MorphoMapping - Example Usage#

This file demonstrates how to use the MorphoMapping package. It provides examples of important methods and aims to facilitate the use of our package.

Imports#

[77]:

from morphomapping import MM

Load object#

[78]:

mm_object1= MM()

Convert files#

[79]:

mm_object1.convert_to_CSV(fcs_path='./example/FCS_test_file.FCS', csv_path='./example/CSV_test_file.CSV')

File successfully converted to ./example/CSV_test_file.CSV.

[ ]:

mm_object1.read_CSV('./example/CSV_test_file.CSV',add_index=True, index_name='ID')

[81]:

mm_object1.get_df()

[81]:

	Area_M01	Area_M02	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	Aspect_Ratio_Intensity_Inspire(M07__Ch07)_Ch07	All	PMN	Singlets	Cytoplasts_and_PMN	Nucleated_cells_and_PMN	R1_and_Nucleated_cells_and_PMN	R2_and_PMN	PDL1minus_and_PMN	PDL1plus_and_PMN
ID
0	91.333336	58.000000	54.444443	42.666668	39.222221	68.222221	48.333332	38.333332	79.333336	0.0	...	0.627883	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0
1	125.777779	64.444443	91.888885	44.111111	3.555556	80.555557	82.666664	50.111111	133.444443	0.0	...	0.715426	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
2	93.666664	76.222221	60.555557	21.777779	10.777778	61.444443	34.444443	29.888889	85.888885	0.0	...	0.988767	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3	106.000000	88.555557	70.666664	17.000000	19.111111	72.444443	75.444443	44.000000	111.888885	0.0	...	0.730149	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
4	127.777779	88.777779	105.666664	75.666664	52.777779	122.555557	111.000000	54.555557	139.777771	0.0	...	0.769359	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13914	122.555557	80.444443	98.000000	49.111111	15.444445	99.000000	71.444443	35.333332	131.888885	0.0	...	0.973271	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
13915	118.000000	82.333336	63.888889	55.444443	41.888889	81.444443	94.888885	54.777779	127.111115	0.0	...	0.763991	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
13916	125.000000	107.777779	123.555557	100.000000	0.000000	86.000000	60.888889	39.777779	109.888885	0.0	...	0.743443	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
13917	114.222221	109.222221	112.777779	84.111115	1.000000	88.444443	62.555557	31.222221	112.444443	0.0	...	0.828440	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
13918	116.777779	93.333336	65.666664	15.222222	26.333334	92.444443	76.444443	49.222221	106.222221	0.0	...	0.920377	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0

13919 rows × 340 columns

[82]:

mm_object1.get_features()

[82]:

