diff --git a/EMT_data_analysis/analysis_scripts/Feature_extraction.py b/EMT_data_analysis/analysis_scripts/Feature_extraction.py index 5bd26b4..fc4288c 100755 --- a/EMT_data_analysis/analysis_scripts/Feature_extraction.py +++ b/EMT_data_analysis/analysis_scripts/Feature_extraction.py @@ -1,99 +1,411 @@ import warnings -import platform +import time import numpy as np import pandas as pd -from tqdm import tqdm from pathlib import Path from bioio import BioImage +from joblib import Parallel, delayed +from aiohttp.client_exceptions import ServerDisconnectedError, ClientError +from bioio_base.exceptions import UnsupportedFileFormatError from EMT_data_analysis.tools import io, alignment warnings.filterwarnings("ignore") -def compute_bf_colony_features_all_movies(output_folder, align=True): - ''' - Computes area of the bright field colony mask at every z position - and extracts corresponding intensity values from the fluorescence - channel. It also adds other features from the bright field colony - mask to the final dataframe that is saved at the end. + +def get_channel_camera(channel_num, bf_ch, ch_488, ch_561, ch_638): + """ + Determine which camera a channel is on based on wavelength mapping. + + Camera assignment rules: + - Camera 1: Brightfield, 638nm wavelength + - Camera 2: 488nm, 561nm wavelength + Parameters ---------- - df: pd.DataFrame - Data manifest to process - save_folder: path - Folder path where feature csv for each movie is stored + channel_num : int + Channel number to check + bf_ch : int or float + Brightfield channel number from manifest + ch_488 : int or float + 488nm wavelength channel number from manifest + ch_561 : int or float + 561nm wavelength channel number from manifest + ch_638 : int or float + 638nm wavelength channel number from manifest - align: bool - Enable alignment of the image using the barcode of the movie - ''' + Returns + ------- + int or None + 1 for Camera 1, 2 for Camera 2, None if unknown + """ + # Handle NaN values by converting to -1 (impossible channel number) + bf_ch = -1 if pd.isna(bf_ch) else int(bf_ch) + ch_488 = -1 if pd.isna(ch_488) else int(ch_488) + ch_561 = -1 if pd.isna(ch_561) else int(ch_561) + ch_638 = -1 if pd.isna(ch_638) else int(ch_638) + + if channel_num == bf_ch or channel_num == ch_638: + return 1 # Camera 1: Brightfield, 638nm + elif channel_num == ch_488 or channel_num == ch_561: + return 2 # Camera 2: 488nm, 561nm + return None # Unknown - df = io.load_imaging_and_segmentation_dataset() - print(f"Dataset loaded. Shape: {df.shape}.") - for movie_id, df_movie in tqdm(df.groupby('Data ID')): - - out_fn = Path(output_folder) / f"Features_bf_colony_mask_{movie_id}.csv" - if out_fn.exists(): +def load_image_with_retry(dask_array, max_retries=5): + """Load dask array with retry logic for transient network errors.""" + last_error = None + for attempt in range(max_retries): + try: + return dask_array.compute() + except Exception as e: + last_error = e + if attempt < max_retries - 1: + wait_time = min(2 ** attempt, 30) + time.sleep(wait_time) continue + raise last_error - print(f"Movie: {movie_id}") - - print("Getting raw data...") - raw_path = df_movie["Raw File URL"].values[0] - raw_reader = BioImage(raw_path) - print(raw_path) - print(raw_reader.shape) - - print("Getting colony mask....") - seg_path = df_movie["All Cells Mask URL"].values[0] - seg_reader = BioImage(seg_path) - - print("Computing features....") + +def open_bioimage_with_retry(path, max_retries=10): + """Open BioImage with retry logic for transient network errors.""" + last_error = None + for attempt in range(max_retries): + try: + return BioImage(path) + except (ServerDisconnectedError, ClientError, ConnectionError, TimeoutError, UnsupportedFileFormatError) as e: + last_error = e + if attempt < max_retries - 1: + wait_time = min(2 ** attempt, 60) + time.sleep(wait_time) + continue + raise last_error + + +def _parse_timelapse_interval_minutes(interval_str): + """ + Parse timelapse interval string to minutes. + + Parameters + ---------- + interval_str : str or None + Timelapse interval string (e.g., '30 minutes', '1 hour', '3 minutes') + + Returns + ------- + float + Interval in minutes, defaults to 30 if not parseable + """ + if pd.isna(interval_str) or interval_str is None: + return 30.0 # Default to 30 minutes (most common) + + interval_str = str(interval_str).lower().strip() + + if 'minute' in interval_str: + try: + return float(''.join(filter(lambda c: c.isdigit() or c == '.', interval_str))) + except ValueError: + return 30.0 + elif 'hour' in interval_str: + try: + hours = float(''.join(filter(lambda c: c.isdigit() or c == '.', interval_str))) + return hours * 60 + except ValueError: + return 60.0 + else: + return 30.0 # Default + + +def _process_single_movie(movie_id, raw_path, seg_path, matrix_string, gene, experimental_condition, + output_folder, align=True, fixation_status=None, fixation_time_hours=None, + timelapse_interval=None, wavelength_channels=None): + """ + Process a single movie to extract features. Designed for parallel execution. + + Parameters + ---------- + movie_id : str + Data ID of the movie + raw_path : str + URL/path to raw image data + seg_path : str + URL/path to segmentation mask + matrix_string : str + Dual camera alignment matrix string + gene : str + Gene name + experimental_condition : str + Experimental condition + output_folder : Path + Output folder for CSV files + align : bool + Whether to apply alignment + fixation_status : str or None + 'Fixed Cells' or 'Live Cells' or None + fixation_time_hours : float or None + Time of fixation post EMT induction (in hours) for fixed cells + timelapse_interval : str or None + Timelapse interval string from metadata (e.g., '30 minutes') + wavelength_channels : dict or None + Dictionary mapping wavelength to channel number: + {'bf': int, '488': int, '561': int, '638': int} + Used to determine which camera each channel is on. + + Returns + ------- + tuple + (movie_id, success, error_message) + """ + # Determine if this is a fixed cell (immunostaining) experiment + is_fixed_cell = (fixation_status == 'Fixed Cells') + + # Parse timelapse interval for timepoint calculation + # For fixed cells, use 30 min standard interval to match live timelapse frame numbering + interval_minutes = _parse_timelapse_interval_minutes(timelapse_interval) if not is_fixed_cell else 30.0 + out_fn = Path(output_folder) / f"Features_bf_colony_mask_{movie_id}.csv" + + # Skip if already processed + if out_fn.exists(): + return (movie_id, True, "already exists") + + # Skip if missing data + if pd.isna(raw_path) or pd.isna(seg_path): + return (movie_id, False, "missing raw data or segmentation mask") + + try: + # Load images + raw_reader = open_bioimage_with_retry(raw_path) + seg_reader = open_bioimage_with_retry(seg_path) + + # Determine which channels to extract based on available channels + num_channels = raw_reader.dims.C + if num_channels == 1: + # Single channel image: use Channel 0 + channels_to_extract = [0] + main_channel = 0 + else: + # Multi-channel image: use Channel 1 as main, add 2 and 3 if available + channels_to_extract = [1] # Channel 1 (main fluorescence) + main_channel = 1 + if num_channels > 2: + channels_to_extract.append(2) # Channel 2 + if num_channels > 3: + channels_to_extract.append(3) # Channel 3 + + # Compute alignment transform once per movie (will be applied per-channel as needed) + # Segmentation (All Cells Mask) is from Brightfield = Camera 1 + # Alignment is only needed when raw channel is on Camera 2 (488nm, 561nm) + base_transform = None + if align and not pd.isna(matrix_string): + matrix = alignment.parse_rotation_matrix_from_string(matrix_string) + base_transform = alignment.get_alignment_matrix(matrix) + + # Determine which channels need alignment based on camera assignment + # Segmentation is Camera 1, so we only align when raw channel is Camera 2 + channels_need_alignment = {} + if wavelength_channels is not None and base_transform is not None: + for ch in channels_to_extract: + ch_camera = get_channel_camera( + ch, + wavelength_channels.get('bf'), + wavelength_channels.get('488'), + wavelength_channels.get('561'), + wavelength_channels.get('638') + ) + # Segmentation is Camera 1, raw channel is Camera 2 -> need alignment + # Use INVERSE transform: Camera 1 (seg) -> Camera 2 (raw) + if ch_camera == 2: + channels_need_alignment[ch] = base_transform.inverse + else: + # Same camera (Camera 1) or unknown -> no alignment + channels_need_alignment[ch] = None + else: + # Fallback: if no wavelength info, apply alignment to all channels (old behavior) + for ch in channels_to_extract: + channels_need_alignment[ch] = base_transform.inverse if base_transform else None + + # Determine timepoints to process (first 48 hours = 98 timepoints) + num_timepoints = raw_reader.dims.T + max_timepoint = min(num_timepoints, 98) df_result = [] - # We only process the first 48 hours (98 timepoints) - max_timepoint = int(np.min([98, df_movie['Image Size T'].values[0]])) - - for frame in tqdm(range(max_timepoint), total=max_timepoint): - raw_img = raw_reader.get_image_dask_data("ZYX", C=1, T=frame) - raw_img = raw_img.compute() - - seg_img = seg_reader.get_image_dask_data("ZYX", T=frame) - seg_img = seg_img.compute() - - if align: - matrix_string = df_movie["Dual Camera Alignment Matrix Value"].values[0] - matrix = alignment.parse_rotation_matrix_from_string(matrix_string) - transform = alignment.get_alignment_matrix(matrix) - transform = transform.inverse - + + for frame in range(max_timepoint): + # Load segmentation for this frame + seg_img = load_image_with_retry(seg_reader.get_image_dask_data("ZYX", T=frame)) + + # Load raw images for all channels we need + raw_imgs = {} + for ch in channels_to_extract: + raw_imgs[ch] = load_image_with_retry(raw_reader.get_image_dask_data("ZYX", C=ch, T=frame)) + for z, seg in enumerate(seg_img): - if align: - seg = alignment.align_image(seg, transform) + # Get the transform for the main channel + main_ch_transform = channels_need_alignment.get(main_channel) + + # Apply alignment if needed for main channel + if main_ch_transform is not None: + seg_aligned = alignment.align_image(seg, main_ch_transform) + else: + seg_aligned = seg - mask = np.bool_(seg) + mask = np.bool_(seg_aligned) area = np.count_nonzero(mask) - mean_intensity = np.mean(raw_img[z][mask]) - total_intensity = np.sum(raw_img[z][mask]) + + # Extract intensity for main channel (Channel 0 for single-channel, Channel 1 otherwise) + masked_values = raw_imgs[main_channel][z][mask] + mean_intensity = np.mean(masked_values) if area > 0 else np.nan + total_intensity = np.sum(masked_values) if area > 0 else np.nan + + # For fixed cells, use fixation time to calculate timepoint + if is_fixed_cell and fixation_time_hours is not None and not pd.isna(fixation_time_hours): + timepoint = int(fixation_time_hours * 60 / interval_minutes) + else: + timepoint = frame row = { "Z plane": z, - "Timepoint": frame, + "Timepoint": timepoint, "Data ID": movie_id, "Mean intensity per Z": mean_intensity, "Total intensity per Z": total_intensity, "Area of all cells mask per Z (pixels)": area } + + # Extract intensity for Channel 2 if available + if 2 in raw_imgs: + # Check if Channel 2 needs different alignment than main channel + ch2_transform = channels_need_alignment.get(2) + if ch2_transform != main_ch_transform: + # Need different alignment for this channel + if ch2_transform is not None: + seg_ch2 = alignment.align_image(seg, ch2_transform) + else: + seg_ch2 = seg + mask_ch2 = np.bool_(seg_ch2) + area_ch2 = np.count_nonzero(mask_ch2) + masked_values_ch2 = raw_imgs[2][z][mask_ch2] + row["Mean intensity per Z (Channel 2)"] = np.mean(masked_values_ch2) if area_ch2 > 0 else np.nan + row["Total intensity per Z (Channel 2)"] = np.sum(masked_values_ch2) if area_ch2 > 0 else np.nan + else: + # Same alignment as main channel, reuse mask + masked_values_ch2 = raw_imgs[2][z][mask] + row["Mean intensity per Z (Channel 2)"] = np.mean(masked_values_ch2) if area > 0 else np.nan + row["Total intensity per Z (Channel 2)"] = np.sum(masked_values_ch2) if area > 0 else np.nan + + # Extract intensity for Channel 3 if available + if 3 in raw_imgs: + # Check if Channel 3 needs different alignment than main channel + ch3_transform = channels_need_alignment.get(3) + if ch3_transform != main_ch_transform: + # Need different alignment for this channel + if ch3_transform is not None: + seg_ch3 = alignment.align_image(seg, ch3_transform) + else: + seg_ch3 = seg + mask_ch3 = np.bool_(seg_ch3) + area_ch3 = np.count_nonzero(mask_ch3) + masked_values_ch3 = raw_imgs[3][z][mask_ch3] + row["Mean intensity per Z (Channel 3)"] = np.mean(masked_values_ch3) if area_ch3 > 0 else np.nan + row["Total intensity per Z (Channel 3)"] = np.sum(masked_values_ch3) if area_ch3 > 0 else np.nan + else: + # Same alignment as main channel, reuse mask + masked_values_ch3 = raw_imgs[3][z][mask] + row["Mean intensity per Z (Channel 3)"] = np.mean(masked_values_ch3) if area > 0 else np.nan + row["Total intensity per Z (Channel 3)"] = np.sum(masked_values_ch3) if area > 0 else np.nan + df_result.append(row) - + + # Create DataFrame and add metadata df_result = pd.DataFrame(df_result) - df_result["Gene"] = df_movie.Gene.values[0] - df_result["Experimental Condition"] = df_movie["Experimental Condition"].values[0] - df_result.to_csv(out_fn) + df_result["Gene"] = gene + df_result["Experimental Condition"] = experimental_condition + df_result.to_csv(out_fn, index=False) -if __name__ == '__main__': - base_results_dir = io.setup_base_directory_name("feature_extraction") - compute_bf_colony_features_all_movies(output_folder=base_results_dir) + return (movie_id, True, "success") + + except Exception as e: + return (movie_id, False, f"{type(e).__name__}: {str(e)}") + +def compute_bf_colony_features_all_movies(output_folder, align=True, n_jobs=32): + ''' + Computes area of the bright field colony mask at every z position + and extracts corresponding intensity values from the fluorescence + channels. Extracts Channel 1 (main), and Channel 2/3 if available. + + Parameters + ---------- + output_folder : path + Folder path where feature csv for each movie is stored + align : bool + Enable alignment of the image using the barcode of the movie + n_jobs : int + Number of parallel jobs. Default: 32. + ''' + + df = io.load_imaging_and_segmentation_dataset() + print(f"Dataset loaded. Shape: {df.shape}.") + + # Prepare arguments for parallel processing + movie_args = [] + for movie_id, df_movie in df.groupby('Data ID'): + raw_path = df_movie["Raw File URL"].values[0] + seg_path = df_movie["All Cells Mask URL"].values[0] + matrix_string = df_movie["Dual Camera Alignment Matrix Value"].values[0] + gene = df_movie["Gene"].values[0] + experimental_condition = df_movie["Experimental Condition"].values[0] + # Get fixation info for immunostaining experiments + fixation_status = df_movie["Fixation Status"].values[0] if "Fixation Status" in df_movie.columns else None + fixation_time_hours = df_movie["Time Of Fixation Post EMT Induction (In Hours)"].values[0] if "Time Of Fixation Post EMT Induction (In Hours)" in df_movie.columns else None + timelapse_interval = df_movie["Timelapse Interval"].values[0] if "Timelapse Interval" in df_movie.columns else None + # Get wavelength-to-channel mapping for camera assignment + # Camera 1: Brightfield, 638nm | Camera 2: 488nm, 561nm + wavelength_channels = { + 'bf': df_movie["Brightfield Channel Number In The raw File"].values[0] if "Brightfield Channel Number In The raw File" in df_movie.columns else None, + '488': df_movie["488 wavelength Channel Number In The raw File"].values[0] if "488 wavelength Channel Number In The raw File" in df_movie.columns else None, + '561': df_movie["561 Wavelength Channel Number In The raw File"].values[0] if "561 Wavelength Channel Number In The raw File" in df_movie.columns else None, + '638': df_movie["638 wavelength Channel Number In The raw File"].values[0] if "638 wavelength Channel Number In The raw File" in df_movie.columns else None, + } - + movie_args.append(( + movie_id, raw_path, seg_path, matrix_string, + gene, experimental_condition, output_folder, align, + fixation_status, fixation_time_hours, timelapse_interval, + wavelength_channels + )) + + print(f"Processing {len(movie_args)} movies with {n_jobs} parallel jobs...") + + # Process movies in parallel + results = Parallel(n_jobs=n_jobs, verbose=10)( + delayed(_process_single_movie)( + movie_id, raw_path, seg_path, matrix_string, + gene, experimental_condition, output_folder, align, + fixation_status, fixation_time_hours, timelapse_interval, + wavelength_channels + ) + for movie_id, raw_path, seg_path, matrix_string, + gene, experimental_condition, output_folder, align, + fixation_status, fixation_time_hours, timelapse_interval, + wavelength_channels in movie_args + ) + + # Report results + success_count = sum(1 for _, success, _ in results if success) + failed = [(movie_id, msg) for movie_id, success, msg in results if not success and msg != "already exists"] + skipped = [(movie_id, msg) for movie_id, success, msg in results if success and msg == "already exists"] + + print(f"\n=== PROCESSING COMPLETE ===") + print(f"Successful: {success_count}") + print(f"Skipped (already exists): {len(skipped)}") + print(f"Failed: {len(failed)}") + + if failed: + print("\nFailed movies:") + for movie_id, msg in failed: + print(f" - {movie_id}: {msg}") + +if __name__ == '__main__': + base_results_dir = io.setup_base_directory_name("feature_extraction") + compute_bf_colony_features_all_movies(output_folder=base_results_dir) diff --git a/EMT_data_analysis/tools/io.py b/EMT_data_analysis/tools/io.py index 26875df..a930702 100644 --- a/EMT_data_analysis/tools/io.py +++ b/EMT_data_analysis/tools/io.py @@ -5,8 +5,35 @@ def convert_to_windows_path(linux_path: Path): return PurePosixPath(linux_path) -def load_imaging_and_segmentation_dataset(): - df = pd.read_csv("https://allencell.s3.amazonaws.com/aics/emt_timelapse_dataset/manifests/imaging_and_segmentation_data.csv") +def load_imaging_and_segmentation_dataset(load_from_aws: bool = True, local_path: str = None): + """ + Load the imaging and segmentation dataset. + + Parameters + ---------- + load_from_aws : bool, default True + If True, load from AWS S3. If False, load from local file. + local_path : str, optional + Path to local CSV file. If not provided and load_from_aws=False, + will look for 'imaging_and_segmentation_data.csv' in the project root. + + Returns + ------- + df : DataFrame + The imaging and segmentation dataset + """ + if load_from_aws: + path = "https://allencell.s3.amazonaws.com/aics/emt_timelapse_dataset/manifests/imaging_and_segmentation_data.csv" + else: + if local_path is not None: + path = local_path + else: + # Default local path: project root (parent of EMT_data_analysis package) + project_root = Path(__file__).parent.parent.parent + path = project_root / "imaging_and_segmentation_data.csv" + print(f'Loading from local file: {path}') + + df = pd.read_csv(path) n_movies = df['Data ID'].nunique() print(f'Total number of movies in the dataset: {n_movies}') return df diff --git a/README.md b/README.md index 9cbb31c..6333251 100755 --- a/README.md +++ b/README.md @@ -7,13 +7,13 @@ This repository contains code for reproducing the plots shown in our manuscript This code has been tested on Ubuntu 18.04.2 LTS and Windows 10 using Python 3.11. # Installation -1. Install python 3.11 and pip>=24.0.0. +1. Install Python 3.11 and pip >= 24.0.0. 2. Install the dependencies for [lxml](https://lxml.de/installation.html). On Ubuntu or Debian: ```bash sudo apt-get install libxml2-dev libxslt-dev python-dev ``` -2. Create a new virtual environment. +3. Create a new virtual environment and install dependencies: ```bash python -m venv venv source venv/bin/activate @@ -22,58 +22,78 @@ pip install -r requirements.txt (Alternatively, if you have `pdm`, you can run `pdm sync`.) -# How to run: +# How to run -## 1 - Feature extraction +The analysis pipeline consists of four sequential steps. Steps 1-3 generate intermediate data, while Step 4 produces the final figures and statistical analysis. Pre-computed outputs from Steps 1-3 are available on [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/), so