R/PoisthNorm.R
In Nanostring-Biostats/GeoDiff: Count model based differential expression and normalization on GeoMx RNA data
#' Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides
#'
#' Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides
#'
#' @param object a valid GeoMx S4 object
#' @param split indicator variable on whether it is for multiple slides (Yes, TRUE; No, FALSE)
#' @param ROIs_high ROIs with high expressions defined based on featfact and featfact
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param calhes The user input whether to calculate hessian: calhes, default=FALSE
#' @param ... additional argument list that might be used
#'
#' @importFrom Biobase pData
#' @importFrom Biobase fData
#' @importFrom Biobase exprs
#' @importFrom Biobase assayDataElement
#'
#'
#' @return if split is FALSE, a valid GeoMx S4 object including the following items
#' \itemize{
#' \item para0_norm, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows, in featureData.
#' \item para_norm, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows, in featureData.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay lot.
#' \item sizefact_norm, estimated sizefact, in phenoData.
#' \item sizefact0_norm, estimated sizefact in iter=1, in phenoData.
#' \item preci1, precision matrix 1, in experimentData.
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged, in featureData
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged, in featureData
#' \item features_high, same as the input features_high, in featureData
#' \item features_all, same as the input features_all, in featureData
#' }
#'
#' if split is TRUE, a valid GeoMx S4 object with the following items appended.
#' \itemize{
#' \item threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows, in featureData.
#' \item threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows, in featureData.
#' \item normmat0_sp, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.
#' \item normmat_sp, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay slot.
#' \item sizefact_norm_sp, estimated sizefact, in phenoData
#' \item sizefact0_norm_sp, estimated sizefact in iter=1, in phenoData
#' \item preci1, precision matrix 1, in experimentData
#' \item conv0_sp_XX, vector of convergence for each unique slide value for iter=1, 0 converged, 1 not converged, in featureData for each unique slide.
#' \item conv_sp_XX, vector of convergence for each unique slide value for iter=2, 0 converged, 1 not converged, in featureData for each unique slide.
#' \item features_high_sp, same as the input features_high, in featureData.
#' \item features_all_sp, same as the input features_all, in featureData.
#' }
#'
#' @importFrom Biobase sampleNames
#' @importFrom Biobase featureNames
#' @importFrom Biobase annotation
#'
#' @examples
#'
#' library(Biobase)
#' library(dplyr)
#' data(demoData)
#' demoData <- fitPoisBG(demoData, size_scale = "sum")
#' demoData <- aggreprobe(demoData, use = "cor")
#' demoData <- BGScoreTest(demoData)
#' thmean <- 1 * mean(fData(demoData)$featfact, na.rm = TRUE)
#' demo_pos <- demoData[which(!fData(demoData)$CodeClass == "Negative"), ]
#' demo_neg <- demoData[which(fData(demoData)$CodeClass == "Negative"), ]
#' sc1_scores <- fData(demo_pos)[, "scores"]
#' names(sc1_scores) <- fData(demo_pos)[, "TargetName"]
#' features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) &
#' (sc1_scores < quantile(sc1_scores, probs = 0.95))) |>
#' which() |>
#' names()
#' set.seed(123)
#' features_high <- sample(features_high, 100)
#' demoData <- fitNBth(demoData,
#' features_high = features_high,
#' sizefact_BG = demo_neg$sizefact,
#' threshold_start = thmean,
#' iterations = 5,
#' start_para = c(200, 1),
#' lower_sizefact = 0,
#' lower_threshold = 100,
#' tol = 1e-8)
#' ROIs_high <- sampleNames(demoData)[which((quantile(fData(demoData)[["para"]][, 1],
#' probs = 0.90, na.rm = TRUE) -
#' notes(demoData)[["threshold"]]) * demoData$sizefact_fitNBth > 2)]
#' features_all <- rownames(demo_pos)
#' thmean <- mean(fData(demo_neg)[["featfact"]])
#' demoData <- fitPoisthNorm(
#' object = demoData,
#' split = FALSE,
#' ROIs_high = ROIs_high,
#' features_high = features_high,
#' features_all = features_all,
#' sizefact_start = demoData[, ROIs_high][["sizefact_fitNBth"]],
#' sizefact_BG = demoData[, ROIs_high][["sizefact"]],
#' threshold_mean = thmean,
#' preci2 = 10000,
#' prior_type = "contrast",
#' covrob = FALSE,
#' preci1con = 1 / 25
#' )
#'
#' @export
#' @docType methods
#' @rdname fitPoisthNorm-methods
setGeneric("fitPoisthNorm",
signature = c("object"),
function(object, ...) standardGeneric("fitPoisthNorm")
)
#' @rdname fitPoisthNorm-methods
#' @aliases fitPoisthNorm,NanoStringGeoMxSet-method
setMethod(
"fitPoisthNorm", "NanoStringGeoMxSet",
function(object, split = FALSE, ROIs_high = NULL, features_high = NULL,
features_all = NULL, sizefact_start = NULL, sizefact_BG = NULL,
threshold_mean = NULL, preci2=10000, iterations = 2, prior_type = c("contrast", "equal"),
sizefactrec = TRUE, size_scale = c("sum", "first"), sizescalebythreshold = FALSE,
covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1, calhes = FALSE) {
# calculate backmean
# setting default values for sizefact_BG
fDat <- Biobase::fData(object)
fDatNeg <- fDat[which(fDat$CodeClass == "Negative"), ]
# only calculate backmean if any of the three params are missing
if (any(c(is.null(sizefact_BG), is.null(sizefact_start), is.null(ROIs_high), is.null(threshold_mean)))) {
if (isFALSE(split)) {
# single slide
if (!("sizefact" %in% varLabels(object))) {
stop("Please run `fitPoisBG` first.")
