Features ======== PyCAT is a comprehensive Python toolkit for analyzing biomolecular condensates and bioimages. It offers robust analysis methods, customizable pipelines, and visualization tools for in-depth biological insights. This page is a reference for features and tools available in PyCAT. Analysis Methods Available -------------------------- PyCAT offers a wide range of analysis methods for quantifying bio-images. **Region Properties Analysis** : Measure area, intensity, shape, texture, and other features of ROIs (regions of interest) like condensates, cells, etc. **Object-Based Colocalization Analysis** : Object ROI mask comparisons for segmented object masks * Mander's M1 value * Mander's M2 value * Jaccard Index * Sorensen-Dice Coefficient * Calculate Distance Between Objects * Percent of Objects Non-coincident * Mander's Colocalization Coefficient **Pixel-Wise Correlation Analysis** : Correlation Analysis for images without distinctive/segmentable objects * Pearson's R value * Spearman's R value * Kendall's Tau value * Weighted Tau value * Li's ICQ value * Mander's Overlap Coefficient * Mander's k1 value * Mander's k2 value **Modified Costes Analysis** : Automatically generated thresholds and statistical significance testing for correlation analyses * Costes Automatic Thresholded M1 M2 * Calculate Costes Significance * Perform Modified Costes Thresholding **Correlation Function Analysis** : Auto- and Cross-Correlation Functions with Gaussian fits * Fitted gaussian parameters (mu, sigma, etc) Analysis Pipelines ------------------ PyCAT also provides several pre-configured analysis pipelines for common use cases. **Condensate Analysis** : Tailored analysis for in-cellulo condensates **Colocalization Analysis** : Object-based and pixel-wise correlation pipelines **General ROI Analysis** : Exploratory pipeline with region property measurements **Fibril Analysis** : Analyze beta-amyloid fibers and fibril structures Toolbox Reference ----------------- This reference table lists all of the individual functions available in the PyCAT toolbox along with brief descriptions. .. list-table:: Toolbox Functions :widths: 30 70 :header-rows: 1 * - Function Category - Description * - **Image Processing Tools** - Tools for image pre-processing, enhancement, and noise reduction. * - Pre-processing - Applies top hat, LoG, and other image processing filters. * - Rescale Intensity - Rescales image intensity values to a desired range. * - Invert Intensity - Inverts pixel intensity values (e.g., dark to bright). * - Upscale Image - Increases image resolution while preserving structural features. * - RB Gauss BG removal - Rolling ball and Gaussian background removal. * - BG removal with edge enhancement - Removes background while enhancing peaks and edges through Gabor filtering. * - WBNS - Wavelet Background and Noise Subtraction. * - Wavelet noise reduction - Reduces noise using wavelet transforms for multi-scale denoising. * - Bilateral noise reduction - Smoothens images while preserving edges through bilateral filtering. * - CLAHE - Contrast-Limited Adaptive Histogram Equalization for local contrast enhancement. * - Peak and Edge enhancement - Enhances image peaks and edges via Gabor and LoG filtering. * - Morphological Gaussian filter - Smoothens images while applying morphological opening and Gaussian filtering. * - LoG enhancement - Laplacian-of-Gaussian enhancement for edge detection. * - Deblur by Pixel Reassignment - An advanced PSF and deblurring tool which upscales images in the process. .. list-table:: :widths: 30 70 :header-rows: 1 * - **Image Segmentation Tools** - Tools for segmenting images into meaningful regions of interest (ROIs). * - Local Thresholding functions - Performs segmentation using local thresholding algorithms. * - Cellpose segmentation - Uses Cellpose, a deep learning-based method for cell segmentation. * - Random Forest classifier segmentation - Segments images using a Random Forest classifier model. * - Felzenszwalb segmentation and RAG region merging - Segments images using graph-based Felzenszwalb algorithms and merges regions. .. list-table:: :widths: 30 70 :header-rows: 1 * - **Label and Mask Tools** - Tools for working with binary masks and labeled mask regions. * - Binary Mask Morphological Operations - Applies morphological operations (e.g., dilation, erosion) to binary masks. * - Measure Binary Mask - Extracts measurements (area, intensity) from binary masks. * - Label Binary Mask - Assigns unique labels to connected components in binary masks. * - Convert Labels to Mask - Converts labeled regions into binary masks. * - Update Labels - Updates or modifies labels in labeled images. * - Region Props on Labeled Mask - Computes region properties (e.g., area, eccentricity) for labeled regions. .. list-table:: :widths: 30 70 :header-rows: 1 * - **Layer Operations** - Tools for managing and merging image layers. * - Simple Multi-layer merging - Merges multiple image layers into a single layer. * - Advanced 2-layer merging - Performs advanced merging operations between two image layers. .. list-table:: :widths: 30 70 :header-rows: 1 * - **Colocalization and Correlation Tools** - Tools for analyzing colocalization and correlation between regions or channels. * - Object-Based Colocalization - Performs object-based analysis with metrics like Manders, Sorensen-Dice, and object distances. * - Pixel-Wise Correlation - Computes pixel-wise correlations using