Step 4 can be run directly without executing the preceding steps. -Run: `python EMT_data_analysis/analysis_scripts/Feature_extraction.py` +## Step 1 — Feature extraction -This will generate one CSV for each movie with the extracted features. CSVs are stored in the folder `EMT_data_analysis/results/feature_extraction` +```bash +python EMT_data_analysis/analysis_scripts/Feature_extraction.py +``` -## 2 - Metric computation +Extracts per-Z-plane features from each movie: colony mask area and fluorescence intensity (main channel plus additional channels when available). Movies are processed in parallel using `joblib`. Each movie produces a CSV stored in `EMT_data_analysis/results/feature_extraction/`. -Run: `python EMT_data_analysis/analysis_scripts/Metric_computation.py` +**Dual-camera alignment**: The imaging system uses two cameras (Camera 1: brightfield + 638 nm; Camera 2: 488 nm + 561 nm). Since the all-cells segmentation mask is derived from brightfield (Camera 1), the mask is aligned to Camera 2 coordinates using the dual-camera calibration matrix before extracting intensity from 488/561 nm channels. Channels on the same camera as the mask do not require alignment. -This will generate a single CSV containing information about all the movies to be used for analysis. The manifest is saved as `EMT_data_analysis/results/metric_computation/Image_analysis_extracted_features.csv`. +## Step 2 — Metric computation -## 3 - Nuclei localization +```bash +python EMT_data_analysis/analysis_scripts/Metric_computation.py +``` -Run: `python EMT_data_analysis/analysis_scripts/Nuclei_localization.py` +Compiles per-movie CSVs from Step 1 into a single manifest and computes gene-specific expression metrics: +- **SOX2**: Time of half-maximal expression (first timepoint where smoothed intensity drops to 50% of dynamic range) +- **TBXT**: Time of maximum expression (peak of smoothed intensity curve) +- **EOMES**: Time of maximum expression (peak of smoothed intensity curve) +- **CDH1**: Time of inflection of E-cadherin expression (minimum of second derivative of smoothed intensity) -This will generate CSV for individual nuclei classified as inside the basement memebrane or not over the course of the timelapse for EOMES and H2B movies. The manifest is saved as `EMT_data_analysis/results/nuclei_localization/Migration_timing_trough_mesh_extracted_feature.csv`. +Output: `EMT_data_analysis/results/metric_computation/Image_analysis_extracted_features.csv` -## 4 - Analysis Plots +## Step 3 — Nuclei localization + +```bash +python EMT_data_analysis/analysis_scripts/Nuclei_localization.py +``` + +Classifies individual nuclei as inside or outside the collagen IV basement membrane mesh at each timepoint. Nuclear centroids from 3D instance segmentation are tested against the mesh boundary using ray-casting. + +Output: `EMT_data_analysis/results/nuclei_localization/Migration_timing_trough_mesh_extracted_feature.csv` + +## Step 4 — Analysis and figure generation + +```bash +python EMT_data_analysis/analysis_scripts/Analysis_tools.py +``` -Run: `python EMT_data_analysis/analysis_scripts/Analysis_tools.py` +Generates all manuscript figures and statistical analyses. By default, input manifests are automatically downloaded from [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/), so this step can be run independently of Steps 1-3. -This will generate the plots in the manuscript and store them in `results/figures` folder. The manifests used as inputs in this workflow are automatically downloaded from [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/) by default. +Output: `EMT_data_analysis/results/figures/` -## 5 - [Optional] 3D Example Rendering +## Optional — 3D example rendering -The functions in `EMT_data_analysis/figure_generation` can be used to generate 3D renderings shown in the paper. Functions have only been tested on Ubuntu 18.04/22.04 +The functions in `EMT_data_analysis/figure_generation/` can be used to generate 3D renderings shown in the paper. These have only been tested on Ubuntu 18.04/22.04. On