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JUMP UMAP analysis with coSMicQC

This notebook analyzes JUMP data (cpg0000-jump-pilot) by leveraging UMAP and coSMicQC.

Outline

We focus on a single file from the JUMP dataset: BR00117012.sqlite. This file is downloaded and prepared by CytoTable to form a single-cell Parquet file which includes all compartment feature data at the single-cell level. We use coSMicQC to find and remove erroneous outlier data in order to prepare for UMAP analysis. Afterwards, we use UMAP to demonstrate patterns within the data.

import logging
import pathlib
from typing import List, Optional

import holoviews
import hvplot.pandas
import numpy as np
import pandas as pd
import plotly.express as px
import pycytominer
import umap
from cytotable.convert import convert
from IPython.display import HTML, Image
from parsl.config import Config
from parsl.executors import ThreadPoolExecutor
from pyarrow import parquet

import cosmicqc

# set bokeh for visualizations with hvplot
hvplot.extension("bokeh")

# create a dir for images
pathlib.Path("./images").mkdir(exist_ok=True)

# avoid displaying plot export warnings
logging.getLogger("bokeh.io.export").setLevel(logging.ERROR)

# set the plate name for use throughout the notebook
example_plate = "BR00117012"

%opts magic unavailable (pyparsing cannot be imported)
%compositor magic unavailable (pyparsing cannot be imported)

Define utility functions for use within this notebook

def generate_umap_embeddings(
    df_input: pd.DataFrame,
    cols_metadata_to_exclude: List[str],
    umap_n_components: int = 2,
    random_state: Optional[int] = None,
) -> np.ndarray:
    """
    Generates UMAP (Uniform Manifold Approximation and Projection)
    embeddings for a given input dataframe,
    excluding specified metadata columns.

    Args:
        df_input (pd.DataFrame]):
            A dataframe which is expected to contain
            numeric columns to be used for UMAP fitting.
        cols_metadata_to_exclude (List[str]):
            A list of column names representing
            metadata columns that should be excluded
            from the UMAP transformation.
        umap_n_components: (int):
            Number of components to use for UMAP.
            Default = 2.
        random_state (int):
            Number to use for random state and
            optional determinism.
            Default = None (random each time)
            Note: values besides None will turn
            off parallelism for umap-learn, likely
            meaning increased processing time.

    Returns:
        np.ndarray:
            A dataframe containing the UMAP embeddings
            with 2 components for each row in the input.
    """

    # Make sure to reinitialize UMAP instance per plate
    umap_fit = umap.UMAP(
        n_components=umap_n_components,
        random_state=random_state,
        # set the default value if we didn't set a random_state
        # otherwise set to 1 (umap-learn will override anyways).
        # this is set to avoid warnings from umap-learn during
        # processing.
        n_jobs=-1 if random_state is None else 1,
    )

    # Fit UMAP and convert to pandas DataFrame
    embeddings = umap_fit.fit_transform(
        X=df_input[
            [
                col
                for col in df_input.columns.tolist()
                if col not in cols_metadata_to_exclude and "cqc." not in col
            ]
            # select only numeric types from the dataframe
        ].select_dtypes(include=[np.number])
    )

    return embeddings

def plot_hvplot_scatter(
    embeddings: np.ndarray,
    title: str,
    filename: str,
    color_dataframe: Optional[pd.DataFrame] = None,
    color_column: Optional[str] = None,
    bgcolor: str = "black",
    cmap: str = "plasma",
    clabel: Optional[str] = None,
) -> holoviews.core.spaces.DynamicMap:
    """
    Creates an outlier-focused scatter hvplot for viewing
    UMAP embedding data with cosmicqc outliers coloration.

    Args:
        embeddings (np.ndarray]):
            A numpy ndarray which includes
            embedding data to display.
        title (str):
            Title for the UMAP scatter plot.
        filename (str):
            Filename which indicates where to export the
            plot.
        color_dataframe (pd.DataFrame):
            A dataframe which includes data used for
            color mapping within the plot. For example,
            coSMicQC .is_outlier columns.
        color_column (str):
            Column name from color_dataframe to use
            for coloring the scatter plot.
        bgcolor (str):
            Sets the background color of the plot.
        cmap (str):
            Sets the colormap used for the plot.
            See here for more:
            https://holoviews.org/user_guide/Colormaps.html
        clabel (str):
            Sets a label on the color map key displayed
            horizontally. Defaults to None (no label).

