R/util.R

In menggf/DEComplexDisease: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis

# read the output of sig.cpp
.read.output.module <- function(res, ges, pas) {
    tt = 1
    sc1 = res[tt]
    tt = tt + 1
    x1 = res[tt]
    if (x1 == 0) 
        return(list())
    tt = tt + 1
    row1 = res[tt:(tt + x1 - 1)]
    tt = tt + x1
    y1 = res[tt]
    if (y1 == 0) 
        return(list())
    tt = tt + 1
    col1 = res[tt:(tt + y1 - 1)]
    tt = tt + y1
    
    sc3 = res[tt]
    tt = tt + 1
    x3 = res[tt]
    if (x3 == 0) 
        return(list())
    tt = tt + 1
    row3 = res[tt:(tt + x3 - 1)]
    tt = tt + x3
    y3 = res[tt]
    if (y3 == 0) 
        return(list())
    tt = tt + 1
    col3 = res[tt:(tt + y3 - 1)]
    tt = tt + y3
    nn = res[tt]
    pp = res[(tt + 1):(tt + nn)]
    aa = pp[seq(1, nn - 1, by = 3)]
    bb = pp[seq(2, nn - 1, by = 3)]
    cc = pp[seq(3, nn, by = 3)]
    
    tt = tt + nn + 1
    mm = res[tt]
    tt = tt + 1
    remove = res[tt:(tt + mm - 1)]
    tt = tt + mm
    mm = res[tt]
    tt = tt + 1
    add = res[tt:(tt + mm - 1)]
    tt = tt + mm - 1
    if (tt != length(res)) 
        stop("Error: output number is not match")
    return(list(max.genes = list(genes = ges[row1], patients = pas[col1], sc = sc1), 
        max.patients = list(genes = ges[row3], patients = pas[col3], sc = sc3), genes.removed = ges[remove], 
        patients.added = pas[add], curve = list(no.gene = bb, no.patient = cc, score = aa)))
}

# read the output of sig.cpp
.read.output.deg <- function(x, ges, pas) {
    tt = 1
    nr = round(x[tt])
    nc = round(x[tt + 1])
    cf = x[tt + 2]
    sc = x[tt + 3]
    tt = tt + 4
    rows = round(x[tt:(tt + nr - 1)])
    genes = ges[rows]
    tt = tt + nr
    cols = round(x[tt:(tt + nc - 1)])
    patients = pas[cols]
    tt = tt + nc
    return(list(genes = genes, patients = patients, sc = sc, cutoff = cf))
}

# transform
.bi.transform <- function(x, min.init.coverage) {
    if (x > min.init.coverage) 
        return(1)
    if (x < -1 * min.init.coverage) 
        return(-1)
    return(0)
}

# to find the seed patients
.find.seed <- function(mx, pas, min.init.coverage) {
    x = rowSums(mx[, pas])
    z = x/length(pas)
    res = vector()
    while (1) {
        res = vapply(z, .bi.transform,1, min.init.coverage )
        if (length(res[res != 0]) < 10000) 
            break
        min.init.coverage = min.init.coverage + 0.05
    }
    return(res)
}

# to find the representive modules
.select.mod <- function(res.module, k = 10, type = "model") {
    if (length(res.module) == 0) 
        stop("Error: res.module: empty module is used!")
    if (is.null(res.module[[1]][[type]])) 
        stop(paste("Error: type: ", type, " does not exist!", sep = ""))
    mods = names(res.module)
    mods = mods[mods != "decd.specific" & mods != "decd.input" & mods != "decd.clustering"]
    n = length(mods)
    if (k >= n) 
        return(mods)
    if (k >= n - 10) 
        return(sample(mods, k))
    mods = mods[mods != "M0"]
    all.patients = unique(unlist(sapply(mods, function(x) res.module[[x]][[type]][["patients"]])))
    rt = vector()
    len.mod0 = vapply(mods, function(x) length(all.patients[all.patients %in% res.module[[x]][[type]][["patients"]]]), 10)
    for (i in seq_len(k)) {
        len.mod = vapply(mods, function(x) length(all.patients[all.patients %in% 
            res.module[[x]][[type]][["patients"]]]), 10)
        used.mod = mods[which.max(len.mod)]
        if (length(used.mod) > 1) 
            used.mod = used.mod[which.max(len.mod[used.mod])][1]
        rt = append(rt, used.mod)
        all.patients = all.patients[!all.patients %in% res.module[[used.mod]][[type]][["patients"]]]
        mods = mods[!mods == used.mod]
    }
    if (!is.null(res.module[["M0"]])) 
        rt = unique(c(rt, "M0"))
    return(rt)
}

.trans.sq <- function(n) {
    m = sqrt(n)
    if (m == as.integer(m)) {
        return(c(m, m))
    } else {
        return(c(round(m), ceiling(m)))
    }
}

# find the parameter of derivative function
.cof <- function(cf) {
    n = length(cf)
    sum = 0
    new.cf = vector()
    for (i in 2:n) {
        new.cf = append(new.cf, (i - 1) * cf[i])
    }
    return(new.cf)
}
# find the parameter of derivative function
.fv <- function(x, cf) {
    n = length(cf)
    sum = 0
    for (i in seq_len(n)) sum = sum + cf[i] * (x^(i - 1))
    return(sum)
}