's useful for evaluating model performance. The function transforms results stored in anndata.uns to anndata.obs so they can be visualized using the scimap package's sm.pl.image viewer function. This displays positive and negative cells overlaid on the raw image.

The addPredictions function can take in two methods. cspotOutput displays the result of running the cspot function, while csScore shows the raw output produced by the csScore function, which returns a probability score. The midpoint parameter, with a default value of 0.5, can be adjusted to define what is considered a positive result, when method is set to csScore.

# Path to csObject
csObject = projectDir + '/CSPOT/cspotOutput/exampleImage_cspotPredict.ome.h5ad'

adata = cs.addPredictions (csObject, 
                    method='cspotOutput',
                    cspotOutput='cspotOutput',
                    csScore='csScore', 
                    midpoint=0.5)

# check the results
adata.obs.columns

Index(['X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength',
       'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation',
       'CellID', 'imageid', 'p_ECAD', 'p_CD45', 'p_CD4', 'p_CD3D', 'p_CD8A',
       'p_CD45R', 'p_KI67'],
      dtype='object')

As it can be seen the addition of p_CD45, p_CD4, p_CD8A, p_CD45R, p_KI67, p_ECAD, p_CD3D to adata.obs. These columns can be vizualized with scimap.

We recommend creating a new environment to install scimap

Download and install the scimap package. We recommend creating a new conda/python environment

# create new conda env (assuming you have conda installed): executed in the conda command prompt or terminal
conda create --name scimap -y python=3.8
conda activate scimap

Install scimap within the conda environment.

pip install scimap

# install jupyter notebook if you want to simply execute this notebook.
pip install notebook

Once scimap is installed the following function can be used to vizualize the results

# import
import scimap as sm
import anndata as ad

# import the csObject
cwd = '/Users/aj/Desktop/cspotExampleData'
csObject = cwd + '/CSPOT/cspotOutput/exampleImage_cspotPredict.ome.h5ad'
adata = ad.read(csObject)

# Path to the raw image
image_path = '/Users/aj/Documents/cspotExampleData/image/exampleImage.tif'
sm.image_viewer(image_path, adata, overlay='p_CD45')