    Returns:
        holoviews.core.spaces.DynamicMap:
            A dynamic holoviews scatter plot which may be
            displayed in a Jupyter notebook.
    """

    # build a scatter plot through hvplot
    plot = pd.DataFrame(embeddings).hvplot.scatter(
        title=title,
        x="0",
        y="1",
        alpha=0.1,
        rasterize=True,
        c=(
            color_dataframe[color_column].astype(int).values
            if color_dataframe is not None
            else None
        ),
        cnorm="linear",
        cmap=cmap,
        bgcolor=bgcolor,
        height=700,
        width=800,
        clabel=clabel,
    )

    # export the plot
    hvplot.save(obj=plot, filename=filename, center=False)

    return plot

Merge single-cell compartment data into one table

# check if we already have prepared data
if not pathlib.Path(f"./{example_plate}.parquet").is_file():
    # process BR00117012.sqlite using CytoTable to prepare data
    merged_single_cells = convert(
        source_path=(
            "s3://cellpainting-gallery/cpg0000-jump-pilot/source_4/workspace"
            "/backend/2020_11_04_CPJUMP1/BR00117012/BR00117012.sqlite"
        ),
        dest_path=f"./{example_plate}.parquet",
        dest_datatype="parquet",
        source_datatype="sqlite",
        chunk_size=8000,
        preset="cellprofiler_sqlite_cpg0016_jump",
        # allows AWS S3 requests without login
        no_sign_request=True,
        # use explicit cache to avoid temp cache removal
        local_cache_dir="./sqlite_s3_cache/",
        parsl_config=Config(
            executors=[ThreadPoolExecutor(label="tpe_for_jump_processing")]
        ),
        sort_output=False,
    )
else:
    merged_single_cells = f"./{example_plate}.parquet"

# read only the metadata from parquet file
parquet.ParquetFile(merged_single_cells).metadata

<pyarrow._parquet.FileMetaData object at 0x724121c0ade0>
  created_by: parquet-cpp-arrow version 16.1.0
  num_columns: 5804
  num_rows: 279789
  num_row_groups: 14
  format_version: 2.6
  serialized_size: 12762532

Process merged single-cell data using coSMicQC

# show the first few columns for metadata column names
schema_names = parquet.read_schema(merged_single_cells).names
schema_names[:12]

['Metadata_ImageNumber',
 'Image_Metadata_Row',
 'Image_Metadata_Site',
 'Metadata_ObjectNumber',
 'Metadata_ObjectNumber_1',
 'Metadata_ObjectNumber_2',
 'Metadata_Plate',
 'Metadata_Well',
 'Image_TableNumber',
 'Cytoplasm_AreaShape_Area',
 'Cytoplasm_AreaShape_BoundingBoxArea',
 'Cytoplasm_AreaShape_BoundingBoxMaximum_X']

# set a list of metadata columns for use throughout
metadata_cols = [
    "Metadata_ImageNumber",
    "Image_Metadata_Row",
    "Image_Metadata_Site",
    "Metadata_ObjectNumber",
    "Metadata_Plate",
    "Metadata_Well",
    "Image_TableNumber",
]

# read only metadata columns with feature columns used for outlier detection
df_merged_single_cells = pd.read_parquet(
    path=merged_single_cells,
    columns=[
        *metadata_cols,
        "Nuclei_AreaShape_Area",
        "Nuclei_AreaShape_FormFactor",
        "Nuclei_AreaShape_Eccentricity",
    ],
)
df_merged_single_cells.head()

	Metadata_ImageNumber	Image_Metadata_Row	Image_Metadata_Site	Metadata_ObjectNumber	Metadata_Plate	Metadata_Well	Image_TableNumber	Nuclei_AreaShape_Area	Nuclei_AreaShape_FormFactor	Nuclei_AreaShape_Eccentricity
0	2	1	2	2	BR00117012	A01	35196781680809101223867031596229856156	1091	0.895393	0.694154
1	2	1	2	3	BR00117012	A01	35196781680809101223867031596229856156	1007	0.837631	0.819062
2	2	1	2	4	BR00117012	A01	35196781680809101223867031596229856156	1368	0.833197	0.820257
3	2	1	2	5	BR00117012	A01	35196781680809101223867031596229856156	847	0.902995	0.345575
4	2	1	2	6	BR00117012	A01	35196781680809101223867031596229856156	983	0.863005	0.742060