Ubuntu or Debian: ```bash sudo apt-get install xvfb libgl1-mesa-glx ``` -On Windows: +On Windows: Comment out any instance of `pv.start_xvfb()` in the code before running. ### All Cells Mask -Run ```bash python EMT_data_analysis/figure_generation/colony_mask.py --data_id [Optional] --output_directory [Optional] ``` If no input arguments are provided, the code will default to the data shown in the paper and output results to `EMT_data_analysis/results/3D_all_cells_mask`. -Data ID values are only valid inputs if they have a none-empty value for `All Cells Mask File Download` in the `image_and_segmentation_data.csv` manifest on [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/) +Data ID values are only valid inputs if they have a non-empty value for `All Cells Mask File Download` in the `image_and_segmentation_data.csv` manifest on [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/). ### Inside-Outside Classification -Run ```bash python EMT_data_analysis/figure_generation/inside-outside_classification.py --data_id [Optional] --output_directory [Optional] ``` If no input arguments are provided, the code will default to the data shown in the paper and output results to `EMT_data_analysis/results/Inside-Outside/mesh-figures`. -Data ID values are only valid inputs if they have a none-empty value for `CollagenIV Segmentation Mesh Folder` in the `image_and_segmentation_data.csv` manifest on [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/) +Data ID values are only valid inputs if they have a non-empty value for `CollagenIV Segmentation Mesh Folder` in the `image_and_segmentation_data.csv` manifest on [AWS](https://open.quiltdata.com/b/allencell/tree/aics/emt_timelapse_dataset/manifests/). # Contact diff --git a/pdm.lock b/pdm.lock index 42ac16b..26e79ee 100644 --- a/pdm.lock +++ b/pdm.lock @@ -5,107 +5,78 @@ groups = ["default", "internal"] strategy = ["inherit_metadata"] lock_version = "4.5.0" -content_hash = "sha256:5f64c6a03611318ab5cd1db24294e634d1ce935963ad49c918b01d4d462c60b4" +content_hash = "sha256:947325f5d265fa9247b330f91bbdd1a2aa3a28bc928e955229149e7e3541e485" [[metadata.targets]] requires_python = ">=3.11,<3.12" [[package]] name = "aiobotocore" -version = "2.25.1" +version = "3.1.1" requires_python = ">=3.9" summary = "Async client for aws services using botocore and aiohttp" groups = ["default"] dependencies = [ - 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dependencies = [ "rtree>=1.4.1", "scikit_posthocs>=0.9.0", "kaleido>=0.2.1,!=0.2.1.post1,<0.4.0", # kaleido 0.2.1.post1 is published incorrectly + "joblib>=1.3.0", "quilt3>=6.0.0", ] requires-python = ">=3.11,<3.12" @@ -36,4 +37,7 @@ internal = [ ] +[tool.setuptools.packages.find] +include = ["EMT_data_analysis*"] + [tool.pdm] diff --git a/requirements.txt b/requirements.txt index d1108cb..f8633ae 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,393 +1,226 @@ # This file is @generated by PDM. # Please do not edit it manually. -aiobotocore==2.25.1 \ - --hash=sha256:ea9be739bfd7ece8864f072ec99bb9ed5c7e78ebb2b0b15f29781fbe02daedbc \ - --hash=sha256:eb6daebe3cbef5b39a0bb2a97cffbe9c7cb46b2fcc399ad141f369f3c2134b1f -aiohttp==3.9.5 \ - --hash=sha256:0a158704edf0abcac8ac371fbb54044f3270bdbc93e254a82b6c82be1ef08f3c \ - --hash=sha256:0cbf56238f4bbf49dab8c2dc2e6b1b68502b1e88d335bea59b3f5b9f4c001475 \ - --hash=sha256:1732102949ff6087589408d76cd6dea656b93c896b011ecafff418c9661dc4ed \ - 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--hash=sha256:a4e12a3bd099cd19dc2b2e9fe01a807131b46ebd0f83f509bda3cb243e988c32 +aiohappyeyeballs==2.6.1 \ + --hash=sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558 \ + --hash=sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8 +aiohttp==3.13.3 \ + --hash=sha256:1cb93e166e6c28716c8c6aeb5f99dfb6d5ccf482d29fe9bf9a794110e6d0ab64 \ + --hash=sha256:28e027cf2f6b641693a09f631759b4d9ce9165099d2b5d92af9bd4e197690eea \ + --hash=sha256:2be0e9ccf23e8a94f6f0650ce06042cefc6ac703d0d7ab6c7a917289f2539ad4 \ + --hash=sha256:3b61b7169ababd7802f9568ed96142616a9118dd2be0d1866e920e77ec8fa92a \ + --hash=sha256:5b179331a481cb5529fca8b432d8d3c7001cb217513c94cd72d668d1248688a3 \ + --hash=sha256:5b6073099fb654e0a068ae678b10feff95c5cae95bbfcbfa7af669d361a8aa6b \ + --hash=sha256:5d2d94f1f5fcbe40838ac51a6ab5704a6f9ea42e72ceda48de5e6b898521da51 \ + --hash=sha256:642f752c3eb117b105acbd87e2c143de710987e09860d674e068c4c2c441034f \ + 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--hash=sha256:620bd241acc0bbb9ec819f1ab215866871b4bbd1f73836a55f799200ee86950c -aiosignal==1.3.1 \ - --hash=sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc \ - --hash=sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17 +aiosignal==1.4.0 \ + --hash=sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e \ + --hash=sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7 annotated-types==0.7.0 \ --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 -attrs==23.2.0 \ - --hash=sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30 \ - --hash=sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1 -bioio==3.0.0 \ - --hash=sha256:8be25df70b611e62d1f1c3b11b47e039f3626e08e17e0dc792d824be3e4aeb89 \ - --hash=sha256:c8a796d2ddf0b60fb240603c51c6b7e48fa1b3d8d2da9ade137f67d04f0707c0 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--hash=sha256:da699831647845751eb752fed0c75ed9a78a9198552241192bc3ec40e7091e88 +bioio==3.2.0 \ + --hash=sha256:731211b43b3c5438cbf597f915a98dcd1be29814b0a67e5a1e89ab38fe746538 \ + --hash=sha256:b25b424de47b877de184c14b0d0836f2a9c678b0c36a338eb6e526f0f74fa359 +bioio-base==3.2.0 \ + --hash=sha256:ae37c33bfc5fe7efa89603ea678c6ba1a2e0b9c67a7e64377d4c5d1f0a75724a \ + --hash=sha256:f935e19ae243e9cdb1c46ae9c5ac2a19343345f6be7a74255e292c7188a37c68 bioio-ome-tiff==1.4.0 \ --hash=sha256:0073ff612926ea17c72a32f378d62361e282dd7afbc561e8c0a53316fd8d2e29 \ --hash=sha256:98107a847244a7b515c7a8c39b5119e67f4047b63661238b49b3d0cc36a86cd9 -bioio-ome-zarr==3.0.3 \ - --hash=sha256:26a2953e365f22583c70309e34b100bc91289b145094ee56c00d1c1aef956fcf \ - --hash=sha256:c6b53aff2dde4d5f69222300d39fd9c161a05f5da8641f160e61fe4b4182b7ee -boto3==1.40.61 \ - --hash=sha256:6b9c57b2a922b5d8c17766e29ed792586a818098efe84def27c8f582b33f898c \ - --hash=sha256:d6c56277251adf6c2bdd25249feae625abe4966831676689ff23b4694dea5b12 -botocore==1.40.61 \ - --hash=sha256:17ebae412692fd4824f99cde0f08d50126dc97954008e5ba2b522eb049238aa7 \ - --hash=sha256:a2487ad69b090f9cccd64cf07c7021cd80ee9c0655ad974f87045b02f3ef52cd -certifi==2024.7.4 \ - --hash=sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b \ - --hash=sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 +bioio-ome-zarr==3.2.1 \ + --hash=sha256:54180e10f4df7b2bddcb8468d05c3d6e7f2b3d3690f4f2ef9d68e0463ac1c9ea \ + --hash=sha256:ef143329de73b88229db03ad3ceb84399622c5fabf2b911b34c64f8a8a4363fd +boto3==1.42.30 \ + --hash=sha256:ba9cd2f7819637d15bfbeb63af4c567fcc8a7dcd7b93dd12734ec58601169538 \ + --hash=sha256:d7e548bea65e0ae2c465c77de937bc686b591aee6a352d5a19a16bc751e591c1 +botocore==1.42.30 \ + --hash=sha256:97070a438cac92430bb7b65f8ebd7075224f4a289719da4ee293d22d1e98db02 \ + --hash=sha256:9bf1662b8273d5cc3828a49f71ca85abf4e021011c1f0a71f41a2ea5769a5116 +certifi==2026.1.4 \ + --hash=sha256:9943707519e4add1115f44c2bc244f782c0249876bf51b6599fee1ffbedd685c \ + --hash=sha256:ac726dd470482006e014ad384921ed6438c457018f4b3d204aea4281258b2120 cgal==6.0.1.post202410241521 \ --hash=sha256:151156aabdb6c95e962d7b1017157faf606e301882278e726a620a7095342b48 \ --hash=sha256:1ebe41ebd1b427f7d7baabbb88f081b40c7b151c1093c3b2699590b7d3a21b52 \ --hash=sha256:71519601772dcc642247ddc721764f0154badab0e819b09462b3a1e01eb464a7 \ --hash=sha256:7a7eb61238be20f6bee11e13dfcba0e7dbbdceb8b87b125b3497f5303de1e058 -charset-normalizer==3.3.2 \ - --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \ - --hash=sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8 \ - --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \ - --hash=sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185 \ - --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \ - 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