AlteredPQR (version 0.1.0)

CorShift: 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)

Value

cor_table table

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.

Author

Marija Buljan <[e-mail]>

Examples

data("int_pairs", package = "AlteredPQR")
data("quant_data_all", package = "AlteredPQR")
samplesGroupA = 1:23
samplesGroupB = (1+23):(23+18)
cor_results = CorShift()