NormalizeSP: Normalization of an 'SPData' object
In kieranrcampbell/SpatialStats: Statistics for spatially resolved proteomics data

Description Usage Arguments Details Value

This method performs four steps of of normalization:

Normalize by cell size
Normalize by cell concentration
Scale-centre the rows
Regenerate the nearest neighbour data from the scale centred data using the nearest neighbour IDs

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NormalizeSP(sp, by.class = TRUE, norm.size = TRUE,
  norm.concentration = TRUE, norm.centrescale = TRUE,
  norm.regenerateNN = TRUE)

`sp`	The `SPData` instance to use
`by.class`	Normalize each class of cells separately
`norm.size`	Regress out a cell size dependency
`norm.concentration`	Regress out the cell concentration dependency
`norm.centrescale`	Scale each column of `cells(sp)` to have mean 0 and sd 1
`norm.regenerateNN`	Regenerate nearest neighbour data after normalization

Each step of normalization can be optionally turned on or off using the appropriate flag.

Setting by.class to true means each cell class is normalized separately within a sample, while if set to false all cells are normalized together. Note if there are many cell classes and few cells, normalization may become nonsensical - this is not checked for.

norm.concentration constructs a measure of cell concentration for each channel as the sum across all other channels. It then regresses each channel against the measure of concentration and accepts the residuals as the normalized quantity.

norm.regenerateNN reconstructs the neighbour data from the newly-normalized cell data. The neighbour information is stored separately in an SPData object so each can be manipulated / normalized without affecting the other.

An object of class SPData that has been appropriately normalized.

kieranrcampbell/SpatialStats documentation built on May 20, 2019, 9:24 a.m.