| CorShift {AlteredPQR} | R Documentation |
Changes in correlation trends
Description
The function identifies instances in which two proteins correlate strongly only in one of the two studied groups.
Usage
CorShift(samplesA = samplesGroupA, samplesB = samplesGroupB, shift_threshold = 0.6,
writeTable = FALSE, min_cor_in_samples = 0.6, cor_signif = 0.01,
quant_data_all_local = quant_data_all, int_pairs_local = int_pairs)
Arguments
samplesA |
Numeric vector with information on column numbers for the samples in the first group for the comparison. |
samplesB |
Numeric vector with information on column numbers for the samples in the second group for the comparison. |
shift_threshold |
Numeric value defining a minimum thresold of the Pearson correlation value between the two sample groups in order for them to be included in the results table. |
writeTable |
Option (T or F) to save results table as a text file. |
min_cor_in_samples |
Numeric value defining a minimum Pearson correlation value of protein quantities, which is taken as a threshold to consider that two proteins correlate in either of the two compared groups. |
cor_signif |
Numeric value defining a maximum allowed p-value for the Pearson correlation, which is taken as a threshold to consider that quantiative measurements for the two proteins correlate significantly in either of the two compared groups. |
quant_data_all_local |
A data matrix with quantiative proteomics measurements in which rows represent uniprot protein identifiers, and columns samples. |
int_pairs_local |
A data matrix with two columns. Rows contain information on interacting protein pairs. |
Value
cor_table table
Author(s)
Marija Buljan <marija.buljan@empa.ch>
Examples
data("int_pairs", package = "AlteredPQR")
data("quant_data_all", package = "AlteredPQR")
samplesGroupA = 1:23
samplesGroupB = (1+23):(23+18)
cor_results = CorShift()