['Area_M01',
 'Area_M02',
 'Area_M03',
 'Area_M04',
 'Area_M05',
 'Area_M06',
 'Area_M07',
 'Area_M08',
 'Area_M09',
 'Area_M10',
 'Area_M11',
 'Area_M12',
 'Area_MC',
 'Aspect_Ratio_M01',
 'Aspect_Ratio_M02',
 'Aspect_Ratio_M03',
 'Aspect_Ratio_M04',
 'Aspect_Ratio_M05',
 'Aspect_Ratio_M06',
 'Aspect_Ratio_M07',
 'Aspect_Ratio_M08',
 'Aspect_Ratio_M09',
 'Aspect_Ratio_M10',
 'Aspect_Ratio_M11',
 'Aspect_Ratio_M12',
 'Aspect_Ratio_Intensity_M01_Ch01',
 'Aspect_Ratio_Intensity_M02_Ch02',
 'Aspect_Ratio_Intensity_M03_Ch03',
 'Aspect_Ratio_Intensity_M04_Ch04',
 'Aspect_Ratio_Intensity_M05_Ch05',
 'Aspect_Ratio_Intensity_M06_Ch06',
 'Aspect_Ratio_Intensity_M07_Ch07',
 'Aspect_Ratio_Intensity_M08_Ch08',
 'Aspect_Ratio_Intensity_M09_Ch09',
 'Aspect_Ratio_Intensity_M10_Ch10',
 'Aspect_Ratio_Intensity_M11_Ch11',
 'Aspect_Ratio_Intensity_M12_Ch12',
 'Bkgd_Mean_Ch01',
 'Bkgd_Mean_Ch02',
 'Bkgd_Mean_Ch03',
 'Bkgd_Mean_Ch04',
 'Bkgd_Mean_Ch05',
 'Bkgd_Mean_Ch06',
 'Bkgd_Mean_Ch07',
 'Bkgd_Mean_Ch08',
 'Bkgd_Mean_Ch09',
 'Bkgd_Mean_Ch10',
 'Bkgd_Mean_Ch11',
 'Bkgd_Mean_Ch12',
 'Modulation_M01_Ch01',
 'Modulation_M02_Ch02',
 'Modulation_M03_Ch03',
 'Modulation_M04_Ch04',
 'Modulation_M05_Ch05',
 'Modulation_M06_Ch06',
 'Modulation_M07_Ch07',
 'Modulation_M08_Ch08',
 'Modulation_M09_Ch09',
 'Modulation_M10_Ch10',
 'Modulation_M11_Ch11',
 'Modulation_M12_Ch12',
 'Contrast_M01_Ch01',
 'Contrast_M02_Ch02',
 'Contrast_M03_Ch03',
 'Contrast_M04_Ch04',
 'Contrast_M05_Ch05',
 'Contrast_M06_Ch06',
 'Contrast_M07_Ch07',
 'Contrast_M08_Ch08',
 'Contrast_M09_Ch09',
 'Contrast_M10_Ch10',
 'Contrast_M11_Ch11',
 'Contrast_M12_Ch12',
 'Gradient_RMS_M01_Ch01',
 'Gradient_RMS_M02_Ch02',
 'Gradient_RMS_M03_Ch03',
 'Gradient_RMS_M04_Ch04',
 'Gradient_RMS_M05_Ch05',
 'Gradient_RMS_M06_Ch06',
 'Gradient_RMS_M07_Ch07',
 'Gradient_RMS_M08_Ch08',
 'Gradient_RMS_M09_Ch09',
 'Gradient_RMS_M10_Ch10',
 'Gradient_RMS_M11_Ch11',
 'Gradient_RMS_M12_Ch12',
 'Intensity_MC_Ch01',
 'Intensity_MC_Ch02',
 'Intensity_MC_Ch03',
 'Intensity_MC_Ch04',
 'Intensity_MC_Ch05',
 'Intensity_MC_Ch06',
 'Intensity_MC_Ch07',
 'Intensity_MC_Ch08',
 'Intensity_MC_Ch09',
 'Intensity_MC_Ch10',
 'Intensity_MC_Ch11',
 'Intensity_MC_Ch12',
 'Mean_Pixel_M01_Ch01',
 'Mean_Pixel_M02_Ch02',
 'Mean_Pixel_M03_Ch03',
 'Mean_Pixel_M04_Ch04',
 'Mean_Pixel_M05_Ch05',
 'Mean_Pixel_M06_Ch06',
 'Mean_Pixel_M07_Ch07',
 'Mean_Pixel_M08_Ch08',
 'Mean_Pixel_M09_Ch09',
 'Mean_Pixel_M10_Ch10',
 'Mean_Pixel_M11_Ch11',
 'Mean_Pixel_M12_Ch12',
 'Median_Pixel_M01_Ch01',
 'Median_Pixel_M02_Ch02',
 'Median_Pixel_M03_Ch03',
 'Median_Pixel_M04_Ch04',
 'Median_Pixel_M05_Ch05',
 'Median_Pixel_M06_Ch06',
 'Median_Pixel_M07_Ch07',
 'Median_Pixel_M08_Ch08',
 'Median_Pixel_M09_Ch09',
 'Median_Pixel_M10_Ch10',
 'Median_Pixel_M11_Ch11',
 'Median_Pixel_M12_Ch12',
 'Max_Pixel_MC_Ch01',
 'Max_Pixel_MC_Ch02',
 'Max_Pixel_MC_Ch03',
 'Max_Pixel_MC_Ch04',
 'Max_Pixel_MC_Ch05',
 'Max_Pixel_MC_Ch06',
 'Max_Pixel_MC_Ch07',
 'Max_Pixel_MC_Ch08',
 'Max_Pixel_MC_Ch09',
 'Max_Pixel_MC_Ch10',
 'Max_Pixel_MC_Ch11',
 'Max_Pixel_MC_Ch12',
 'Raw_Max_Pixel_MC_Ch01',
 'Raw_Max_Pixel_MC_Ch02',
 'Raw_Max_Pixel_MC_Ch03',
 'Raw_Max_Pixel_MC_Ch04',
 'Raw_Max_Pixel_MC_Ch05',
 'Raw_Max_Pixel_MC_Ch06',
 'Raw_Max_Pixel_MC_Ch07',
 'Raw_Max_Pixel_MC_Ch08',
 'Raw_Max_Pixel_MC_Ch09',
 'Raw_Max_Pixel_MC_Ch10',
 'Raw_Max_Pixel_MC_Ch11',
 'Raw_Max_Pixel_MC_Ch12',
 'Raw_Min_Pixel_MC_Ch01',
 'Raw_Min_Pixel_MC_Ch02',
 'Raw_Min_Pixel_MC_Ch03',
 'Raw_Min_Pixel_MC_Ch04',
 'Raw_Min_Pixel_MC_Ch05',
 'Raw_Min_Pixel_MC_Ch06',
 'Raw_Min_Pixel_MC_Ch07',
 'Raw_Min_Pixel_MC_Ch08',
 'Raw_Min_Pixel_MC_Ch09',
 'Raw_Min_Pixel_MC_Ch10',
 'Raw_Min_Pixel_MC_Ch11',
 'Raw_Min_Pixel_MC_Ch12',
 'Length_M01',
 'Length_M02',
 'Length_M03',
 'Length_M04',
 'Length_M05',
 'Length_M06',
 'Length_M07',
 'Length_M08',
 'Length_M09',
 'Length_M10',
 'Length_M11',
 'Length_M12',
 'Width_M01',
 'Width_M02',
 'Width_M03',
 'Width_M04',
 'Width_M05',
 'Width_M06',
 'Width_M07',
 'Width_M08',
 'Width_M09',
 'Width_M10',
 'Width_M11',
 'Width_M12',
 'Height_M01',
 'Height_M02',
 'Height_M03',
 'Height_M04',
 'Height_M05',
 'Height_M06',
 'Height_M07',
 'Height_M08',
 'Height_M09',
 'Height_M10',
 'Height_M11',
 'Height_M12',
 'Saturation_Count_M01_Ch01',
 'Saturation_Count_M02_Ch02',
 'Saturation_Count_M03_Ch03',
 'Saturation_Count_M04_Ch04',
 'Saturation_Count_M05_Ch05',
 'Saturation_Count_M06_Ch06',
 'Saturation_Count_M07_Ch07',
 'Saturation_Count_M08_Ch08',
 'Saturation_Count_M09_Ch09',
 'Saturation_Count_M10_Ch10',
 'Saturation_Count_M11_Ch11',
 'Saturation_Count_M12_Ch12',
 'Saturation_Percent_M01_Ch01',
 'Saturation_Percent_M02_Ch02',
 'Saturation_Percent_M03_Ch03',
 'Saturation_Percent_M04_Ch04',
 'Saturation_Percent_M05_Ch05',
 'Saturation_Percent_M06_Ch06',
 'Saturation_Percent_M07_Ch07',
 'Saturation_Percent_M08_Ch08',
 'Saturation_Percent_M09_Ch09',
 'Saturation_Percent_M10_Ch10',
 'Saturation_Percent_M11_Ch11',
 'Saturation_Percent_M12_Ch12',
 'Bright_Detail_Intensity_R3_MC_Ch01',
 'Bright_Detail_Intensity_R3_MC_Ch02',
 'Bright_Detail_Intensity_R3_MC_Ch03',
 'Bright_Detail_Intensity_R3_MC_Ch04',
 'Bright_Detail_Intensity_R3_MC_Ch05',
 'Bright_Detail_Intensity_R3_MC_Ch06',
 'Bright_Detail_Intensity_R3_MC_Ch07',
 'Bright_Detail_Intensity_R3_MC_Ch08',
 'Bright_Detail_Intensity_R3_MC_Ch09',
 'Bright_Detail_Intensity_R3_MC_Ch10',
 'Bright_Detail_Intensity_R3_MC_Ch11',
 'Bright_Detail_Intensity_R3_MC_Ch12',
 'Time',
 'Object_Number',
 'Raw_Centroid_X',
 'Raw_Centroid_Y',
 'Flow_Speed',
 'Intensity_Surface_Ch03',
 'Intensity_Surface_Ch04',
 'Intensity_Surface_Ch05',
 'Intensity_Surface_Ch07',
 'Intensity_Surface_Ch11',
 'Circularity_Morphology(M07__Ch07)',
 'Delta_Centroid_XY',
 'Bright_Detail_Intensity_R7_M07_Ch07',
 'Area_Morphology(M07__Ch07)',
 'Bright_Detail_Similarity_R3_MC_Ch03_Ch04',
 'Bright_Detail_Colocalization_3_MC_Ch03_Ch04_Ch06',
 'Bright_Detail_Similarity_R3_MC_Ch03_Ch06',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch06',
 'CDD6_on_surface_(%)',
 'Bright_Detail_Similarity_R3_MC_Ch03_Ch05',
 'Bright_Detail_Similarity_R3_M01_Ch04_Ch05',
 'Bright_Detail_Colocalization_3_MC_Ch03_Ch04_Ch05',
 'Internalization_Intracellular_Ch03',
 'Internalization_Intracellular_Ch04',
 'Internalization_Intracellular_Ch02',
 'Internalization_Intracellular_Ch05',
 'Internalization_Intracellular_Ch07',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch05',
 'Circularity_M01',
 'Diameter_M01',
 'Perimeter_M01',
 'Symmetry_3_M01_Ch07',
 'Circularity_Morphology(M07__Ch07)_/_Intensity_Surface_Ch03',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch11',
 'Internalization_Intracellular_Ch11',
 'Max_Pixel_Surface_Ch03',
 'Max_Pixel_Surface_Ch04',
 'Max_Pixel_Surface_Ch11',
 'Max_Pixel_Intracellular_Ch04',
 'Max_Pixel_Surface_Ch04_minus_Max_Pixel_Intracellular_Ch04',
 'Max_Pixel_Surface_Ch04_/_Max_Pixel_Intracellular_Ch04',
 'Intensity_Surface_Ch04_/_Intensity_MC_Ch04',
 'Intensity_Surface_Ch11_/_Intensity_MC_Ch11',
 'Intensity_MC_Ch11_minus_Bkgd_Mean_Ch11',
 'Bright_Detail_Intensity_R3_Surface_Ch11',
 'Max_Pixel_Surface_Ch03_/_Max_Pixel_MC_Ch03',
 'Area_Threshold(M07__Ch07__40)',
 'Circularity_Threshold(M07__Ch07__40)',
 'Compactness_Threshold(M07__Ch07__40)_Ch07',
 'Diameter_Threshold(M07__Ch07__40)',
 'Elongatedness_Threshold(M07__Ch07__40)',
 'Symmetry_2_Threshold(M07__Ch07__40)_Ch07',
 'Symmetry_3_Threshold(M07__Ch07__40)_Ch07',
 'Area_Morphology(M01__Ch01)',
 'Ratio_cell/nucleus',
 'Intensity_Cortical_Ch02',
 'Intensity_nuclear_actin',
 'Lobe_Count_Morphology(M07__Ch07)_Ch07',
 'Major_Axis_MC',
 'Cytoplasmic_size',
 'Similarity_MC_Ch02_Ch07',
 'Circularity_MC',
 'Circularity_AdaptiveErode(M01__Ch01__95)',
 'Delta_Centroid_XY_MC_Ch01_IntensityWeighted_MC_Ch01',
 'Major_Axis_M02',
 'Mean_Pixel_Morphology(M07__Ch07)_Ch07',
 'Area_M01_/_Bright_Detail_Intensity_R7_M07_Ch07',
 'Delta_Centroid_XY_MC_Ch07_IntensityWeighted_MC_Ch07',
 'Contrast_Surface_Ch01',
 'Spot_Distance_Min_Threshold(M07__Ch07__40)',
 'Spot_Area_Min_Threshold(M07__Ch07__40)',
 'Intensity_Cortical_F_actin',
 'Intensity_Nuclear_f_actin',
 'Cortical_F_actin_(%)',
 'Nuclear_F_actin',
 'Delta_Centroid_XY_MC_Ch01_M02_Ch02_IntensityWeighted',
 'Area_Inspire(M07__Ch07)',
 'Area_System(M07__Ch07__39)',
 'Shape_Ratio_M01',
 'Area_Component(1__Area__Peak(M07__Ch07__Bright__0)__Descending)',
 'Area_Inspire(M01__Ch01)',
 'cytoplasmic_size_Insp',
 'Area_Inspire(M01__Ch01)_minus_Area_Inspire(M07__Ch07)',
 'Area_Cytoplasm',
 'Insp_cytoplasm_index',
 'Circularity_Inspire(M07__Ch07)',
 'Symmetry_2_Inspire(M01__Ch01)_Ch01',
 'Symmetry_3_Inspire(M01__Ch01)_Ch01',
 'Lobe_Count_Inspire(M01__Ch01)_Ch01',
 'Spot_Count_Inspire(M01__Ch01)_8',
 'Delta_Centroid_XY_Intesity_Weighted_Actin',
 'H_Variance_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Compactness_Inspire(M01__Ch01)_Ch02',
 'Ensquared_Energy_Ch02_4',
 'H_Entropy_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Shape_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Intensity_Inspire(M07__Ch07)_Ch02',
 'Aspect_Ratio_Inspire(M01__Ch01)',
 'Aspect_Ratio_Intensity_Inspire(M01__Ch01)_Ch02',
 'Shape_Ratio_Inspire(M02__Ch02)',
 'Aspect_Ratio_Intensity_Inspire(M07__Ch07)_Ch07',
 'All',
 'PMN',
 'Singlets',
 'Cytoplasts_and_PMN',
 'Nucleated_cells_and_PMN',
 'R1_and_Nucleated_cells_and_PMN',
 'R2_and_PMN',
 'PDL1minus_and_PMN',
 'PDL1plus_and_PMN']