Pearson, Weighted Tau, Li’s ICA, and other metrics. * - Correlation Function (ACF, CCF) - Calculates Auto-Correlation (ACF) and Cross-Correlation Functions (CCF). .. list-table:: :widths: 30 70 :header-rows: 1 * - **Data Visualization** - Tools for visualizing analysis results. * - Plotting Widget - Interactive widget for plotting and visualizing analysis outputs. Cell Analysis Features ---------------------- This table lists the features measured in the cell_df data structure when using the condensate analysis pipeline and the cell analyzer and condensate analyzer functions. .. list-table:: Features Measured in cell_df :widths: 20 80 :header-rows: 1 * - Feature - Description * - label - Unique identifier for each object (cell or ROI). * - area - Total area of the object in pixels. * - intensity_mean - Mean intensity of the object. * - axis_major_length - Length of the major axis of the object’s fitted ellipse. * - axis_minor_length - Length of the minor axis of the object’s fitted ellipse. * - eccentricity - Deviation from circularity; 0 for a circle, 1 for a line. * - perimeter - Perimeter length of the object in pixels. * - intensity_std_dev - Standard deviation of the object's intensity. * - intensity_median - Median intensity value of the object. * - intensity_total - Total summed intensity of the object. * - cell_micron_area - Object area in square microns, based on image resolution. * - image_resolution_um_per_px_sq - Image resolution in (um/px)^2 * - cell_snr - Signal-to-noise ratio: mean cell intensity / std dev of background (non-cell) intensity. * - gaussian_snr_estimate - Gaussian SNR: mean cell intensity / Gaussian background noise estimate. * - contrast - Contrast measure for the object region. * - dissimilarity - Texture-based measure of intensity dissimilarity. * - homogeneity - Texture-based measure of regional intensity uniformity. * - ASM - Angular Second Moment; measures image uniformity. * - energy - Energy metric derived from the image region. * - correlation - Correlation measure between neighboring pixel intensities. * - 32_bit_entropy - Entropy of the object calculated from 32-bit float intensities. * - 8_bit_entropy - Entropy of the object calculated from 8-bit unsigned integer intensities. * - 8_bit_entropy_img_avg - Average entropy of the 8-bit image. * - img_kurtosis - Measure of the "tailedness" of the intensity distribution. * - standardized_sixth_moment - Standardized sixth moment of the intensity distribution. * - kurtosis_z_score - Z-score for kurtosis, indicating deviation from normality. * - p_val - p-value for statistical significance of kurtosis. * - lbp_mean - Mean of the local binary pattern (LBP) features. * - lbp_std - Standard deviation of LBP features. * - lbp_entropy - Entropy of the LBP features. * - puncta_micron_area_mean - Mean size of puncta within a cell, in square microns. * - puncta_micron_area_std - Standard deviation of puncta sizes within a cell, in square microns. * - puncta_ellipticity_mean - Mean ellipticity of all puncta within a cell. * - puncta_intensity_total - Total intensity: mean intensity * area of all puncta. * - puncta_intensity_dist_mean - Mean intensity of all puncta within the cell. * - number_of_puncta - Total number of puncta detected within the cell. * - cell_xor_puncta_int_mean - Mean intensity of the region inside the cell, excluding puncta regions. * - cell_xor_puncta_int_std - Standard deviation of intensity in the cell region, excluding puncta ROIs. * - cell_xor_puncta_int_total - Total intensity of the region inside the cell, excluding puncta ROIs. * - cell_xor_puncta_area - Area of the region inside the cell, excluding puncta ROIs (square microns). * - snr_test - Signal-to-noise ratio: mean puncta intensity / std dev of dilute phase intensity. * - partition_test - Partition coefficient: puncta intensity mean / cell XOR puncta intensity mean. * - partition_test_total_int - Partition coefficient: puncta total intensity / cell XOR puncta total intensity. * - spark_score - Total puncta intensity / total cell intensity. * - puncta_classifier - Binary classifier for puncta presence: 1 (puncta), 0 (none). Puncta Analysis Features ------------------------ This table lists the features measured in the puncta_df data structure when using the condensate analysis pipeline and the condensate analyzer function. .. list-table:: Features Measured in puncta_df :widths: 20 80 :header-rows: 1 * - Feature - Description * - label - Unique identifier for each punctum (object). * - area - Total area of the punctum in pixels. * - intensity_mean - Mean intensity of the punctum. * - axis_major_length - Length of the major axis of the punctum’s fitted ellipse. * - axis_minor_length - Length of the minor axis of the punctum’s fitted ellipse. * - eccentricity - Deviation from circularity; 0 for a circle, 1 for a line. * - perimeter - Perimeter length of the punctum in pixels. * - ellipticity - Measure of elongation: `1 - axis_minor_length / axis_major_length`. Higher values indicate more elongated puncta. * - circularity - Measure of shape compactness, normalized for irregular shapes: `4 * pi * area / (perimeter ** 2)`. Note: Normalized to account for the "coast of England" fractal paradox, where irregular shapes cause perimeter to scale non-linearly with area. * - micron area - Area of the punctum in square microns, based on image resolution. * - cell label - Label of the corresponding cell to which the punctum belongs.