Tools¶

import scimap as sm

Scimap provides a suite of tools to preprocess the data for subsequent analysis.

Function	Short Description
`sm.pp.mcmicro_to_scimap`	`mcmicro` output to scimap compatible object
`sm.pp.rescale`	Manual/Auto gate based scaling of data


`sm.tl.phenotype_cells`	Probability distribution based cell phenotyping
`sm.tl.cluster`	Cluster or sub-cluster single-cells using a variety of algorithms
`sm.tl.foldchange`	Compute foldchange in phenotypes between samples/ROIs
`sm.tl.spatial_distance`	Computes nearest distance between all phenotypes for every cell
`sm.tl.spatial_interaction`	cell–cell interactions analysis
`sm.tl.spatial_count`	Distribution of cell-types with local neighborhood
`sm.tl.spatial_expression`	Distribution of spatial expression with local neighborhood
`sm.tl.spatial_aggregate`	Aggregates of cell-types with local neighborhood
`sm.tl.spatial_pscore`	Scoring proximity between user defined cell types
`sm.tl.spatial_lda`	Latent Dirichlet Allocation (LDA) modelling for spatial motifs


`sm.pl.gate_finder`	Overlays gating positivity on the image for manual gating
`sm.pl.image_viewer`	Opens the image with overlays on a `napari` browser
`sm.pl.stacked_barplot`	Generate a stacked barplot from any two columns of categorical data
`sm.pl.pie`	Generate a pieplot of cell-type proportion or any categorical data
`sm.pl.foldchange`	vizualize foldchange in phenotypes between samples/ROIs
`sm.pl.voronoi`	Generate a voronoi diagram and color it with categorical data
`sm.pl.spatial_interaction`	Heatmap of cell–cell interaction analysis
`sm.pl.spatial_distance`	Visualize distance between phenotypes
`sm.pl.spatial_pscore`	Bar plot of the derived Spatial Proximity Scores


`sm.hl.classify`	Quickly classify cells based on pos/negativity of list of markers
`sm.hl.scimap_to_csv`	Export scimap object to CSV
`sm.hl.add_roi_omero`	Add ROI's extracted from Omero to Scimap object