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import scimap as sm

Preprocessing: pp

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

Tools: tl Dimensionality Reduction using UMAP Cluster or sub-cluster single-cells using a variety of algorithms Compute foldchange in phenotypes between samples/ROIs Probability distribution based cell phenotyping Aggregates of cell-types with local neighborhood Distribution of cell-types with local neighborhood Computes nearest distance between all phenotypes for every cell Distribution of spatial expression with local neighborhood cell–cell interactions analysis Latent Dirichlet Allocation (LDA) modelling for spatial motifs Scoring proximity between user defined cell types Search for similar looking regions within and across images

Plotting: pl Overlays markers on UMAP Overlays gating positivity on the image for manual gating Opens the image with overlays on a napari browser Generate a stacked barplot from any two columns of categorical data Generate a pieplot of cell-type proportion or any categorical data vizualize foldchange in phenotypes between samples/ROIs Generate a voronoi diagram and color it with categorical data Heatmap of cell–cell interaction analysis Visualize distance between phenotypes Bar plot of the derived Spatial Proximity Scores Add ROI's with napari browser

Helper Functions: hl

sm.hl.classify Quickly classify cells based on pos/negativity of list of markers
sm.hl.rename Quickly rename values within columns based on dict mapping
sm.hl.animate Create a animated scatter plot of embedding -> physical location
sm.hl.scimap_to_csv Export scimap object to CSV
sm.hl.addROI_omero Add ROI's extracted from Omero to Scimap object
sm.hl.dropFeatures Handy Function to subset the adata object