} else {
# calculate the backmean for WTA or CTA data
thmean <- mean(fDatNeg[["featfact"]])
}
} else {
# multiple slides
if (!("sizefact_sp" %in% varLabels(object))) {
stop("Please run `fitPoisBG` first with `groupvar`.")
} else {
# calculate the backmean for WTA or CTA data
thmean <- colMeans(fDatNeg[, grep("featfact_", fvarLabels(object))])[1]
}
}
}
# setting default value for ROIs_high
if (is.null(ROIs_high)) {
if (!("sizefact_fitNBth" %in% varLabels(object))) {
stop("Please run `fitNBth` first.")
} else {
ROIs_high <- Biobase::sampleNames(object)[which((quantile(fData(object)[["para"]][, 1],
probs = 0.90, na.rm = TRUE
) - notes(object)[["threshold"]]) * object$sizefact_fitNBth > 2)]
}
}
object_high <- object[, ROIs_high]
fDat <- Biobase::fData(object_high)
pDat <- Biobase::pData(object_high)
posdat <- object_high[-which(fDat$CodeClass == "Negative"), ]
countmat <- Biobase::exprs(posdat)
# calculate probenum for the dataset
if ("probenum" %in% fvarLabels(posdat)) {
probenum <- fData(posdat)[["probenum"]]
} else {
stop("No `probenum` is found. Run `aggreprobe` first.")
}
names(probenum) <- rownames(fData(posdat))
# setting default value for sizefact_BG
if (is.null(sizefact_BG)) {
if (isFALSE(split)) {
# single slide
sizefact_BG <- pDat[["sizefact"]]
} else {
# multiple slides
sizefact_BG <- pDat[["sizefact_sp"]]
}
}
# setting default value for sizefact_start
if (is.null(sizefact_start)) {
if (!("sizefact_fitNBth" %in% colnames(pDat))) {
stop("Please run `fitNBth` first.")
} else {
sizefact_start <- pDat[["sizefact_fitNBth"]]
}
}
# setting default value for features_all
if (is.null(features_all)) {
gene_sum <- rowSums(countmat)
if (any(grepl("WTA", toupper(Biobase::annotation(object))))) {
features_all <- rownames(countmat)
} else if (any(grepl("CTA", toupper(Biobase::annotation(object))))) {
features_all <- rownames(countmat)
} else {
stop("No information is found to determine the data type (CTA or WTA).")
}
}
# setting default value for sizefact_start
if (is.null(threshold_mean)) {
threshold_mean <- thmean
}
# setting default value for features_high
if (is.null(features_high)) {
gene_sum <- rowSums(countmat)
if (any(grepl("WTA", toupper(Biobase::annotation(object))))) {
features_high <- names(which(((gene_sum > quantile(gene_sum, probs = 0.5)) & (gene_sum < quantile(gene_sum, probs = 0.95)))))
features_high <- sort(sample(features_high, 1500))
} else if (any(grepl("CTA", toupper(Biobase::annotation(object))))) {
if (any(grepl("scores", fvarLabels(object)))) {
if (split == TRUE) {
sc1_scores <- fData(object)[-which(fData(object)$Negative), grepl("scores_", fvarLabels(object))]
rownames(sc1_scores) <- fData(object)[-which(fData(object)$Negative), "TargetName"]
features_high <- apply(sc1_scores, 2, function(x) {
((x > quantile(x, probs = 0.4)) & (x < quantile(x, probs = 0.95)))
})
features_high <- names(which(apply(features_high, 1, all)))
} else {
sc1_scores <- fData(object)[-which(fData(object)$Negative), "scores"]
names(sc1_scores) <- fData(object)[-which(fData(object)$Negative), "TargetName"]
features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) & (sc1_scores < quantile(sc1_scores, probs = 0.95)))
features_high <- names(which(features_high))
}
} else {
stop("Please run score test first using `BGScoreTest`.")