# label outliers within the dataset
print("Large nuclei outliers:")
df_labeled_outliers = cosmicqc.analyze.find_outliers(
    df=df_merged_single_cells,
    metadata_columns=metadata_cols,
    feature_thresholds="large_nuclei",
)

Large nuclei outliers:
Number of outliers: 1355 (0.48%)
Outliers Range:
Nuclei_AreaShape_Area Min: 1754
Nuclei_AreaShape_Area Max: 4414
Nuclei_AreaShape_FormFactor Min: 0.3367261940249281
Nuclei_AreaShape_FormFactor Max: 0.7140072671383899

# label outliers within the dataset
print("Elongated nuclei outliers:")
df_labeled_outliers = cosmicqc.analyze.find_outliers(
    df=df_merged_single_cells,
    metadata_columns=metadata_cols,
    feature_thresholds="elongated_nuclei",
)

Elongated nuclei outliers:
Number of outliers: 15 (0.01%)
Outliers Range:
Nuclei_AreaShape_Eccentricity Min: 0.9868108584805481
Nuclei_AreaShape_Eccentricity Max: 0.9995098494433163

# label outliers within the dataset
print("Small and low formfactor nuclei outliers:")
df_labeled_outliers = cosmicqc.analyze.find_outliers(
    df=df_merged_single_cells,
    metadata_columns=metadata_cols,
    feature_thresholds="small_and_low_formfactor_nuclei",
)

Small and low formfactor nuclei outliers:
Number of outliers: 6548 (2.34%)
Outliers Range:
Nuclei_AreaShape_Area Min: 79
Nuclei_AreaShape_Area Max: 744
Nuclei_AreaShape_FormFactor Min: 0.0945907341645769
Nuclei_AreaShape_FormFactor Max: 0.7781815132858318

# label outliers within the dataset
df_labeled_outliers = cosmicqc.analyze.label_outliers(
    df=df_merged_single_cells,
    include_threshold_scores=True,
)
# show added columns
df_labeled_outliers[
    [col for col in df_labeled_outliers.columns.tolist() if "cqc." in col]
].head()

	cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Area	cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactor	cqc.small_and_low_formfactor_nuclei.is_outlier	cqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricity	cqc.elongated_nuclei.is_outlier	cqc.large_nuclei.Z_Score.Nuclei_AreaShape_Area	cqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactor	cqc.large_nuclei.is_outlier
0	0.030121	0.826248	False	-0.154094	False	0.030121	0.826248	False
1	-0.219592	-0.073800	False	0.765830	False	-0.219592	-0.073800	False
2	0.853580	-0.142903	False	0.774634	False	0.853580	-0.142903	False
3	-0.695236	0.944704	False	-2.721308	False	-0.695236	0.944704	False
4	-0.290938	0.321578	False	0.198723	False	-0.290938	0.321578	False

# create a column which indicates whether an erroneous outlier was detected
# from all cosmicqc outlier threshold sets. For ex. True for is_outlier in
# one threshold set out of three would show True for this column. False for
# is_outlier in all threshold sets would show False for this column.
df_labeled_outliers["analysis.included_at_least_one_outlier"] = df_labeled_outliers[
    [col for col in df_labeled_outliers.columns.tolist() if ".is_outlier" in col]
].any(axis=1)

# show value counts for all outliers
outliers_counts = df_labeled_outliers[
    "analysis.included_at_least_one_outlier"
].value_counts()
outliers_counts

analysis.included_at_least_one_outlier
False    271883
True       7906
Name: count, dtype: int64

# show the percentage of total dataset
print(
    (outliers_counts.iloc[1] / outliers_counts.iloc[0]) * 100,
    "%",
    "of",
    outliers_counts.iloc[0],
    "include erroneous outliers of some kind.",
)

2.9078684581235312 % of 271883 include erroneous outliers of some kind.

