peptide_data | R Documentation |
A high-density peptide microarray example to identify peptides for which antibody binding levels differ between control subjects and rheumatoid arthritis (RA) patients expressing a specific disease marker combination (i.e., CCP+RF+ RA).
data("peptide_data")
A data frame with 152603 observations on the following 16 variables.
The first 8 columns are RA patients and the remaining columns are from control subjects.
Each row of the data (rownames(peptide_data)
) is a probed length-12 peptide and each column of the data (colnames(peptide_data)
) is a subject with distinct pseudo sample ID. The binding value is doubly-log transformed using natural base to stabilize variance.
Zheng, Zihao, et al. Disordered Antigens and Epitope Overlap Between Anti Citrullinated Protein Antibodies and Rheumatoid Factor in Rheumatoid Arthritis. Arthritis & Rheumatology 72.2 (2020): 262-272. https://onlinelibrary.wiley.com/doi/abs/10.1002/art.41074
Zheng et al. MixTwice: Large scale hypothesis testing for peptide arrays by variance mixing. Technical Report, October 2020.
#### load the RA data
data(peptide_data)
#### visualize the data
## each row is a peptide with unique peptide sequence
## each column is a subject with information on group and pseudo ID
colnames(peptide_data)
## z-score for peptide
get_zscore = function(x){
n = length(x)
t = t.test(x[1:(n/2)], x[(n/2 + 1):n], var.equal = T)$statistic
return(qnorm(pt(t, df = n-2)))
}
z = apply(peptide_data, 1, get_zscore)
## visualize the density of z-score
hist(z, probability = T, 100, ylim = c(0,0.4), col = "blue")
lines(density(rnorm(10^5)), lwd =2)
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