Structure dataset#

Modify the data by adding new columns, selecting rows, and performing additional transformations.

[83]:

mm_object1.add_metadata('donor','ABC')
mm_object1.add_metadata('file_number',1)
mm_object1.select_condition('PDL1plus_and_PMN',1)
mm_object1.select_events(1500)
mm_object1.weighted_features(['Area_M01','Area_M02'],2)

[83]:

	Area_M01	Area_M02	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	PMN	Singlets	Cytoplasts_and_PMN	Nucleated_cells_and_PMN	R1_and_Nucleated_cells_and_PMN	R2_and_PMN	PDL1minus_and_PMN	PDL1plus_and_PMN	donor	file_number
ID
6	263.333344	204.000000	104.333336	82.666664	7.222222	82.222221	59.444443	38.888889	121.666664	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
7	305.333344	212.888885	152.333328	107.000000	0.000000	104.111115	78.333336	42.000000	151.666672	0.000000	...	1.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	ABC	1
30	221.111115	165.111115	56.222221	44.666668	41.444443	83.222221	71.444443	51.111111	121.666664	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
36	275.777771	208.888885	109.555557	23.777779	15.555555	115.222221	48.777779	36.444443	140.000000	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
41	251.111115	148.222229	77.000000	63.111111	0.000000	116.222221	72.222221	44.666668	134.444443	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13859	162.222229	101.333336	74.888885	50.666668	3.777778	71.222221	37.333332	34.777779	88.888885	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
13860	262.888885	192.666672	86.000000	47.444443	36.777779	76.666664	92.666664	53.222221	147.000000	0.000000	...	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	ABC	1
13868	214.666672	152.000000	77.444443	60.777779	49.888889	98.888885	52.666668	63.111111	116.444443	22.666666	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
13887	216.888885	159.555557	48.888889	24.444445	21.777779	85.777779	50.333332	43.444443	100.555557	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
13917	228.444443	218.444443	112.777779	84.111115	1.000000	88.444443	62.555557	31.222221	112.444443	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1

1500 rows × 342 columns

Load second object#

[ ]:

mm_object2 = MM()
mm_object2.read_CSV('./example/CSV_test_file.CSV',add_index=True, index_name='ID')
mm_object2.add_metadata('donor','XYZ')
mm_object2.add_metadata('file_number',2)
mm_object2.select_condition('PDL1plus_and_PMN',1)
mm_object2.select_events(1500)

Read all CSV files in a given folder#

[ ]:

mm_object3 = MM()
mm_object3.read_files('./example',add_index=True,join='inner')
mm_object3.get_df()

Concatenate object 1 and object 2#

[86]:

df = mm_object2.get_df()
mm_object1.concat_df(df)
mm_object1.get_df()

[86]:

	Area_M01	Area_M02	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	PMN	Singlets	Cytoplasts_and_PMN	Nucleated_cells_and_PMN	R1_and_Nucleated_cells_and_PMN	R2_and_PMN	PDL1minus_and_PMN	PDL1plus_and_PMN	donor	file_number
0	263.333344	204.000000	104.333336	82.666664	7.222222	82.222221	59.444443	38.888889	121.666664	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
1	305.333344	212.888885	152.333328	107.000000	0.000000	104.111115	78.333336	42.000000	151.666672	0.000000	...	1.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	ABC	1
2	221.111115	165.111115	56.222221	44.666668	41.444443	83.222221	71.444443	51.111111	121.666664	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
3	275.777771	208.888885	109.555557	23.777779	15.555555	115.222221	48.777779	36.444443	140.000000	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
4	251.111115	148.222229	77.000000	63.111111	0.000000	116.222221	72.222221	44.666668	134.444443	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	ABC	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2995	81.111115	50.666668	74.888885	50.666668	3.777778	71.222221	37.333332	34.777779	88.888885	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	XYZ	2
2996	131.444443	96.333336	86.000000	47.444443	36.777779	76.666664	92.666664	53.222221	147.000000	0.000000	...	1.0	0.0	1.0	0.0	0.0	1.0	0.0	1.0	XYZ	2
2997	107.333336	76.000000	77.444443	60.777779	49.888889	98.888885	52.666668	63.111111	116.444443	22.666666	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	XYZ	2
2998	108.444443	79.777779	48.888889	24.444445	21.777779	85.777779	50.333332	43.444443	100.555557	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	XYZ	2
2999	114.222221	109.222221	112.777779	84.111115	1.000000	88.444443	62.555557	31.222221	112.444443	0.000000	...	1.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	XYZ	2