Prepare data for analysis with pycytominer

parquet_sampled_with_outliers = f"./{example_plate}_sampled_with_outliers.parquet"

# check if we already have normalized data
if not pathlib.Path(parquet_sampled_with_outliers).is_file():
    # set a fraction for sampling to limit the amount
    # of data processed based on system memory constraints.
    # note: data was processed on system with 16 CPU, 64 GB ram
    sample_fraction = 0.44

    # read the dataset
    df_features = pd.read_parquet(f"./{example_plate}.parquet")

    # group by metadata_well for all features then sample
    # the dataset by a fraction.
    df_features = (
        # note: we add the column selection here to avoid a pandas
        # DeprecationWarning. See the following link for more details:
        # https://stackoverflow.com/questions/77969964/deprecation-warning-with-groupby-apply
        df_features.groupby(["Metadata_Well"])[df_features.columns]
        .apply(lambda x: x.sample(frac=sample_fraction))
        .reset_index(drop=True)
    )

    # join the sampled feature data with the cosmicqc outlier data
    df_features_with_cqc_outlier_data = df_features.merge(
        # select metadata columns plus those which don't exist in
        # df_features (cosmicqc or analysis-specific columns)
        df_labeled_outliers[
            [
                *metadata_cols,
                *[
                    col
                    for col in df_labeled_outliers.columns
                    if col not in df_features.columns
                ],
            ]
        ],
        how="inner",
        left_on=metadata_cols,
        right_on=metadata_cols,
    )

    df_features_with_cqc_outlier_data.to_parquet(parquet_sampled_with_outliers)

else:
    df_features_with_cqc_outlier_data = pd.read_parquet(parquet_sampled_with_outliers)

df_features_with_cqc_outlier_data

	Metadata_ImageNumber	Image_Metadata_Row	Image_Metadata_Site	Metadata_ObjectNumber	Metadata_ObjectNumber_1	Metadata_ObjectNumber_2	Metadata_Plate	Metadata_Well	Image_TableNumber	Cytoplasm_AreaShape_Area	...	Nuclei_Texture_Variance_RNA_5_03_256	cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_Area	cqc.small_and_low_formfactor_nuclei.Z_Score.Nuclei_AreaShape_FormFactor	cqc.small_and_low_formfactor_nuclei.is_outlier	cqc.elongated_nuclei.Z_Score.Nuclei_AreaShape_Eccentricity	cqc.elongated_nuclei.is_outlier	cqc.large_nuclei.Z_Score.Nuclei_AreaShape_Area	cqc.large_nuclei.Z_Score.Nuclei_AreaShape_FormFactor	cqc.large_nuclei.is_outlier	analysis.included_at_least_one_outlier
0	9	1	9	36	36	36	BR00117012	A01	123427782301510124726705416481797689855	3816	...	17.533683	-0.415795	0.971457	False	-0.130095	False	-0.415795	0.971457	False	False
1	8	1	8	3	3	3	BR00117012	A01	195970212232453775991782680321528206390	3042	...	7.773093	-0.166082	-2.590543	False	1.539330	False	-0.166082	-2.590543	False	False
2	5	1	5	47	47	47	BR00117012	A01	327256301155101152193147088571265307381	4343	...	5.135272	1.596773	0.052141	False	0.483920	False	1.596773	0.052141	False	False
3	7	1	7	50	50	50	BR00117012	A01	40464853431235667657927130465268580453	4114	...	6.922958	0.184705	0.997999	False	-0.488523	False	0.184705	0.997999	False	False
4	7	1	7	17	17	17	BR00117012	A01	40464853431235667657927130465268580453	2239	...	3.321053	-0.531733	1.294974	False	-0.612494	False	-0.531733	1.294974	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
117508	3454	16	7	68	68	68	BR00117012	P24	53376628655035216909378587811283720456	5259	...	53.436383	1.367870	0.176541	False	0.095166	False	1.367870	0.176541	False	False
117509	3448	16	1	65	65	65	BR00117012	P24	115066026069838743189211380847920733616	2777	...	6.429991	-0.290938	-1.220927	False	1.124422	False	-0.290938	-1.220927	False	False
117510	3451	16	4	87	87	87	BR00117012	P24	202830213553184495296857704660514325554	3542	...	7.046681	-0.347421	0.684254	False	0.167217	False	-0.347421	0.684254	False	False
117511	3454	16	7	76	76	76	BR00117012	P24	53376628655035216909378587811283720456	2646	...	20.230426	-0.671454	-0.050317	False	-1.363158	False	-0.671454	-0.050317	False	False
117512	3453	16	6	27	27	27	BR00117012	P24	191371623772255400331268116024855319512	5172	...	7.019160	1.412461	0.669994	False	-0.154804	False	1.412461	0.669994	False	False

