Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

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Documentation for package ‘smoppix’ version 1.1.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

addCell	Add cell boundaries and event-wise cell identifiers to a hyperframe.
addDesign	Add design variables to hyperframe
addNuclei	Add nuclei to a hyperframe
addTabObs	Add tables with gene counts to the hyperframe, presort by gene and x-ccordinate and add design varibales
addWeightFunction	Estimate probabilistic indices for single-molecule localization patterns, and add the variance weighting function.
buildDataFrame	Extract a data frame for a certain gene and PI from a fitted object
buildFormula	Build a formula from different components
buildHyperFrame	Build a hyperframe containing all point patterns of an experiment.
buildHyperFrame-method	Build a hyperframe containing all point patterns of an experiment.
buildMoransIDataFrame	Build a data frame with Moran's I as outcome variable
buildMoransIWeightMat	Build a weight matrix based on nearest neighbourship for Moran's I calculations
calcIndividualPIs	Calculate individual PI entries of a single point pattern
calcNNPI	Estimate the PI for the nearest neighbour distances, given a set of ranks, using the negative hypergeometric distribution
calcWindowDistPI	Estimate the PI for the distance to a fixed object of interest, such as a cell wall or centroid
centerNumeric	Center numeric variables
checkFeatures	Check if features are present in hyperframe
checkPi	Check if the required PI's are present in the object
constructDesignVars	Check for or construct design matrix
convertToOwins	Convert windows to spatstat.geom owin format
crossdistWrapper	A wrapper for C-functions calculating cross-distance matrix fast
Eng	Spatial transcriptomics data of mouse fibroblast cells
EngRois	Spatial transcriptomics data of mouse fibroblast cells
estGradients	Estimate gradients over multiple point patterns, and test for significance
estGradientsSingle	Estimate gradients over multiple point patterns, and test for significance
estPis	Estimate probabilistic indices for single-molecule localization patterns, and add the variance weighting function.
estPisSingle	Estimate probabilistic indices for single-molecule localization patterns, and add the variance weighting function.
evalWeightFunction	Evaluate a variance weighting function
extractResults	Extract results from a list of fitted LMMs. For internal use mainly.
findEcdfsCell	Construct empirical cumulative distribution functions (ecdfs) for distances within the cell
findOverlap	Find overlap between list of windows
fitGradient	Test for presence of gradient in a hyperframe of point patterns
fitLMMs	Fit linear (mixed) models for all probabilistic indices (PIs) and all genes
fitLMMsSingle	Fit linear (mixed) models for all probabilistic indices (PIs) and all genes
fitPiModel	Fit a linear model for an individual gene and PI combination
getCoordsMat	Extract coordinates from a point pattern or data frame
getDesignVars	getDesignVars() returns all design variables, both at the level of the point pattern and the level of the event
getElement	Extract en element from a matrix or vector
getEventVars	getDesignVars() returns all design variables, both at the level of the point pattern and the level of the event
getFeatures	Extract all unique features from an object, or the ones for which PIs were estimated
getGp	Helper function to get gene pair from a vector or list
getHypFrame	Extract the hyperframe
getPPPvars	getDesignVars() returns all design variables, both at the level of the point pattern and the level of the event
getPvaluesGradient	Estimate gradients over multiple point patterns, and test for significance
getResults	Fit linear (mixed) models for all probabilistic indices (PIs) and all genes
loadBalanceBplapply	Parallel processing with BiocParallel with load balancing
makeDesignVar	Make design variable by combining different design variables
makePairs	An aux function to build gene pairs
moransI	Calculate the Moran's I test statistic for spatial autocorrelation
named.contr.sum	A version of contr.sum that retains names, a bit controversial but also clearer
nestRandom	Nest random effects within fixed variables, in case the names are the same
plotCells	Plot the n cells with highest abundance of a feature
plotExplore	Plot a hyperframe with chosen features highlighted
plotTopResults	Plot the most significant findings for a certain PI
plotWf	Plot the variance weighting function
sortGp	Sort feature pairs alphabetically
splitWindow	Split a number of plots into rows and columns
subSampleP	Subsample a point pattern when it is too large
sund	Helper function to spit gene pairs
writeToXlsx	Write effect sizes and p-values results to excel worksheet
Yang	Spatial transcriptomics data of Selaginella moellendorffii roots