3000 rows × 342 columns

Structure dataset#

Remove unnecessary columns or features to simplify the dataset

[87]:

d_features=[
             'Bkgd_Mean_Ch01',
             'Bkgd_Mean_Ch02',
             'Bkgd_Mean_Ch03',
             'Bkgd_Mean_Ch04',
             'Bkgd_Mean_Ch05',
             'Bkgd_Mean_Ch06',
             'Bkgd_Mean_Ch07',
             'Bkgd_Mean_Ch08',
             'Bkgd_Mean_Ch09',
             'Bkgd_Mean_Ch10',
             'Bkgd_Mean_Ch11',
             'Bkgd_Mean_Ch12',
             'Modulation_M01_Ch01',
             'Modulation_M02_Ch02',
             'Modulation_M03_Ch03',
             'Modulation_M04_Ch04',
             'Modulation_M05_Ch05',
             'Modulation_M06_Ch06',
             'Modulation_M07_Ch07',
             'Modulation_M08_Ch08',
             'Modulation_M09_Ch09',
             'Modulation_M10_Ch10',
             'Modulation_M11_Ch11',
             'Modulation_M12_Ch12',
             'Contrast_M01_Ch01',
             'Contrast_M02_Ch02',
             'Contrast_M03_Ch03',
             'Contrast_M04_Ch04',
             'Contrast_M05_Ch05',
             'Contrast_M06_Ch06',
             'Contrast_M07_Ch07',
             'Contrast_M08_Ch08',
             'Contrast_M09_Ch09',
             'Contrast_M10_Ch10',
             'Contrast_M11_Ch11',
             'Contrast_M12_Ch12',
             'Gradient_RMS_M01_Ch01',
             'Gradient_RMS_M02_Ch02',
             'Gradient_RMS_M03_Ch03',
             'Gradient_RMS_M04_Ch04',
             'Gradient_RMS_M05_Ch05',
             'Gradient_RMS_M06_Ch06',
             'Gradient_RMS_M07_Ch07',
             'Gradient_RMS_M08_Ch08',
             'Gradient_RMS_M09_Ch09',
             'Gradient_RMS_M10_Ch10',
             'Gradient_RMS_M11_Ch11',
             'Gradient_RMS_M12_Ch12',
             'Intensity_MC_Ch01',
             'Intensity_MC_Ch02',
             'Intensity_MC_Ch03',
             'Intensity_MC_Ch04',
             'Intensity_MC_Ch05',
             'Intensity_MC_Ch06',
             'Intensity_MC_Ch07',
             'Intensity_MC_Ch08',
             'Intensity_MC_Ch09',
             'Intensity_MC_Ch10',
             'Intensity_MC_Ch11',
             'Intensity_MC_Ch12',
             'Mean_Pixel_M01_Ch01',
             'Mean_Pixel_M02_Ch02',
             'Mean_Pixel_M03_Ch03',
             'Mean_Pixel_M04_Ch04',
             'Mean_Pixel_M05_Ch05',
             'Mean_Pixel_M06_Ch06',
             'Mean_Pixel_M07_Ch07',
             'Mean_Pixel_M08_Ch08',
             'Mean_Pixel_M09_Ch09',
             'Mean_Pixel_M10_Ch10',
             'Mean_Pixel_M11_Ch11',
             'Mean_Pixel_M12_Ch12',
             'Median_Pixel_M01_Ch01',
             'Median_Pixel_M02_Ch02',
             'Median_Pixel_M03_Ch03',
             'Median_Pixel_M04_Ch04',
             'Median_Pixel_M05_Ch05',
             'Median_Pixel_M06_Ch06',
             'Median_Pixel_M07_Ch07',
             'Median_Pixel_M08_Ch08',
             'Median_Pixel_M09_Ch09',
             'Median_Pixel_M10_Ch10',
             'Median_Pixel_M11_Ch11',
             'Median_Pixel_M12_Ch12',
             'Max_Pixel_MC_Ch01',
             'Max_Pixel_MC_Ch02',
             'Max_Pixel_MC_Ch03',
             'Max_Pixel_MC_Ch04',
             'Max_Pixel_MC_Ch05',
             'Max_Pixel_MC_Ch06',
             'Max_Pixel_MC_Ch07',
             'Max_Pixel_MC_Ch08',
             'Max_Pixel_MC_Ch09',
             'Max_Pixel_MC_Ch10',
             'Max_Pixel_MC_Ch11',
             'Max_Pixel_MC_Ch12',
             'Raw_Max_Pixel_MC_Ch01',
             'Raw_Max_Pixel_MC_Ch02',
             'Raw_Max_Pixel_MC_Ch03',
             'Raw_Max_Pixel_MC_Ch04',
             'Raw_Max_Pixel_MC_Ch05',
             'Raw_Max_Pixel_MC_Ch06',
             'Raw_Max_Pixel_MC_Ch07',
             'Raw_Max_Pixel_MC_Ch08',
             'Raw_Max_Pixel_MC_Ch09',
             'Raw_Max_Pixel_MC_Ch10',
             'Raw_Max_Pixel_MC_Ch11',
             'Raw_Max_Pixel_MC_Ch12',
             'Raw_Min_Pixel_MC_Ch01',
             'Raw_Min_Pixel_MC_Ch02',
             'Raw_Min_Pixel_MC_Ch03',
             'Raw_Min_Pixel_MC_Ch04',
             'Raw_Min_Pixel_MC_Ch05',
             'Raw_Min_Pixel_MC_Ch06',
             'Raw_Min_Pixel_MC_Ch07',
             'Raw_Min_Pixel_MC_Ch08',
             'Raw_Min_Pixel_MC_Ch09',
             'Raw_Min_Pixel_MC_Ch10',
             'Raw_Min_Pixel_MC_Ch11',
             'Raw_Min_Pixel_MC_Ch12',
             'Aspect_Ratio_Intensity_M01_Ch01',
             'Aspect_Ratio_Intensity_M02_Ch02',
             'Aspect_Ratio_Intensity_M03_Ch03',
             'Aspect_Ratio_Intensity_M04_Ch04',
             'Aspect_Ratio_Intensity_M05_Ch05',
             'Aspect_Ratio_Intensity_M06_Ch06',
             'Aspect_Ratio_Intensity_M07_Ch07',
             'Aspect_Ratio_Intensity_M08_Ch08',
             'Aspect_Ratio_Intensity_M09_Ch09',
             'Aspect_Ratio_Intensity_M10_Ch10',
             'Aspect_Ratio_Intensity_M11_Ch11',
             'Aspect_Ratio_Intensity_M12_Ch12',
             'Saturation_Count_M01_Ch01',
             'Saturation_Count_M02_Ch02',
             'Saturation_Count_M03_Ch03',
             'Saturation_Count_M04_Ch04',
             'Saturation_Count_M05_Ch05',
             'Saturation_Count_M06_Ch06',
             'Saturation_Count_M07_Ch07',
             'Saturation_Count_M08_Ch08',
             'Saturation_Count_M09_Ch09',
             'Saturation_Count_M10_Ch10',
             'Saturation_Count_M11_Ch11',
             'Saturation_Count_M12_Ch12',
             'Saturation_Percent_M01_Ch01',
             'Saturation_Percent_M02_Ch02',
             'Saturation_Percent_M03_Ch03',
             'Saturation_Percent_M04_Ch04',
             'Saturation_Percent_M05_Ch05',
             'Saturation_Percent_M06_Ch06',
             'Saturation_Percent_M07_Ch07',
             'Saturation_Percent_M08_Ch08',
             'Saturation_Percent_M09_Ch09',
             'Saturation_Percent_M10_Ch10',
             'Saturation_Percent_M11_Ch11',
             'Saturation_Percent_M12_Ch12',
             'Bright_Detail_Intensity_R3_MC_Ch01',
             'Bright_Detail_Intensity_R3_MC_Ch02',
             'Bright_Detail_Intensity_R3_MC_Ch03',
             'Bright_Detail_Intensity_R3_MC_Ch04',
             'Bright_Detail_Intensity_R3_MC_Ch05',
             'Bright_Detail_Intensity_R3_MC_Ch06',
             'Bright_Detail_Intensity_R3_MC_Ch07',
             'Bright_Detail_Intensity_R3_MC_Ch08',
             'Bright_Detail_Intensity_R3_MC_Ch09',
             'Bright_Detail_Intensity_R3_MC_Ch10',
             'Bright_Detail_Intensity_R3_MC_Ch11',
             'Bright_Detail_Intensity_R3_MC_Ch12',
             'Time',
             'Object_Number',
             'Raw_Centroid_X',
             'Raw_Centroid_Y',
             'Flow_Speed',
             'Intensity_Surface_Ch03',
             'Intensity_Surface_Ch04',
             'Intensity_Surface_Ch05',
             'Intensity_Surface_Ch07',
             'Intensity_Surface_Ch11',
             'Bright_Detail_Intensity_R7_M07_Ch07',
             'Bright_Detail_Similarity_R3_MC_Ch03_Ch04',
              'Bright_Detail_Colocalization_3_MC_Ch03_Ch04_Ch06',
             'Bright_Detail_Similarity_R3_MC_Ch03_Ch06',
             'Bright_Detail_Similarity_R3_MC_Ch04_Ch06',
             'Max_Pixel_Surface_Ch03',
             'Max_Pixel_Surface_Ch04',
             'Max_Pixel_Surface_Ch11',
             'Max_Pixel_Intracellular_Ch04',
             'Max_Pixel_Surface_Ch04_minus_Max_Pixel_Intracellular_Ch04',
             'Max_Pixel_Surface_Ch04_/_Max_Pixel_Intracellular_Ch04',
             'Intensity_Surface_Ch04_/_Intensity_MC_Ch04',
             'Intensity_Surface_Ch11_/_Intensity_MC_Ch11',
             'Intensity_MC_Ch11_minus_Bkgd_Mean_Ch11',
             'Bright_Detail_Intensity_R3_Surface_Ch11',
             'Max_Pixel_Surface_Ch03_/_Max_Pixel_MC_Ch03',
             'Intensity_Cortical_Ch02',
             'Intensity_nuclear_actin',
             'Delta_Centroid_XY_MC_Ch01_IntensityWeighted_MC_Ch01',
             'Contrast_Surface_Ch01',
             'Intensity_Cortical_F_actin',
             'Intensity_Nuclear_f_actin',
             'Delta_Centroid_XY_MC_Ch01_M02_Ch02_IntensityWeighted',
             'Mean_Pixel_Morphology(M07__Ch07)_Ch07',
             'Area_M01_/_Bright_Detail_Intensity_R7_M07_Ch07',
             'Delta_Centroid_XY_MC_Ch07_IntensityWeighted_MC_Ch07',
             'Spot_Distance_Min_Threshold(M07__Ch07__40)',
             'Aspect_Ratio_Intensity_Inspire(M07__Ch07)_Ch02',
             'Aspect_Ratio_Intensity_Inspire(M01__Ch01)_Ch02',
             'Aspect_Ratio_Intensity_Inspire(M07__Ch07)_Ch07',
             'Circularity_Morphology(M07__Ch07)_/_Intensity_Surface_Ch03',
             'Delta_Centroid_XY_Intesity_Weighted_Actin',
             'All',
             'PMN',
             'Singlets',
             'Cytoplasts_and_PMN',
             'Nucleated_cells_and_PMN',
             'R1_and_Nucleated_cells_and_PMN',
             'R2_and_PMN',
             'PDL1minus_and_PMN',
             'PDL1plus_and_PMN',
    ]