117513 rows × 5813 columns

# show our data value counts regarding outliers vs inliers
df_features_with_cqc_outlier_data[
    "analysis.included_at_least_one_outlier"
].value_counts()

analysis.included_at_least_one_outlier
False    114138
True       3375
Name: count, dtype: int64

# prepare data for normalization and feature selection
# by removing cosmicqc and analaysis focused columns.
df_for_normalize_and_feature_select = df_features_with_cqc_outlier_data[
    # read feature names from cytotable output, which excludes
    # cosmicqc-added columns.
    parquet.read_schema(merged_single_cells).names
]
# show the modified column count
len(df_for_normalize_and_feature_select.columns)

5804

# join JUMP metadata with platemap data to prepare for annotation
df_platemap_and_metadata = pd.read_csv(
    filepath_or_buffer=(
        "s3://cellpainting-gallery/cpg0000-jump-pilot/source_4"
        "/workspace/metadata/platemaps/2020_11_04_CPJUMP1/"
        "platemap/JUMP-Target-1_compound_platemap.txt"
    ),
    sep="\t",
).merge(
    right=pd.read_csv(
        filepath_or_buffer=(
            "s3://cellpainting-gallery/cpg0000-jump-pilot/source_4"
            "/workspace/metadata/external_metadata/"
            "JUMP-Target-1_compound_metadata.tsv"
        ),
        sep="\t",
    ),
    left_on="broad_sample",
    right_on="broad_sample",
)

parquet_pycytominer_annotated = f"./{example_plate}_annotated.parquet"

# check if we already have annotated data
if not pathlib.Path(parquet_pycytominer_annotated).is_file():
    # annotate the data using pycytominer
    pycytominer.annotate(
        profiles=df_for_normalize_and_feature_select,
        # read the platemap directly from AWS S3 related location
        platemap=df_platemap_and_metadata,
        join_on=["Metadata_well_position", "Metadata_Well"],
        output_file=parquet_pycytominer_annotated,
        output_type="parquet",
    )

parquet_pycytominer_normalized = f"./{example_plate}_normalized.parquet"

# check if we already have normalized data
if not pathlib.Path(parquet_pycytominer_normalized).is_file():
    # normalize the data using pcytominer
    df_pycytominer_normalized = pycytominer.normalize(
        profiles=parquet_pycytominer_annotated,
        features="infer",
        image_features=False,
        meta_features="infer",
        method="standardize",
        samples="Metadata_control_type == 'negcon'",
        output_file=parquet_pycytominer_normalized,
        output_type="parquet",
    )

parquet_pycytominer_feature_selected = f"./{example_plate}_feature_select.parquet"

# check if we already have feature selected data
if not pathlib.Path(parquet_pycytominer_feature_selected).is_file():
    # feature select normalized data using pycytominer
    df_pycytominer_feature_selected = pycytominer.feature_select(
        profiles=parquet_pycytominer_normalized,
        operation=[
            "variance_threshold",
            "correlation_threshold",
            "blocklist",
            "drop_na_columns",
        ],
        na_cutoff=0,
        output_file=parquet_pycytominer_feature_selected,
        output_type="parquet",
    )

# regather metadata columns to account for new additions
all_metadata_cols = [
    col
    for col in parquet.read_schema(parquet_pycytominer_feature_selected).names
    if col.startswith("Metadata_")
]
all_metadata_cols

