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import scimap as sm

Preprocessing (pp)

Scimap offers an array of preprocessing functions, meticulously designed to optimize high-dimensional datasets for advanced analysis.

Function Detailed Description
sm.pp.mcmicro_to_scimap Facilitates the transformation of mcmicro outputs into scimap-compatible formats, enabling the integration of microscopy data for comprehensive analysis.
sm.pp.log1p Applies a log1p transformation to the raw data within an AnnData object.
sm.pp.rescale Employs both manual and automated gating-based strategies for data rescaling, enhancing measurement scale sensitivity and dynamic range for optimal data representation.
sm.pp.combat Implements an advanced batch correction algorithm to effectively neutralize batch-related variabilities, ensuring data consistency across different experimental batches.

Tools (tl)

Scimap presents a broad spectrum of analytical tools, each engineered to extract nuanced insights from single-cell datasets through sophisticated algorithms.

Function Description Leverages probability distribution models for precise cell phenotyping, enabling detailed characterization of cellular identities based on marker expressions. Provides a flexible clustering framework to delineate cellular subpopulations using a range of algorithms, facilitating the discovery of previously unrecognized cell types. Applies the UMAP algorithm for dimensionality reduction, affording a more interpretable visualization of complex single-cell data landscapes. Calculates fold changes in phenotype expressions between samples or Regions of Interest (ROIs), enabling quantitative comparisons of cellular characteristics. Computes the nearest distances between all phenotypes for each cell, offering insights into spatial arrangements and distributions within tissue contexts. Analyzes cell-cell interactions to uncover patterns of spatial organization and intercellular communication within microenvironments. Evaluates the distribution of cell types within local neighborhoods, providing a quantitative measure of cellular diversity and density. Utilizes Latent Dirichlet Allocation (LDA) modeling to identify spatial motifs, facilitating the understanding of complex spatial patterns and their biological implications. Investigates the distribution of spatial expression patterns within local neighborhoods, offering insights into the spatial heterogeneity of gene expression. Identifies clusters based on spatial expression patterns, enabling the elucidation of spatially defined cellular networks and communities. Scores the proximity between predefined cell types, quantifying the spatial relationships and potential functional interactions between distinct cellular populations. Summarizes aggregates of cell types within local neighborhoods, providing a macroscopic view of cellular organization and tissue architecture. Searches for regions within and across images that exhibit similar spatial patterns, aiding in the identification of recurring spatial motifs and their biological relevance.

Plotting (pl)

Scimap incorporates a comprehensive suite of plotting functions designed for the intuitive visualization and interpretation of spatial and phenotypic data.

Function Description Integrates with napari to offer an interactive platform for enhanced image viewing and annotation with data overlays. Facilitates the addition of Regions of Interest (ROIs) through napari, enriching spatial analyses with precise locational data. Aids in the manual gating process by overlaying marker positivity on images, simplifying the identification and analysis of cellular subsets. Creates heatmaps to visually explore marker expression or feature distributions across different groups. Computes and visualizes the correlation among selected markers. Calculates and displays the correlation between the abundances of groups across user defined conditions. Generates distribution plots for specific markers, allowing for the visual comparison of marker expression across different conditions or cell types. Creates two-dimensional density plots of marker expressions, facilitating the visualization of expression patterns and densities in a spatial context. Provides a meta-function that outputs a combination of UMAP, heatmap, and ranked markers for each group, offering a comprehensive view of clustering results. Overlays markers on UMAP projections, enhancing the interpretation of dimensional reduction analyses with annotated data points. Visualizes fold changes in phenotypes between samples or ROIs, enabling the graphical comparison of cellular expression profiles. Produces scatter plots of spatially resolved data, illustrating the distribution and organization of cells within tissue sections. Visualizes the spatial distances between phenotypes, providing insights into the physical separation and clustering of cell types. Displays heatmaps of cell-cell interaction analyses, highlighting the complex interplay between different cellular populations. Displays cell-cell interaction analyses as a Network plot. Generates bar plots of Spatial Proximity Scores, quantifying and visualizing the proximity between selected cell types within a spatial context. Creates stacked barplots from any two columns of categorical data, offering a clear visualization of proportions and relationships between categories. Produces pie charts to represent the proportions of cell types or any categorical data, facilitating the quick assessment of composition within datasets. Generates Voronoi diagrams colored by categorical data, providing a unique visual representation of spatial distributions and territories.

Helper Functions (hl)

Scimap also features a collection of helper functions designed to facilitate efficient data manipulation and enhance the analytical workflow.

Function Description
sm.hl.classify Streamlines the classification of cells based on the positivity or negativity of specified markers, simplifying the assignment of cellular identities.
sm.hl.rename Enables quick and efficient renaming within data columns through the application of dictionary-based mappings, enhancing data clarity and consistency.
sm.hl.addROI_omero Allows for the seamless integration of Regions of Interest (ROIs) extracted from Omero into scimap objects, bridging imaging and analytical platforms.
sm.hl.dropFeatures Provides a convenient function for subsetting the adata object by removing specified features, aiding in the focus on relevant data.
sm.hl.animate Creates animated scatter plots transitioning from embedding to physical location, offering dynamic visual insights into spatial data relationships.
sm.hl.merge_adata_obs Facilitates the merging of multiple AnnData objects, ensuring cohesive analysis across disparate datasets.
sm.hl.scimap_to_csv Enables the export of scimap objects to CSV format, providing flexibility in data sharing and further analysis with external tools.