[88]:

mm_object1.drop_variables(*d_features)
mm_object1.get_features()

[88]:

['Area_M01',
 'Area_M02',
 'Area_M03',
 'Area_M04',
 'Area_M05',
 'Area_M06',
 'Area_M07',
 'Area_M08',
 'Area_M09',
 'Area_M10',
 'Area_M11',
 'Area_M12',
 'Area_MC',
 'Aspect_Ratio_M01',
 'Aspect_Ratio_M02',
 'Aspect_Ratio_M03',
 'Aspect_Ratio_M04',
 'Aspect_Ratio_M05',
 'Aspect_Ratio_M06',
 'Aspect_Ratio_M07',
 'Aspect_Ratio_M08',
 'Aspect_Ratio_M09',
 'Aspect_Ratio_M10',
 'Aspect_Ratio_M11',
 'Aspect_Ratio_M12',
 'Length_M01',
 'Length_M02',
 'Length_M03',
 'Length_M04',
 'Length_M05',
 'Length_M06',
 'Length_M07',
 'Length_M08',
 'Length_M09',
 'Length_M10',
 'Length_M11',
 'Length_M12',
 'Width_M01',
 'Width_M02',
 'Width_M03',
 'Width_M04',
 'Width_M05',
 'Width_M06',
 'Width_M07',
 'Width_M08',
 'Width_M09',
 'Width_M10',
 'Width_M11',
 'Width_M12',
 'Height_M01',
 'Height_M02',
 'Height_M03',
 'Height_M04',
 'Height_M05',
 'Height_M06',
 'Height_M07',
 'Height_M08',
 'Height_M09',
 'Height_M10',
 'Height_M11',
 'Height_M12',
 'Circularity_Morphology(M07__Ch07)',
 'Delta_Centroid_XY',
 'Area_Morphology(M07__Ch07)',
 'CDD6_on_surface_(%)',
 'Bright_Detail_Similarity_R3_MC_Ch03_Ch05',
 'Bright_Detail_Similarity_R3_M01_Ch04_Ch05',
 'Bright_Detail_Colocalization_3_MC_Ch03_Ch04_Ch05',
 'Internalization_Intracellular_Ch03',
 'Internalization_Intracellular_Ch04',
 'Internalization_Intracellular_Ch02',
 'Internalization_Intracellular_Ch05',
 'Internalization_Intracellular_Ch07',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch05',
 'Circularity_M01',
 'Diameter_M01',
 'Perimeter_M01',
 'Symmetry_3_M01_Ch07',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch11',
 'Internalization_Intracellular_Ch11',
 'Area_Threshold(M07__Ch07__40)',
 'Circularity_Threshold(M07__Ch07__40)',
 'Compactness_Threshold(M07__Ch07__40)_Ch07',
 'Diameter_Threshold(M07__Ch07__40)',
 'Elongatedness_Threshold(M07__Ch07__40)',
 'Symmetry_2_Threshold(M07__Ch07__40)_Ch07',
 'Symmetry_3_Threshold(M07__Ch07__40)_Ch07',
 'Area_Morphology(M01__Ch01)',
 'Ratio_cell/nucleus',
 'Lobe_Count_Morphology(M07__Ch07)_Ch07',
 'Major_Axis_MC',
 'Cytoplasmic_size',
 'Similarity_MC_Ch02_Ch07',
 'Circularity_MC',
 'Circularity_AdaptiveErode(M01__Ch01__95)',
 'Major_Axis_M02',
 'Spot_Area_Min_Threshold(M07__Ch07__40)',
 'Cortical_F_actin_(%)',
 'Nuclear_F_actin',
 'Area_Inspire(M07__Ch07)',
 'Area_System(M07__Ch07__39)',
 'Shape_Ratio_M01',
 'Area_Component(1__Area__Peak(M07__Ch07__Bright__0)__Descending)',
 'Area_Inspire(M01__Ch01)',
 'cytoplasmic_size_Insp',
 'Area_Inspire(M01__Ch01)_minus_Area_Inspire(M07__Ch07)',
 'Area_Cytoplasm',
 'Insp_cytoplasm_index',
 'Circularity_Inspire(M07__Ch07)',
 'Symmetry_2_Inspire(M01__Ch01)_Ch01',
 'Symmetry_3_Inspire(M01__Ch01)_Ch01',
 'Lobe_Count_Inspire(M01__Ch01)_Ch01',
 'Spot_Count_Inspire(M01__Ch01)_8',
 'H_Variance_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Compactness_Inspire(M01__Ch01)_Ch02',
 'Ensquared_Energy_Ch02_4',
 'H_Entropy_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Shape_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Inspire(M01__Ch01)',
 'Shape_Ratio_Inspire(M02__Ch02)',
 'donor',
 'file_number']

