oppti: Outlier protein and phosphosite target identification

In oppti: Outlier Protein and Phosphosite Target Identifier

Description

Find outlying markers and events across cancer types.

Usage

oppti(data, mad.norm = FALSE, cohort.names = NULL, panel = "global",
panel.markers = NULL, tol.nas = 20, ku = 6, miss.pstat = 0.4,
demo.panels = FALSE, save.data = FALSE, draw.sc.plots = FALSE,
draw.vi.plots = FALSE, draw.sc.markers = NULL,
draw.ou.plots = FALSE, draw.ou.markers = NULL, verbose = FALSE)

Arguments

data	a list object where each element contains a proteomics data for a different cohort (markers in the rows, samples in the columns) or a character string defining the path to such data (in .RDS format).
mad.norm	logical, to normalize the proteomes to have a unit Median Absolute Deviation.
cohort.names	character array.
panel	a character string describing marker panel, e.g., 'kinases'. Use 'global' to analyze all markers quantified across cohorts (default). Use 'pancan' to analyze the markers commonly quantified across the cohorts.
panel.markers	a character array containing the set of marker names that user wants to analyze, e.g., panel.markers = c("AAK1", "AATK", "ABL1", "ABL2", ...).
tol.nas	a constant in [0,100], tolerance for the percentage of NAs in a marker, e.g., tol.nas = 20 will filter out markers containing 20% or more NAs across samples.
ku	an integer in [1,num.markers], upper bound on the number of nearest neighbors of a marker.
miss.pstat	a constant in [0,1], statistic to estimate potential outliers. See 'artImpute()'.
demo.panels	logical, to draw demographics of the panel in each cohort.
save.data	logical, to save intermediate data (background inference and dysregulation measures).
draw.sc.plots	logical, to draw each marker's qqplot of observed vs inferred (imputed) expressions.
draw.vi.plots	logical, to draw each marker's violin plot of observed vs imputed expressions.
draw.sc.markers	character array, marker list to draw scatter plots
draw.ou.plots	logical, to draw each marker's outlier prevalence (by the percentage of outlying samples) across the cohorts.
draw.ou.markers	character array, marker list to draw pan-cancer outlier percentage plots
verbose	logical, to show progress of the algorithm.

Value

dysregulation scores of every marker for each sample.

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

the result of Kolmogorov-Smirnov tests that evaluates the statistical significance of each marker's outlier samples.

a data list containing, for each cohort, the percentage of outlier samples for every marker.

a data list containing, for each cohort, the outlier significance threshold.

See Also

[artImpute()] for how to set 'miss.pstat' and 'ku'

Examples

set.seed(1)
dat = setNames(as.data.frame(matrix(runif(10*10),10,10),
row.names = paste('marker',1:10,sep='')), paste('sample',1:10,sep=''))
result = oppti(dat)

oppti documentation built on Nov. 8, 2020, 5 p.m.