['Metadata_broad_sample',
 'Metadata_solvent',
 'Metadata_InChIKey',
 'Metadata_pert_iname',
 'Metadata_pubchem_cid',
 'Metadata_gene',
 'Metadata_pert_type',
 'Metadata_control_type',
 'Metadata_smiles',
 'Metadata_ImageNumber',
 'Metadata_ObjectNumber',
 'Metadata_ObjectNumber_1',
 'Metadata_ObjectNumber_2',
 'Metadata_Plate',
 'Metadata_Well']

# calculate UMAP embeddings from the data
# which was prepared by pycytominer.
embeddings_with_outliers = generate_umap_embeddings(
    df_input=pd.read_parquet(parquet_pycytominer_feature_selected),
    cols_metadata_to_exclude=all_metadata_cols,
    random_state=0,
)
# show the shape and top values from the embeddings array
print(embeddings_with_outliers.shape)
embeddings_with_outliers[:3]

(117513, 2)

array([[-0.39003304,  4.0597053 ],
       [ 0.430353  ,  5.700904  ],
       [-0.33348054,  4.008859  ]], dtype=float32)

plot_hvplot_scatter(
    embeddings=embeddings_with_outliers,
    title=f"UMAP of JUMP embeddings from {example_plate} (with erroneous outliers)",
    filename=(
        image_with_all_outliers
        := f"./images/umap_with_all_outliers_{example_plate}.png"
    ),
    bgcolor="white",
    cmap=px.colors.sequential.Greens[4:],
    clabel="density of single cells",
)
# conserve filespace by displaying export instead of dynamic plot
Image(image_with_all_outliers)

# show a UMAP for all outliers within the data
plot_hvplot_scatter(
    embeddings=embeddings_with_outliers,
    title=f"UMAP of JUMP all coSMicQC erroneous outliers within {example_plate}",
    filename=f"./images/umap_erroneous_outliers_{example_plate}.png",
    color_dataframe=df_features_with_cqc_outlier_data,
    color_column="analysis.included_at_least_one_outlier",
    clabel="density of single cells classified as outliers",
)

# show small and low formfactor nuclei outliers within the data
plot_hvplot_scatter(
    embeddings=embeddings_with_outliers,
    title=f"UMAP of JUMP small and low formfactor nuclei outliers within {example_plate}",
    filename=(
        plot_image
        := f"./images/umap_small_and_low_formfactor_nuclei_outliers_{example_plate}.png"
    ),
    color_dataframe=df_features_with_cqc_outlier_data,
    color_column="cqc.small_and_low_formfactor_nuclei.is_outlier",
    clabel="density of single cells classified as outliers",
)
# conserve filespace by displaying export instead of dynamic plot
Image(plot_image)

# show elongated nuclei outliers within the data
plot_hvplot_scatter(
    embeddings=embeddings_with_outliers,
    title=f"UMAP of JUMP elongated nuclei outliers within {example_plate}",
    filename=(
        plot_image := f"./images/umap_elongated_nuclei_outliers_{example_plate}.png"
    ),
    color_dataframe=df_features_with_cqc_outlier_data,
    color_column="cqc.elongated_nuclei.is_outlier",
    clabel="density of single cells classified as outliers",
)
# conserve filespace by displaying export instead of dynamic plot
Image(plot_image)

# show small and large nuclei outliers within the data
plot_hvplot_scatter(
    embeddings=embeddings_with_outliers,
    title=f"UMAP of JUMP large nuclei outliers within {example_plate}",
    filename=(plot_image := f"./images/umap_large_nuclei_outliers_{example_plate}.png"),
    color_dataframe=df_features_with_cqc_outlier_data,
    color_column="cqc.large_nuclei.is_outlier",
    clabel="density of single cells classified as outliers",
)
# conserve filespace by displaying export instead of dynamic plot
Image(plot_image)