Rename columns using new labels

[89]:

label_mapping = {'Area_M01': 'Area F Actin', 'Area_M02': 'Area Nucleus'}
mm_object1.rename_variables(label_mapping)
mm_object1.get_features()

[89]:

['Area F Actin',
 'Area Nucleus',
 'Area_M03',
 'Area_M04',
 'Area_M05',
 'Area_M06',
 'Area_M07',
 'Area_M08',
 'Area_M09',
 'Area_M10',
 'Area_M11',
 'Area_M12',
 'Area_MC',
 'Aspect_Ratio_M01',
 'Aspect_Ratio_M02',
 'Aspect_Ratio_M03',
 'Aspect_Ratio_M04',
 'Aspect_Ratio_M05',
 'Aspect_Ratio_M06',
 'Aspect_Ratio_M07',
 'Aspect_Ratio_M08',
 'Aspect_Ratio_M09',
 'Aspect_Ratio_M10',
 'Aspect_Ratio_M11',
 'Aspect_Ratio_M12',
 'Length_M01',
 'Length_M02',
 'Length_M03',
 'Length_M04',
 'Length_M05',
 'Length_M06',
 'Length_M07',
 'Length_M08',
 'Length_M09',
 'Length_M10',
 'Length_M11',
 'Length_M12',
 'Width_M01',
 'Width_M02',
 'Width_M03',
 'Width_M04',
 'Width_M05',
 'Width_M06',
 'Width_M07',
 'Width_M08',
 'Width_M09',
 'Width_M10',
 'Width_M11',
 'Width_M12',
 'Height_M01',
 'Height_M02',
 'Height_M03',
 'Height_M04',
 'Height_M05',
 'Height_M06',
 'Height_M07',
 'Height_M08',
 'Height_M09',
 'Height_M10',
 'Height_M11',
 'Height_M12',
 'Circularity_Morphology(M07__Ch07)',
 'Delta_Centroid_XY',
 'Area_Morphology(M07__Ch07)',
 'CDD6_on_surface_(%)',
 'Bright_Detail_Similarity_R3_MC_Ch03_Ch05',
 'Bright_Detail_Similarity_R3_M01_Ch04_Ch05',
 'Bright_Detail_Colocalization_3_MC_Ch03_Ch04_Ch05',
 'Internalization_Intracellular_Ch03',
 'Internalization_Intracellular_Ch04',
 'Internalization_Intracellular_Ch02',
 'Internalization_Intracellular_Ch05',
 'Internalization_Intracellular_Ch07',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch05',
 'Circularity_M01',
 'Diameter_M01',
 'Perimeter_M01',
 'Symmetry_3_M01_Ch07',
 'Bright_Detail_Similarity_R3_MC_Ch04_Ch11',
 'Internalization_Intracellular_Ch11',
 'Area_Threshold(M07__Ch07__40)',
 'Circularity_Threshold(M07__Ch07__40)',
 'Compactness_Threshold(M07__Ch07__40)_Ch07',
 'Diameter_Threshold(M07__Ch07__40)',
 'Elongatedness_Threshold(M07__Ch07__40)',
 'Symmetry_2_Threshold(M07__Ch07__40)_Ch07',
 'Symmetry_3_Threshold(M07__Ch07__40)_Ch07',
 'Area_Morphology(M01__Ch01)',
 'Ratio_cell/nucleus',
 'Lobe_Count_Morphology(M07__Ch07)_Ch07',
 'Major_Axis_MC',
 'Cytoplasmic_size',
 'Similarity_MC_Ch02_Ch07',
 'Circularity_MC',
 'Circularity_AdaptiveErode(M01__Ch01__95)',
 'Major_Axis_M02',
 'Spot_Area_Min_Threshold(M07__Ch07__40)',
 'Cortical_F_actin_(%)',
 'Nuclear_F_actin',
 'Area_Inspire(M07__Ch07)',
 'Area_System(M07__Ch07__39)',
 'Shape_Ratio_M01',
 'Area_Component(1__Area__Peak(M07__Ch07__Bright__0)__Descending)',
 'Area_Inspire(M01__Ch01)',
 'cytoplasmic_size_Insp',
 'Area_Inspire(M01__Ch01)_minus_Area_Inspire(M07__Ch07)',
 'Area_Cytoplasm',
 'Insp_cytoplasm_index',
 'Circularity_Inspire(M07__Ch07)',
 'Symmetry_2_Inspire(M01__Ch01)_Ch01',
 'Symmetry_3_Inspire(M01__Ch01)_Ch01',
 'Lobe_Count_Inspire(M01__Ch01)_Ch01',
 'Spot_Count_Inspire(M01__Ch01)_8',
 'H_Variance_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Compactness_Inspire(M01__Ch01)_Ch02',
 'Ensquared_Energy_Ch02_4',
 'H_Entropy_Mean_Inspire(M01__Ch01)_Ch02_3',
 'Shape_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Inspire(M07__Ch07)',
 'Aspect_Ratio_Inspire(M01__Ch01)',
 'Shape_Ratio_Inspire(M02__Ch02)',
 'donor',
 'file_number']

Standardization/ Normalization#

Apply Min-Max normalization to a specific part of the dataset.

[90]:

mm_object1.minmax_norm('Area F Actin','Shape_Ratio_Inspire(M02__Ch02)')
mm_object1.get_df()

[90]:

	Area F Actin	Area Nucleus	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	H_Variance_Mean_Inspire(M01__Ch01)_Ch02_3	Compactness_Inspire(M01__Ch01)_Ch02	Ensquared_Energy_Ch02_4	H_Entropy_Mean_Inspire(M01__Ch01)_Ch02_3	Shape_Ratio_Inspire(M07__Ch07)	Aspect_Ratio_Inspire(M07__Ch07)	Aspect_Ratio_Inspire(M01__Ch01)	Shape_Ratio_Inspire(M02__Ch02)	donor	file_number
0	0.566978	0.502290	0.573610	0.644156	0.087131	0.447148	0.313243	0.377970	0.410785	0.00	...	0.313591	0.761270	0.462252	0.780636	0.620690	0.686934	0.611811	0.722222	ABC	1
1	0.684735	0.526718	0.837508	0.833766	0.000000	0.596958	0.448057	0.408207	0.624901	0.00	...	0.076305	0.535507	0.589105	0.433597	0.512821	0.555405	0.408304	0.325581	ABC	1
2	0.448598	0.395420	0.309102	0.348052	0.500000	0.453992	0.398890	0.496760	0.410785	0.00	...	1.000000	0.767202	0.335122	0.910088	0.896552	0.957763	0.962465	0.896552	ABC	1
3	0.601869	0.515725	0.602321	0.185281	0.187668	0.673004	0.237113	0.354212	0.541634	0.00	...	0.382121	0.757604	0.334155	0.841622	0.869565	0.961271	0.741346	0.266667	ABC	1
4	0.532710	0.349008	0.423335	0.491775	0.000000	0.679848	0.404441	0.434125	0.501983	0.00	...	0.605582	0.605352	0.414423	0.948612	0.687500	0.741091	0.855884	0.000000	ABC	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2995	0.056075	0.080916	0.411729	0.394805	0.045576	0.371863	0.155432	0.338013	0.176844	0.00	...	0.678908	0.678608	0.390732	0.932213	0.857143	0.861827	0.920663	0.266667	XYZ	2
2996	0.197196	0.206412	0.472816	0.369697	0.443700	0.409125	0.550357	0.517279	0.591594	0.00	...	0.391627	0.760775	0.463557	0.851694	0.045455	0.276153	0.186740	0.048780	XYZ	2
2997	0.129595	0.150534	0.425779	0.473593	0.601877	0.561217	0.264869	0.613391	0.373513	0.34	...	0.121971	0.772110	0.509750	0.502878	0.846154	0.863870	0.783250	0.866667	XYZ	2
2998	0.132710	0.160916	0.268784	0.190476	0.262735	0.471483	0.248216	0.422246	0.260111	0.00	...	0.431445	0.818263	0.344421	0.872232	0.833333	0.992552	0.829925	0.666667	XYZ	2
2999	0.148910	0.241832	0.620037	0.655411	0.012064	0.489734	0.335448	0.303456	0.344964	0.00	...	0.454690	0.815170	0.466046	0.909651	0.814815	0.884373	0.758536	0.363636	XYZ	2

3000 rows × 123 columns

Apply Standard Scaler to a specific part of the dataset.

