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library(atSNP)
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library(BiocParallel)
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library(testthat)
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## process the data
data(example)
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motif_scores <- ComputeMotifScore(motif_library, snpInfo, ncores = 1)
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motif_scores <- MatchSubsequence(motif_scores$snp.tbl, motif_scores$motif.scores, ncores = 1, motif.lib = motif_library)
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motif_scores[which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1"), ]
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len_seq <- sapply(motif_scores$ref_seq, nchar)
snp_pos <- as.integer(len_seq / 2) + 1
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i <- which(motif_scores$snpid == "rs7412" & motif_scores$motif == "SIX5_disc1")
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test_that("Error: reference bases are not the same as the sequence matrix.", {
expect_equal(sum(snpInfo$sequence_matrix[31, ] != snpInfo$ref_base), 0)
expect_equal(sum(snpInfo$sequence_matrix[31, ] == snpInfo$snp_base), 0)
})
test_that("Error: log_lik_ratio is not correct.", {
expect_equal(motif_scores$log_lik_ref - motif_scores$log_lik_snp, motif_scores$log_lik_ratio)
})
test_that("Error: log likelihoods are not correct.", {
log_lik <- sapply(seq(nrow(motif_scores)),
function(i) {
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motif_mat <- motif_library[[motif_scores$motif[i]]]
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colind<-which(snpInfo$snpids==motif_scores$snpid[i])
bases <- snpInfo$sequence_matrix[motif_scores$ref_start[i]:motif_scores$ref_end[i], colind]
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if(motif_scores$ref_strand[i] == "-")
bases <- 5 - rev(bases)
log(prod(
motif_mat[cbind(seq(nrow(motif_mat)),
bases)]))
})
expect_equal(log_lik, motif_scores$log_lik_ref)
snp_mat <- snpInfo$sequence_matrix
snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
log_lik <- sapply(seq(nrow(motif_scores)),
function(i) {
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motif_mat <- motif_library[[motif_scores$motif[i]]]
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colind<-which(snpInfo$snpids==motif_scores$snpid[i])
bases <- snp_mat[motif_scores$snp_start[i]:motif_scores$snp_end[i], colind]
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if(motif_scores$snp_strand[i] == "-")
bases <- 5 - rev(bases)
log(prod(
motif_mat[cbind(seq(nrow(motif_mat)),
bases)]))
})
expect_equal(log_lik, motif_scores$log_lik_snp)
})
test_that("Error: log_enhance_odds not correct.", {
len_seq <- sapply(motif_scores$ref_seq, nchar)
snp_pos <- as.integer(len_seq / 2) + 1
## log odds for reduction in binding affinity
pos_in_pwm <- snp_pos - motif_scores$ref_start + 1
neg_ids <- which(motif_scores$ref_strand == "-")
pos_in_pwm[neg_ids] <- motif_scores$ref_end[neg_ids]- snp_pos[neg_ids] + 1
snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
snp_base[neg_ids] <- 5 - snp_base[neg_ids]
ref_base[neg_ids] <- 5 - ref_base[neg_ids]
my_log_reduce_odds <- sapply(seq(nrow(motif_scores)),
function(i)
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log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]]) -
log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]])
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)
expect_equal(my_log_reduce_odds, motif_scores$log_reduce_odds)
## log odds in enhancing binding affinity
pos_in_pwm <- snp_pos - motif_scores$snp_start + 1
neg_ids <- which(motif_scores$snp_strand == "-")
pos_in_pwm[neg_ids] <- motif_scores$snp_end[neg_ids]- snp_pos[neg_ids] + 1
snp_base <- sapply(substr(motif_scores$snp_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
ref_base <- sapply(substr(motif_scores$ref_seq, snp_pos, snp_pos), function(x) which(c("A", "C", "G", "T") == x))
snp_base[neg_ids] <- 5 - snp_base[neg_ids]
ref_base[neg_ids] <- 5 - ref_base[neg_ids]
my_log_enhance_odds <- sapply(seq(nrow(motif_scores)),
function(i)
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log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], snp_base[i]]) -
log(motif_library[[motif_scores$motif[i]]][pos_in_pwm[i], ref_base[i]])
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)
expect_equal(my_log_enhance_odds, motif_scores$log_enhance_odds)
})
test_that("Error: the maximum log likelihood computation is not correct.", {
snp_mat <- snpInfo$sequence_matrix
snp_mat[cbind(snp_pos, seq(ncol(snp_mat)))] <- snpInfo$snp_base
.findMaxLog <- function(seq_vec, pwm) {
snp_pos <- as.integer(length(seq_vec) / 2) + 1
start_pos <- snp_pos - nrow(pwm) + 1
end_pos <- snp_pos
rev_seq <- 5 - rev(seq_vec)
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maxLogProb <- -Inf
for(i in start_pos : end_pos) {
LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
seq_vec[i - 1 + seq(nrow(pwm))])]))
if(LogProb > maxLogProb)
maxLogProb <- LogProb
}
for(i in start_pos : end_pos) {
LogProb <- log(prod(pwm[cbind(seq(nrow(pwm)),
rev_seq[i - 1 + seq(nrow(pwm))])]))
if(LogProb > maxLogProb)
maxLogProb <- LogProb
}
return(maxLogProb)
}
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## find the maximum log likelihood on the reference sequence
my_log_lik_ref <- sapply(seq(nrow(motif_scores)),
function(x) {
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colind<-which(snpInfo$snpids==motif_scores$snpid[x])
seq_vec<- snpInfo$sequence_matrix[, colind]
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pwm <- motif_library[[motif_scores$motif[x]]]
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return(.findMaxLog(seq_vec, pwm))
})
## find the maximum log likelihood on the SNP sequence
my_log_lik_snp <- sapply(seq(nrow(motif_scores)),
function(x) {
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colind<-which(snpInfo$snpids==motif_scores$snpid[x]) #ADDED
seq_vec<- snp_mat[, colind]
pwm <- motif_library[[motif_scores$motif[x]]]
return(.findMaxLog(seq_vec, pwm))
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})
expect_equal(my_log_lik_ref, motif_scores$log_lik_ref)
expect_equal(my_log_lik_snp, motif_scores$log_lik_snp)
})
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