CytoDataFrame at a Glance — CytoDataFrame documentation

import pathlib

import pandas as pd

from cytodataframe.frame import CytoDataFrame

# create paths for use with CytoDataFrames below
jump_data_path = "../../../tests/data/cytotable/JUMP_plate_BR00117006"
nf1_cellpainting_path = "../../../tests/data/cytotable/NF1_cellpainting_data_shrunken/"
nuclear_speckles_path = "../../../tests/data/cytotable/nuclear_speckles"
pediatric_cancer_atlas_path = (
    "../../../tests/data/cytotable/pediatric_cancer_atlas_profiling"
)

%%time
# view JUMP plate BR00117006 with images
CytoDataFrame(
    data=f"{jump_data_path}/BR00117006_shrunken.parquet",
    data_context_dir=f"{jump_data_path}/images/orig",
)[
    [
        "Metadata_ImageNumber",
        "Cells_Number_Object_Number",
        "Image_FileName_OrigAGP",
        "Image_FileName_OrigDNA",
        "Image_FileName_OrigRNA",
    ]
][:3]

CPU times: user 902 ms, sys: 686 ms, total: 1.59 s
Wall time: 502 ms

	Metadata_ImageNumber	Cells_Number_Object_Number
0	1	1
1	1	2
2	1	3

%%time
# view JUMP plate BR00117006 with images and overlaid outlines for segmentation
CytoDataFrame(
    data=f"{jump_data_path}/BR00117006_shrunken.parquet",
    data_context_dir=f"{jump_data_path}/images/orig",
    data_outline_context_dir=f"{jump_data_path}/images/outlines",
)[
    [
        "Metadata_ImageNumber",
        "Cells_Number_Object_Number",
        "Image_FileName_OrigAGP",
        "Image_FileName_OrigDNA",
        "Image_FileName_OrigRNA",
    ]
][:3]

CPU times: user 865 ms, sys: 673 ms, total: 1.54 s
Wall time: 467 ms

	Metadata_ImageNumber	Cells_Number_Object_Number
0	1	1
1	1	2
2	1	3

%%time
# view NF1 Cell Painting data with images
CytoDataFrame(
    data=f"{nf1_cellpainting_path}/Plate_2_with_image_data_shrunken.parquet",
    data_context_dir=f"{nf1_cellpainting_path}/Plate_2_images",
)[
    [
        "Metadata_ImageNumber",
        "Metadata_Cells_Number_Object_Number",
        "Image_FileName_GFP",
        "Image_FileName_RFP",
        "Image_FileName_DAPI",
    ]
][:3]

CPU times: user 256 ms, sys: 197 ms, total: 453 ms
Wall time: 140 ms

	Metadata_ImageNumber	Metadata_Cells_Number_Object_Number
353	31	4
1564	113	17
1275	94	5

%%time
# view NF1 Cell Painting data with images and overlaid outlines from masks
CytoDataFrame(
    data=f"{nf1_cellpainting_path}/Plate_2_with_image_data_shrunken.parquet",
    data_context_dir=f"{nf1_cellpainting_path}/Plate_2_images",
    data_mask_context_dir=f"{nf1_cellpainting_path}/Plate_2_masks",
)[
    [
        "Metadata_ImageNumber",
        "Metadata_Cells_Number_Object_Number",
        "Image_FileName_GFP",
        "Image_FileName_RFP",
        "Image_FileName_DAPI",
    ]
][:3]

CPU times: user 268 ms, sys: 186 ms, total: 454 ms
Wall time: 156 ms

	Metadata_ImageNumber	Metadata_Cells_Number_Object_Number
353	31	4
1564	113	17
1275	94	5

%%time
# add active paths on the local system to show how CytoDataFrame
# may be used without specifying a context directory for images.
# Note: normally these paths are local to the system where the
# profile data was generated, which often is not the same as the
# system which will be used to analyze the data.
parquet_path = f"{nf1_cellpainting_path}/Plate_2_with_image_data_shrunken.parquet"
nf1_dataset_with_modified_image_paths = pd.read_parquet(path=parquet_path)
nf1_dataset_with_modified_image_paths.loc[
    :, ["Image_PathName_DAPI", "Image_PathName_GFP", "Image_PathName_RFP"]
] = f"{pathlib.Path(parquet_path).parent}/Plate_2_images"

# view NF1 Cell Painting data with images and overlaid outlines from masks
CytoDataFrame(
    # note: we can read directly from an existing Pandas DataFrame
    data=nf1_dataset_with_modified_image_paths,
    data_mask_context_dir=f"{nf1_cellpainting_path}/Plate_2_masks",
)[
    [
        "Metadata_ImageNumber",
        "Metadata_Cells_Number_Object_Number",
        "Image_FileName_GFP",
        "Image_FileName_RFP",
        "Image_FileName_DAPI",
    ]
][:3]

CPU times: user 273 ms, sys: 200 ms, total: 473 ms
Wall time: 159 ms

	Metadata_ImageNumber	Metadata_Cells_Number_Object_Number
353	31	4
1564	113	17
1275	94	5

%%time
# view nuclear speckles data with images and overlaid outlines from masks
CytoDataFrame(
    data=f"{nuclear_speckles_path}/test_slide1_converted.parquet",
    data_context_dir=f"{nuclear_speckles_path}/images/plate1",
    data_mask_context_dir=f"{nuclear_speckles_path}/masks/plate1",
)[
    [
        "Metadata_ImageNumber",
        "Nuclei_Number_Object_Number",
        "Image_FileName_A647",
        "Image_FileName_DAPI",
        "Image_FileName_GOLD",
    ]
][:3]

CPU times: user 114 ms, sys: 85.5 ms, total: 200 ms
Wall time: 62.7 ms

	Metadata_ImageNumber	Nuclei_Number_Object_Number	Image_FileName_A647	Image_FileName_GOLD
0	1	1	slide1_A1_M10_CH1_Z09_illumcorrect.tiff	slide1_A1_M10_CH2_Z09_illumcorrect.tiff
1	1	2	slide1_A1_M10_CH1_Z09_illumcorrect.tiff	slide1_A1_M10_CH2_Z09_illumcorrect.tiff
2	1	3	slide1_A1_M10_CH1_Z09_illumcorrect.tiff	slide1_A1_M10_CH2_Z09_illumcorrect.tiff

%%time
# view ALSF pediatric cancer atlas plate BR00143976 with images
CytoDataFrame(
    data=f"{pediatric_cancer_atlas_path}/BR00143976_shrunken.parquet",
    data_context_dir=f"{pediatric_cancer_atlas_path}/images/orig",
    data_outline_context_dir=f"{pediatric_cancer_atlas_path}/images/outlines",
    segmentation_file_regex={
        r"CellsOutlines_BR(\d+)_C(\d{2})_\d+\.tiff": r".*ch3.*\.tiff",
        r"NucleiOutlines_BR(\d+)_C(\d{2})_\d+\.tiff": r".*ch5.*\.tiff",
    },
)[
    [
        "Metadata_ImageNumber",
        "Metadata_Nuclei_Number_Object_Number",
        "Image_FileName_OrigAGP",
        "Image_FileName_OrigDNA",
    ]
][:3]

CPU times: user 360 ms, sys: 263 ms, total: 622 ms
Wall time: 199 ms

	Metadata_ImageNumber	Metadata_Nuclei_Number_Object_Number
0	3	3
1	3	4
2	3	6

CytoDataFrame at a Glance#

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