[91]:

mm_object1.stand_scaler('Area F Actin','Shape_Ratio_Inspire(M02__Ch02)')
mm_object1.get_df()

[91]:

	Area F Actin	Area Nucleus	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	H_Variance_Mean_Inspire(M01__Ch01)_Ch02_3	Compactness_Inspire(M01__Ch01)_Ch02	Ensquared_Energy_Ch02_4	H_Entropy_Mean_Inspire(M01__Ch01)_Ch02_3	Shape_Ratio_Inspire(M07__Ch07)	Aspect_Ratio_Inspire(M07__Ch07)	Aspect_Ratio_Inspire(M01__Ch01)	Shape_Ratio_Inspire(M02__Ch02)	donor	file_number
0	1.337076	1.625742	0.897175	1.463912	-0.628185	-0.140131	-0.261273	-0.125493	0.123897	-0.235168	...	-0.973292	0.012054	0.695038	-0.205093	-0.025373	-0.702550	-0.961786	0.549558	ABC	1
1	1.957742	1.799961	2.223853	2.425170	-1.065478	0.965897	0.601929	0.120193	1.367473	-0.235168	...	-2.296819	-1.962752	2.418283	-2.839864	-0.371441	-1.402301	-2.109929	-0.640646	ABC	1
2	0.713126	0.863530	-0.432575	-0.037230	1.443909	-0.089601	0.287114	0.839701	0.123897	-0.235168	...	2.855345	0.063941	-1.031957	0.777724	0.859653	0.738303	1.016538	1.072670	ABC	1
3	1.520976	1.721562	1.041512	-0.862419	-0.123617	1.527333	-0.748728	-0.318532	0.883860	-0.235168	...	-0.591044	-0.020016	-1.045098	0.257919	0.773074	0.756964	-0.230971	-0.817432	ABC	1
4	1.156458	0.532512	0.141705	0.691394	-1.065478	1.577862	0.322658	0.330781	0.653568	-0.235168	...	0.655372	-1.351795	0.045301	1.070207	0.188969	-0.414427	0.415230	-1.617622	ABC	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2995	-1.355761	-1.379551	0.083355	0.199792	-0.836740	-0.695952	-1.271727	-0.450149	-1.234825	-0.235168	...	1.064366	-0.711015	-0.276522	0.945700	0.733221	0.227906	0.780702	-0.817432	XYZ	2
2996	-0.611947	-0.484497	0.390457	0.072502	1.161350	-0.420848	1.256947	1.006416	1.174028	-0.235168	...	-0.538022	0.007720	0.712764	0.334387	-1.870853	-2.887961	-3.359953	-1.471246	XYZ	2
2997	-0.968255	-0.883025	0.153989	0.599219	1.955205	0.702022	-0.571010	1.787345	-0.092577	3.391365	...	-2.042102	0.106871	1.340272	-2.313874	0.697965	0.238777	0.005441	0.982994	XYZ	2
2998	-0.951836	-0.808981	-0.635262	-0.836083	0.253127	0.039529	-0.677641	0.234261	-0.751212	-0.235168	...	-0.315926	0.510587	-0.905646	0.490320	0.656834	0.923382	0.268773	0.382852	XYZ	2
2999	-0.866453	-0.231878	1.130572	1.520973	-1.004930	0.174273	-0.119098	-0.730932	-0.258387	-0.235168	...	-0.186269	0.483525	0.746580	0.774405	0.597423	0.347855	-0.133989	-0.526454	XYZ	2

3000 rows × 123 columns

Save a specific feature into a new variable.

[92]:

donor = mm_object1.save_feature('donor')
mm_object1.drop_variables('donor')

[93]:

file_number = mm_object1.save_feature('file_number')
mm_object1.drop_variables('file_number')

DMAP#

Run DMAP, or alternatively UMAP.

[94]:

mm_object1.dmap(5,10,1,'euclidean')

[95]:

#mm_object1.umap(nn=5,mdist=0.3,met='euclidean')

Feature Importance#

Calculate the importance of features in the dataset:

[96]:

fi_x=mm_object1.feature_importance('x','y')
print(fi_x)

length of data for training: 2400
length of data for testing: 600
r² Score: 0.9208301665560771
                       index1  importance_normalized  percentage_importance
0     Area_Inspire(M07__Ch07)               0.044624               1.869693
1                   Width_M10               0.051926               2.175636
2               Major_Axis_MC               0.053932               2.259647
3                  Length_M10               0.066301               2.777915
4               Perimeter_M01               0.072882               3.053641
5                    Area_M10               0.082522               3.457550
6                  Height_M10               0.104980               4.398503
7                    Area_M07               0.112134               4.698258
8  Area_Morphology(M07__Ch07)               0.305888              12.816248
9  Area_System(M07__Ch07__39)               1.000000              41.898441

Plot the feature importance, renaming one of the features in advance as an example:

[97]:

fi_x['index1'] = fi_x['index1'].replace({'Aspect_Ratio_M03': 'Area Ratio CXCR4'})
mm_object1.plot_feature_importance(fi_x,'./example/png/feature_importance_x.png',15,7)

Plot successfully saved to ./example/png/feature_importance_x.png

../_images/vignettes_morphomapping_tutorial_42_1.png

Calculate the feature importance for the values of the y coordinate

[98]:

fi_y=mm_object1.feature_importance('y','x')
print(fi_y)
mm_object1.plot_feature_importance(fi_y,'./example/png/feature_importance_y.png')

length of data for training: 2400
length of data for testing: 600
r² Score: 0.8355020516609143
                                  index1  importance_normalized  \
0                             Length_M03               0.101797
1                               Area_M05               0.126089
2                         Major_Axis_M02               0.131002
3  Lobe_Count_Morphology(M07__Ch07)_Ch07               0.140793
4                             Height_M04               0.201060
5                               Area_M12               0.284924
6                       Aspect_Ratio_M03               0.360203
7                              Width_M03               0.389692
8                               Area_M03               0.520689
9                             Height_M03               1.000000

   percentage_importance
0               2.167009
1               2.684136
2               2.788721
3               2.997145
4               4.280088
5               6.065362
6               7.667887
7               8.295619
8              11.084251
9              21.287652
Plot successfully saved to ./example/png/feature_importance_y.png

../_images/vignettes_morphomapping_tutorial_44_1.png

Clustering#

Cluster the dataset using either K-means, Gaussian Mixture Models, or HDBSCAN

[99]:

mm_object1.cluster_kmeans(4,'DMAP 1','DMAP 2')

mm_object1.cluster_gmm(5,42,'DMAP 1','DMAP 2')

mm_object1.cluster_hdbscan(3,'DMAP 1','DMAP 2')

../_images/vignettes_morphomapping_tutorial_47_0.png

../_images/vignettes_morphomapping_tutorial_47_1.png

../_images/vignettes_morphomapping_tutorial_47_2.png

Reattach the donor variable to the original dataset to preserve its association with the other features.

