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. |
Scimap
presents a broad spectrum of analytical tools, each engineered to extract nuanced insights from single-cell datasets through sophisticated algorithms.
Function |
Description |
sm.tl.phenotype_cells |
Leverages probability distribution models for precise cell phenotyping, enabling detailed characterization of cellular identities based on marker expressions. |
sm.tl.cluster |
Provides a flexible clustering framework to delineate cellular subpopulations using a range of algorithms, facilitating the discovery of previously unrecognized cell types. |
sm.tl.umap |
Applies the UMAP algorithm for dimensionality reduction, affording a more interpretable visualization of complex single-cell data landscapes. |
sm.tl.foldchange |
Calculates fold changes in phenotype expressions between samples or Regions of Interest (ROIs), enabling quantitative comparisons of cellular characteristics. |
sm.tl.spatial_distance |
Computes the nearest distances between all phenotypes for each cell, offering insights into spatial arrangements and distributions within tissue contexts. |
sm.tl.spatial_interaction |
Analyzes cell-cell interactions to uncover patterns of spatial organization and intercellular communication within microenvironments. |
sm.tl.spatial_count |
Evaluates the distribution of cell types within local neighborhoods, providing a quantitative measure of cellular diversity and density. |
sm.tl.spatial_lda |
Utilizes Latent Dirichlet Allocation (LDA) modeling to identify spatial motifs, facilitating the understanding of complex spatial patterns and their biological implications. |
sm.tl.spatial_expression |
Investigates the distribution of spatial expression patterns within local neighborhoods, offering insights into the spatial heterogeneity of gene expression. |
sm.tl.spatial_cluster |
Identifies clusters based on spatial expression patterns, enabling the elucidation of spatially defined cellular networks and communities. |
sm.tl.spatial_pscore |
Scores the proximity between predefined cell types, quantifying the spatial relationships and potential functional interactions between distinct cellular populations. |
sm.tl.spatial_aggregate |
Summarizes aggregates of cell types within local neighborhoods, providing a macroscopic view of cellular organization and tissue architecture. |
sm.tl.spatial_similarity_search |
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 |
sm.pl.image_viewer |
Integrates with napari to offer an interactive platform for enhanced image viewing and annotation with data overlays. |
sm.pl.addROI_image |
Facilitates the addition of Regions of Interest (ROIs) through napari , enriching spatial analyses with precise locational data. |
sm.pl.gate_finder |
Aids in the manual gating process by overlaying marker positivity on images, simplifying the identification and analysis of cellular subsets. |
sm.pl.heatmap |
Creates heatmaps to visually explore marker expression or feature distributions across different groups. |
sm.pl.markerCorrelation |
Computes and visualizes the correlation among selected markers. |
sm.pl.groupCorrelation |
Calculates and displays the correlation between the abundances of groups across user defined conditions. |
sm.pl.distPlot |
Generates distribution plots for specific markers, allowing for the visual comparison of marker expression across different conditions or cell types. |
sm.pl.densityPlot2D |
Creates two-dimensional density plots of marker expressions, facilitating the visualization of expression patterns and densities in a spatial context. |
sm.pl.cluster_plots |
Provides a meta-function that outputs a combination of UMAP, heatmap, and ranked markers for each group, offering a comprehensive view of clustering results. |
sm.pl.umap |
Overlays markers on UMAP projections, enhancing the interpretation of dimensional reduction analyses with annotated data points. |
sm.pl.foldchange |
Visualizes fold changes in phenotypes between samples or ROIs, enabling the graphical comparison of cellular expression profiles. |
sm.pl.spatial_scatterPlot |
Produces scatter plots of spatially resolved data, illustrating the distribution and organization of cells within tissue sections. |
sm.pl.spatial_distance |
Visualizes the spatial distances between phenotypes, providing insights into the physical separation and clustering of cell types. |
sm.pl.spatial_interaction |
Displays heatmaps of cell-cell interaction analyses, highlighting the complex interplay between different cellular populations. |
sm.pl.spatialInteractionNetwork |
Displays cell-cell interaction analyses as a Network plot. |
sm.pl.spatial_pscore |
Generates bar plots of Spatial Proximity Scores, quantifying and visualizing the proximity between selected cell types within a spatial context. |
sm.pl.stacked_barplot |
Creates stacked barplots from any two columns of categorical data, offering a clear visualization of proportions and relationships between categories. |
sm.pl.pie |
Produces pie charts to represent the proportions of cell types or any categorical data, facilitating the quick assessment of composition within datasets. |
sm.pl.voronoi |
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. |