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4 CellType Proportion Exploration

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"""
Created on Mon May 16 19:00:32 2022
@author: Ajit Johnson Nirmal
SCIMAP tutorial May 2022
"""

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# load packages
import scimap as sm
import scanpy as sc
import pandas as pd
import anndata as ad

Tutorial material

You can download the material for this tutorial from the following link:
The jupyter notebook is available here:

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common_path = "/Users/aj/Dropbox (Partners HealthCare)/conferences/scimap_tutorial/may_2022_tutorial/"
#common_path = "C:/Users/ajn16/Dropbox (Partners HealthCare)/conferences/scimap_tutorial/may_2022_tutorial/"

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# load data
#adata = sm.pp.mcmicro_to_scimap (image_path= str(common_path) + 'exemplar_001/quantification/unmicst-exemplar-001_cell.csv')
#manual_gate = pd.read_csv(str(common_path) + 'manual_gates.csv')
#adata = sm.pp.rescale (adata, gate=manual_gate)
#phenotype = pd.read_csv(str(common_path) + 'phenotype_workflow.csv')
#adata = sm.tl.phenotype_cells (adata, phenotype=phenotype, label="phenotype") 
# add user defined ROI's before proceeding

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# load saved anndata object
adata = ad.read(str(common_path) + 'may2022_tutorial.h5ad')

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adata

AnnData object with n_obs × n_vars = 11170 × 9
    obs: 'X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation', 'imageid', 'phenotype', 'index_info', 'ROI', 'ROI_individual'
    uns: 'all_markers', 'dendrogram_phenotype'

Investigate cell-type composition within the ROI's

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# https://scimap.xyz/All%20Functions/C.%20Plotting/sm.pl.stacked_barplot/
sm.pl.stacked_barplot (adata,
                       x_axis='ROI_individual',
                       y_axis='phenotype',
                       method='absolute')

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# Plot the number of cells normalized to 100% 
sm.pl.stacked_barplot (adata,
                       x_axis='ROI_individual',
                       y_axis='phenotype',
                       method='percent')

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# specify the elements to be in the plot
x_axis_elements = ['CD57-low-1', 'CD57-low-2', 'CD57-low-3', 'CD57-high-2', 'CD57-high-1', 'CD57-high-3']
y_axis_elements = ['ASMA+ cells', 'Myeloid', 'NK cells', 'Neutrophils', 'Other Immune cells', 'Treg', 'Tumor']

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# replot
sm.pl.stacked_barplot (adata,
                       x_axis='ROI_individual',
                       y_axis='phenotype',
                       method='percent',
                       subset_xaxis=x_axis_elements,
                       subset_yaxis=y_axis_elements)

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# quiet a number of parameters to play around:
sm.pl.stacked_barplot (adata, 
                x_axis='ROI_individual', y_axis='phenotype', 
                subset_xaxis=x_axis_elements, subset_yaxis=y_axis_elements, 
                order_xaxis=None, order_yaxis=None, 
                method='percent', plot_tool='plotly', 
                matplotlib_cmap=None, 
                matplotlib_bbox_to_anchor=(1, 1.02), 
                matplotlib_legend_loc=2, 
                return_data=False)

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Calculate the fold change in cell types between the different ROI's

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adata = sm.tl.foldchange (adata, 
                          from_group=['CD57-low-1', 'CD57-low-2', 'CD57-low-3'], 
                          to_group=None, 
                          imageid='ROI_individual', 
                          phenotype='phenotype',
                          normalize=True, 
                          subset_phenotype=None, 
                          label='foldchange')

/opt/anaconda3/envs/scimap/lib/python3.9/site-packages/scimap/tools/_foldchange.py:102: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/opt/anaconda3/envs/scimap/lib/python3.9/site-packages/scimap/tools/_foldchange.py:103: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/opt/anaconda3/envs/scimap/lib/python3.9/site-packages/scimap/tools/_foldchange.py:104: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/opt/anaconda3/envs/scimap/lib/python3.9/site-packages/scimap/tools/_foldchange.py:109: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/opt/anaconda3/envs/scimap/lib/python3.9/site-packages/scimap/tools/_foldchange.py:110: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy



calculating P values

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adata

AnnData object with n_obs × n_vars = 11170 × 9
    obs: 'X_centroid', 'Y_centroid', 'Area', 'MajorAxisLength', 'MinorAxisLength', 'Eccentricity', 'Solidity', 'Extent', 'Orientation', 'imageid', 'phenotype', 'index_info', 'ROI', 'ROI_individual'
    uns: 'all_markers', 'dendrogram_phenotype', 'foldchange_pval', 'foldchange_fc'

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# Heatmap of foldchnage  
sm.pl.foldchange (adata, label='foldchange', method='heatmap',
                     p_val=0.05, nonsig_color='grey',
                     cmap = 'vlag', log=True, center=0, linecolor='black',linewidths=0.7,
                     vmin=-5, vmax=5, row_cluster=False)

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# Parallel_coordinates plot of the foldchanges
sm.pl.foldchange (adata, label='foldchange', 
                  subset_xaxis = ['ASMA+ cells', 'NK cells', 'Neutrophils', 'Treg', 'Tumor'],
                log=True, method='parallel_coordinates', invert_axis=True,
                parallel_coordinates_color=['black','blue','green','red','#000000'],
                matplotlib_bbox_to_anchor=(1.04,1),
                matplotlib_legend_loc='upper left',
                xticks_rotation=90,
                return_data = False)

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# save adata
adata.write(str(common_path) + 'may2022_tutorial.h5ad')

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