[100]:

mm_object1.concat_variables(donor,file_number)

[100]:

	Area F Actin	Area Nucleus	Area_M03	Area_M04	Area_M05	Area_M06	Area_M07	Area_M08	Area_M09	Area_M10	...	Aspect_Ratio_Inspire(M07__Ch07)	Aspect_Ratio_Inspire(M01__Ch01)	Shape_Ratio_Inspire(M02__Ch02)	x	y	kmeans_cluster	GMM_cluster	hdbscan_cluster	donor	file_number
0	1.337076	1.625742	0.897175	1.463912	-0.628185	-0.140131	-0.261273	-0.125493	0.123897	-0.235168	...	-0.702550	-0.961786	0.549558	12.690500	-3.765087	3	1	10	ABC	1
1	1.957742	1.799961	2.223853	2.425170	-1.065478	0.965897	0.601929	0.120193	1.367473	-0.235168	...	-1.402301	-2.109929	-0.640646	8.297702	-6.612311	3	3	10	ABC	1
2	0.713126	0.863530	-0.432575	-0.037230	1.443909	-0.089601	0.287114	0.839701	0.123897	-0.235168	...	0.738303	1.016538	1.072670	12.378029	3.824110	0	2	10	ABC	1
3	1.520976	1.721562	1.041512	-0.862419	-0.123617	1.527333	-0.748728	-0.318532	0.883860	-0.235168	...	0.756964	-0.230971	-0.817432	13.908522	6.700144	1	2	10	ABC	1
4	1.156458	0.532512	0.141705	0.691394	-1.065478	1.577862	0.322658	0.330781	0.653568	-0.235168	...	-0.414427	0.415230	-1.617622	13.321676	-0.480107	3	1	10	ABC	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2995	-1.355761	-1.379551	0.083355	0.199792	-0.836740	-0.695952	-1.271727	-0.450149	-1.234825	-0.235168	...	0.227906	0.780702	-0.817432	17.468170	2.318774	1	2	10	XYZ	2
2996	-0.611947	-0.484497	0.390457	0.072502	1.161350	-0.420848	1.256947	1.006416	1.174028	-0.235168	...	-2.887961	-3.359953	-1.471246	2.973614	-2.454584	3	3	6	XYZ	2
2997	-0.968255	-0.883025	0.153989	0.599219	1.955205	0.702022	-0.571010	1.787345	-0.092577	3.391365	...	0.238777	0.005441	0.982994	6.217534	0.889777	2	1	10	XYZ	2
2998	-0.951836	-0.808981	-0.635262	-0.836083	0.253127	0.039529	-0.677641	0.234261	-0.751212	-0.235168	...	0.923382	0.268773	0.382852	16.091002	5.345477	1	2	10	XYZ	2
2999	-0.866453	-0.231878	1.130572	1.520973	-1.004930	0.174273	-0.119098	-0.730932	-0.258387	-0.235168	...	0.347855	-0.133989	-0.526454	13.406321	-2.877094	3	1	10	XYZ	2

3000 rows × 128 columns

Interactive Plots#

Examples of various visualizations. Create a plot using a categaorical color mapper to differentiate between donors.

[101]:

mm_object1.cat_plot(
    feature='donor',
    subs=['ABL','XYZ'],
    colors=['blue','red'],
    outputf='test_cat_plot.html',
    fig_width=700,
    fig_height=700,
    fig_title='Simple Cat Plot - Donors',
    label_x='DensMAP 1',
    label_y='DensMAP 2',
    range_x=[-30, 40],
    range_y=[-35, 35],
    hover_tooltips=[
        ("Donor", "donor"),
        ("ID", "ID"),
        ("Area Ch05", "Area_M05"),
        ("X Value", "x"),
        ("Y Value", "y"),
    ],
    show_legend=True,
    point_size=12,
    point_alpha=0.8,
    show_axes=False,
    title_align='center'

)

[102]:

mm_object1.lin_plot(
    outputf='test_lin_plot.html',
    feature='Area_M05',
    colors='Plasma256',
    fig_width=700,
    fig_height=700,
    fig_title='Simple Linear Plot - Area_M05',
    label_x='DensMAP 1',
    label_y='DensMAP 2',
    range_x=[-30, 40],
    range_y=[-35, 35],
    hover_tooltips=[
        ("Donor", "donor"),
        ("ID", "ID"),
        ("Area Ch05", "Area_M05"),
        ("X-Value", "x"),
        ("Y-Value", "y")
    ],
    show_legend=False,
    point_size=12,
    point_alpha=0.8,
    show_axes=False,
    title_align='center'
)

Plot the values with a linear color mapper to represent the data on a continous color scale.

[103]:

colors = ['red', 'blue', 'orange', 'yellow', 'pink']

mm_object1.lin_plot(
    feature='GMM_cluster',
    colors=colors,
    outputf='cluster.html',
    fig_width=700,
    fig_height=700,
    fig_title='GMM-Cluster',
    label_x='DensMAP 1',
    label_y='DensMAP 2',
    range_x=[-30, 40],
    range_y=[-35, 35],
    show_legend=True,
    point_size=10,
    point_alpha=0.6,
    show_axes=True,
    title_align='center'
)

Plot the five clusters.

[104]:

colors = ['red', 'blue', 'orange', 'yellow', 'pink']

rename_dict = {
    0: 'cluster 0',
    1: 'cluster A',
    2: 'cluster H\u2082O\u2082',
    3: 'cluster 4',
    4: 'cluster 5'
}

mm_object1.update_column_values('GMM_cluster', rename_dict)

mm_object1.cat_plot(
    feature='GMM_cluster',
    subs=['cluster 0','cluster A','cluster H\u2082O\u2082','cluster 4','cluster 5'],
    colors=colors,
    outputf='cluster_new_labels.html',
    fig_width=800,
    fig_height=800,
    fig_title='Cluster - New Legend',
    label_x='DensMAP 1',
    label_y='DensMAP 2',
    range_x=[-30, 50],
    range_y=[-35, 35],
    hover_tooltips=[
        ("Donor", "donor"),
        ("ID", "ID"),
        ("Area Ch05", "Area_M05"),
        ("Cluster", "GMM_cluster"),
        ("X-Value", "x"),
        ("Y-Value", "y"),
    ],
    show_legend=True,
    point_size=10,
    point_alpha=0.8,
    show_axes=True,
    title_align='center'
)

Cell Plot#

plot clusters with given IDs:

kmeans

[109]:

mm_object1.cell_plot(cluster_column='kmeans_cluster',
                     top_clusters=4,
                     palette=p,
                     ID=[1,100,556,600,2456,2589,1800],
                     x_label='X',
                     y_label='Y',
                     png=True,
                     svg=False,
                     name='./example/cell_plot_example_kmeans')

 plot saved as: ./example/cell_plot_example_kmeans.png

../_images/vignettes_morphomapping_tutorial_61_1.png

Save dataset#

[108]:

mm_object1.save_xlsx('./example/excel.xlsx')
mm_object1.save_csv('./example/csvtest.CSV')

DataFrame successfully saved to ./example/excel.xlsx
DataFrame successfully saved to ./example/csvtest.CSV

MorphoMapping - Example Usage

Contents

MorphoMapping - Example Usage#

Imports#

Load object#

Convert files#

Structure dataset#

Load second object#

Read all CSV files in a given folder#

Concatenate object 1 and object 2#

Structure dataset#

Standardization/ Normalization#

DMAP#

Feature Importance#

Clustering#

Interactive Plots#

Cell Plot#

Save dataset#