# prepare data for normalization and feature selection
# by removing cosmicqc and analaysis focused columns.
df_for_normalize_and_feature_select_without_outliers = (
    df_features_with_cqc_outlier_data[
        # seek values which are false (not considered an outlier)
        ~df_features_with_cqc_outlier_data["analysis.included_at_least_one_outlier"]
    ][
        # read feature names from cytotable output, which excludes
        # cosmicqc-added columns.
        parquet.read_schema(merged_single_cells).names
    ]
)
# show the modified column count
len(df_for_normalize_and_feature_select_without_outliers.columns)

df_for_normalize_and_feature_select_without_outliers

	Metadata_ImageNumber	Image_Metadata_Row	Image_Metadata_Site	Metadata_ObjectNumber	Metadata_ObjectNumber_1	Metadata_ObjectNumber_2	Metadata_Plate	Metadata_Well	Image_TableNumber	Cytoplasm_AreaShape_Area	...	Nuclei_Texture_Variance_RNA_10_02_256	Nuclei_Texture_Variance_RNA_10_03_256	Nuclei_Texture_Variance_RNA_3_00_256	Nuclei_Texture_Variance_RNA_3_01_256	Nuclei_Texture_Variance_RNA_3_02_256	Nuclei_Texture_Variance_RNA_3_03_256	Nuclei_Texture_Variance_RNA_5_00_256	Nuclei_Texture_Variance_RNA_5_01_256	Nuclei_Texture_Variance_RNA_5_02_256	Nuclei_Texture_Variance_RNA_5_03_256
0	9	1	9	36	36	36	BR00117012	A01	123427782301510124726705416481797689855	3816	...	17.710557	18.991890	18.447506	18.367887	18.701323	17.702356	17.615662	17.587456	17.953973	17.533683
1	8	1	8	3	3	3	BR00117012	A01	195970212232453775991782680321528206390	3042	...	8.179383	6.671637	7.189478	7.536011	7.104708	7.279566	7.302198	7.580178	7.376624	7.773093
2	5	1	5	47	47	47	BR00117012	A01	327256301155101152193147088571265307381	4343	...	5.163118	5.640761	5.020867	4.992194	5.184511	5.008445	5.055747	4.964113	5.162984	5.135272
3	7	1	7	50	50	50	BR00117012	A01	40464853431235667657927130465268580453	4114	...	7.258791	7.610346	6.637604	6.817955	6.711880	6.838380	6.808615	7.053098	6.868436	6.922958
4	7	1	7	17	17	17	BR00117012	A01	40464853431235667657927130465268580453	2239	...	3.551165	3.746588	3.351621	3.306194	3.172812	3.165931	3.354827	3.333333	3.170766	3.321053
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
117508	3454	16	7	68	68	68	BR00117012	P24	53376628655035216909378587811283720456	5259	...	41.944830	56.983976	46.352110	39.910755	48.548599	52.983990	41.889314	38.855503	45.221182	53.436383
117509	3448	16	1	65	65	65	BR00117012	P24	115066026069838743189211380847920733616	2777	...	7.326642	7.853596	6.997897	6.980528	6.350092	6.186232	6.905416	6.896725	6.223140	6.429991
117510	3451	16	4	87	87	87	BR00117012	P24	202830213553184495296857704660514325554	3542	...	6.781933	6.532444	7.127214	6.905356	6.895536	6.937326	7.191295	6.893606	6.980451	7.046681
117511	3454	16	7	76	76	76	BR00117012	P24	53376628655035216909378587811283720456	2646	...	19.973679	22.506483	20.690118	20.081294	22.259422	20.408965	20.774290	19.253769	20.838725	20.230426
117512	3453	16	6	27	27	27	BR00117012	P24	191371623772255400331268116024855319512	5172	...	6.797173	7.068533	7.507398	7.280371	7.028356	7.128716	7.401507	7.071818	6.756597	7.019160

114138 rows × 5804 columns

print("Length of dataset with outliers: ", len(df_for_normalize_and_feature_select))
print(
    "Length of dataset without outliers: ",
    len(df_for_normalize_and_feature_select_without_outliers),
)

Length of dataset with outliers:  117513
Length of dataset without outliers:  114138

parquet_pycytominer_annotated_wo_outliers = (
    f"./{example_plate}_annotated_wo_outliers.parquet"
)

# check if we already have annotated data
if not pathlib.Path(parquet_pycytominer_annotated_wo_outliers).is_file():
    # annotate the data using pycytominer
    pycytominer.annotate(
        profiles=df_for_normalize_and_feature_select_without_outliers,
        # read the platemap directly from AWS S3 related location
        platemap=df_platemap_and_metadata,
        join_on=["Metadata_well_position", "Metadata_Well"],
        output_file=parquet_pycytominer_annotated_wo_outliers,
        output_type="parquet",
    )

parquet_pycytominer_normalized_wo_outliers = (
    f"./{example_plate}_normalized_wo_outliers.parquet"
)

