View source: R/BioTIP_update_04202022.R
getIc | R Documentation |
Retrieve Ic scores (Pearson correlation of genes / Pearson correlation of samples) for the identified critical transition state
getIc(
counts,
sampleL,
genes,
output = c("Ic", "PCCg", "PCCs"),
fun = c("cor", "BioTIP"),
shrink = TRUE,
use = c("everything", "all.obs", "complete.obs", "na.or.complete",
"pairwise.complete.obs")
)
counts |
A numeric matrix or data frame. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively. |
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'. |
genes |
A character vector consisting of unique CTS gene ids. This can be obtained from |
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) |
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". |
A list of numeric values, whose length and names are inherited from sampleL
Zhezhen Wang zhezhen@uchicago.edu; Xinan H Yang xyang2@uchicago.edu
Schafer and Strimmer (2005) "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics"
M. Mojtahedi et al., Cell Fate Decision as High-Dimensional Critical State Transition. PLoS Biol 14, e2000640 (2016).
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'])
CTS = c('loci1', 'loci2')
## Comparing the results with an estiamted correlation matrix with that without estimation.
Ic = getIc(counts, samplesL, CTS, fun='cor')
Ic.2 = getIc(counts, samplesL, CTS, fun='BioTIP', shrink=FALSE)
BioTIP = getIc(counts, samplesL, CTS, fun='BioTIP')
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