artImpute: Artificially miss and impute each data entry individually by...

In Huang-lab/oppti: Outlier Protein and Phosphosite Target Identifier

Artificially miss and impute each data entry individually by ignoring outlying values

Description

Infers the normal-state expression of a marker based on its co-expression network, i.e., the weighted average of the marker's nearest neighbors in the data. The returned imputed data will later be used to elucidate dysregulated (protruding) events.

Usage

artImpute(dat, ku = 6, marker.proc.list = NULL, miss.pstat = 0.4,
verbose = FALSE)

Arguments

dat	an object of log2-normalized protein (or gene) expressions, containing markers in rows and samples in columns.
ku	an integer in [1,num.markers], upper bound on the number of nearest neighbors of a marker.
marker.proc.list	character array, the row names of the data to be processed/imputed.
miss.pstat	the score threshold for ignoring potential outliers during imputation. miss.pstat = 1 ignores values outside of the density box (i.e., 1st-3rd quartiles). The algorithm ignores values lying at least (1/miss.pstat)-1 times IQR away from the box; e.g., use miss.pstat=1 to ignore all values lying outside of the box; use miss.pstat=0.4 to ignore values lying at least 1.5 x IQR away from the box; use miss.pstat=0 to employ all data during imputation.
verbose	logical, to show progress of the algorithm.

Value

the imputed data that putatively represents the expressions of the markers in the (matched) normal states.

Examples

dat = setNames(as.data.frame(matrix(1:(5*10),5,10),
row.names = paste('marker',1:5,sep='')), paste('sample',1:10,sep=''))
imputed = artImpute(dat, ku = 2)

Huang-lab/oppti documentation built on March 26, 2023, 12:52 p.m.