# check if we already have normalized data
if not pathlib.Path(parquet_pycytominer_normalized_wo_outliers).is_file():
    # normalize the data using pcytominer
    df_pycytominer_normalized = pycytominer.normalize(
        profiles=parquet_pycytominer_annotated_wo_outliers,
        features="infer",
        image_features=False,
        meta_features="infer",
        method="standardize",
        samples="Metadata_control_type == 'negcon'",
        output_file=parquet_pycytominer_normalized_wo_outliers,
        output_type="parquet",
    )

parquet_pycytominer_feature_selected_wo_outliers = (
    f"./{example_plate}_feature_select_wo_outliers.parquet"
)

# check if we already have feature selected data
if not pathlib.Path(parquet_pycytominer_feature_selected_wo_outliers).is_file():
    # feature select normalized data using pycytominer
    df_pycytominer_feature_selected = pycytominer.feature_select(
        profiles=parquet_pycytominer_normalized_wo_outliers,
        operation=[
            "variance_threshold",
            "correlation_threshold",
            "blocklist",
            "drop_na_columns",
        ],
        na_cutoff=0,
        output_file=parquet_pycytominer_feature_selected_wo_outliers,
        output_type="parquet",
    )

# calculate UMAP embeddings from data without coSMicQC-detected outliers
embeddings_without_outliers = generate_umap_embeddings(
    df_input=pd.read_parquet(parquet_pycytominer_feature_selected_wo_outliers),
    cols_metadata_to_exclude=all_metadata_cols,
    random_state=0,
)
# show the shape and top values from the embeddings array
print(embeddings_without_outliers.shape)
embeddings_without_outliers[:3]

(114138, 2)

array([[-0.5193578 ,  3.6437721 ],
       [ 0.13389692,  5.410578  ],
       [-0.40673473,  3.684243  ]], dtype=float32)

# plot UMAP for embeddings without outliers
plot_hvplot_scatter(
    embeddings=embeddings_without_outliers,
    title=f"UMAP of JUMP embeddings from {example_plate} (without erroneous outliers)",
    filename=(
        image_without_all_outliers
        := f"./images/umap_without_outliers_{example_plate}.png"
    ),
    bgcolor="white",
    cmap=px.colors.sequential.Greens[4:],
    clabel="density of single cells",
)
# conserve filespace by displaying export instead of dynamic plot
Image(image_without_all_outliers)

# compare the UMAP images with and without outliers side by side
HTML(
    f"""
    <div style="display: flex;">
      <img src="{image_with_all_outliers}" alt="UMAP which includes erroneous outliers" style="width: 50%;"/>
      <img src="{image_without_all_outliers}" alt="UMAP which includes no erroneous outliers" style="width: 50%;"/>
    </div>
    """
)

# concatenate embeddings together
combined_embeddings = np.vstack((embeddings_with_outliers, embeddings_without_outliers))

# Step 2: Create the labels array
combined_labels = np.concatenate(
    [np.zeros(len(embeddings_with_outliers)), np.ones(len(embeddings_without_outliers))]
)

# visualize UMAP embeddings both with and without outliers together for comparison
plot_hvplot_scatter(
    embeddings=combined_embeddings,
    title=f"UMAP comparing JUMP embeddings from {example_plate} with and without erroneous outliers",
    filename=f"./images/umap_comparison_with_and_without_erroneous_outliers_{example_plate}.png",
    color_dataframe=pd.DataFrame(
        combined_labels, columns=["combined_embedding_color_label"]
    ),
    color_column="combined_embedding_color_label",
    bgcolor="white",
    cmap=[
        "#e76f51",  # Darkest Orange
        "#f4a261",  # Darker Orange
        "#ffbb78",  # Light Orange
        "#aec7e8",  # Light Blue
        "#6baed6",  # Darker Blue
        "#1f77b4",  # Darkest Blue
    ],
    clabel="density of single cells with (orange) and without outliers (blue)",
)