View source: R/BioTIP_update_04202022.R
simulation_Ic | R Documentation |
Simulating Ic scores for x
randomly selected samples, where x should be the same
as the length of identified critical-transition signal (CTS) (e.g., number of genes) and B
is self-defined running times.
simulation_Ic(
obs.x,
sampleL,
counts,
B = 1000,
fun = c("cor", "BioTIP"),
shrink = TRUE,
use = c("everything", "all.obs", "complete.obs", "na.or.complete",
"pairwise.complete.obs"),
output = c("Ic", "PCCg", "PCCs")
)
obs.x |
An integer, length of identified CTS. |
sampleL |
A list of vectors, whose length is the number of states. Each vector gives the sample names in a state. Note that the vector s (sample names) has to be among the column names of the R object 'df'. |
counts |
A numeric matrix or dataframe in which columns are samples and rows are transcripts. Each row needs to have a unique row name (i.e. transcript ID). |
B |
An integer, setting the permutation with |
fun |
An optional character string indicating the R functon to calculate correlations
for all possible pairs of columns of a matrix.
When using "BioTIP", The method is modified to ignore missing values, analogous to how
|
shrink |
A flag specifying whether to shrink the correlation or not.
This appraoch uses the method outlined by Schafer and Strimmer in
"A Shrinkage Approach to Large-Scale Covariance Matrix Estimation
and Implications for Functional Genomics" (2005)
Comparing to fun='cor', the 'BioTIP' method without shinkage is modified
to ignore missing values, analogous to how |
use |
An optional character string, when fun=="cor", it gives a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". |
output |
A string. Please select from 'Ic', 'PCCg', or 'PCCs'. Uses 'Ic' by default. 'PCCg' is the PCC between genes (numerator) and 'PCCs' is PCC between samples (denominator) |
A matrix of y
rows and B
columns where y
is the length of sampleL
and B
is self-defined. Each column is a set of Ic scores calculated for each state
Zhezhen Wang zhezhen@uchicago.edu
counts = matrix(sample(1:100, 27), 3, 9)
colnames(counts) = 1:9
row.names(counts) = c('loci1', 'loci2', 'loci3')
cli = cbind(1:9, rep(c('state1', 'state2', 'state3'), each = 3))
colnames(cli) = c('samples', 'group')
samplesL <- split(cli[, 1], f = cli[, 'group'])
simulation_Ic(2, samplesL, counts, B =3, fun="BioTIP", shrink=TRUE)
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