}
} else {
stop("No information is found to determine the data type (CTA or WTA).")
}
}
if (isFALSE(split)) {
result <- fitPoisthNorm(
object = countmat,
probenum = probenum,
features_high = features_high,
features_all = features_all,
sizefact_start = sizefact_start,
sizefact_BG = sizefact_BG,
threshold_mean = threshold_mean,
preci2 = preci2,
iterations = iterations,
prior_type = prior_type,
sizefactrec = sizefactrec,
size_scale = size_scale,
sizescalebythreshold = sizescalebythreshold,
covrob = covrob,
preci1con = preci1con,
cutoff = cutoff,
confac = confac,
calhes = calhes
)
# para0
Biobase::fData(object)[["para0_norm"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$para0),
dimnames = list(Biobase::featureNames(object), paste0("var", seq_len((nrow(result$para0)))))
)
Biobase::fData(object)[["para0_norm"]][colnames(result$para0), ] <- t(result$para0)
# para
Biobase::fData(object)[["para_norm"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$para),
dimnames = list(Biobase::featureNames(object), paste0("var", seq_len((nrow(result$para)))))
)
Biobase::fData(object)[["para_norm"]][colnames(result$para), ] <- t(result$para)
# normmat0
normmat0_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat0_mat[colnames(result$normmat0), ROIs_high] <- t(result$normmat0)
Biobase::assayDataElement(object, "normmat0") <- normmat0_mat
# normmat
normmat_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat_mat[colnames(result$normmat), ROIs_high] <- t(result$normmat)
Biobase::assayDataElement(object, "normmat") <- normmat_mat
# sizefact
Biobase::pData(object)[ROIs_high, "sizefact_norm"] <- result$sizefact
# sizefac0
Biobase::pData(object)[ROIs_high, "sizefact0_norm"] <- result$sizefact0
# preci1
Biobase::notes(object)$preci1_norm <- result$preci1
# skipping appending Im0 and Im from result for now.
# conv0
Biobase::fData(object)[features_high, "conv0"] <- result$conv0
# conv
Biobase::fData(object)[features_all, "conv"] <- result$conv
# features_high
Biobase::fData(object)[features_high, "features_high"] <- 1
# features_all
Biobase::fData(object)[features_all, "features_all"] <- 1
} else {
if (is.null(Biobase::notes(object)$fitPoisBG_sp_var)) {
stop("Please run `fitPoisBG` first with `groupvar`.")
} else {
idvar <- Biobase::notes(object)$fitPoisBG_sp_var
id <- Biobase::pData(object)[[idvar]][match(ROIs_high, colnames(object))]
message(sprintf("The results are based on stored `groupvar`, %s", idvar))
}
result <- fitPoisthNorm_sp(
object = countmat,
probenum = probenum,
features_high = features_high,
features_all = features_all,
sizefact_start = sizefact_start,
sizefact_BG = sizefact_BG,
threshold_mean = threshold_mean,
preci2 = preci2,
id = id,
iterations = iterations,
prior_type = prior_type,
sizefactrec = sizefactrec,
size_scale = size_scale,
sizescalebythreshold = sizescalebythreshold,
covrob = covrob,
preci1con = preci1con,
cutoff = cutoff,
confac = confac
)
# threshold0
Biobase::fData(object)[["threshold0"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$threshold0),
dimnames = list(Biobase::featureNames(object), rownames(result$threshold0))
)
Biobase::fData(object)[["threshold0"]][colnames(result$threshold0), ] <- t(result$threshold0)
# threshold
Biobase::fData(object)[["threshold"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$threshold),
dimnames = list(Biobase::featureNames(object), rownames(result$threshold))
)
Biobase::fData(object)[["threshold"]][colnames(result$threshold), ] <- t(result$threshold)
# normmat0
normmat0_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat0_mat[colnames(result$normmat0), ROIs_high] <- t(result$normmat0)
Biobase::assayDataElement(object, "normmat0_sp") <- normmat0_mat
# normmat
normmat_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat_mat[colnames(result$normmat), ROIs_high] <- t(result$normmat)
Biobase::assayDataElement(object, "normmat_sp") <- normmat_mat
# sizefact
Biobase::pData(object)[ROIs_high, "sizefact_norm_sp"] <- result$sizefact
# sizefact0
Biobase::pData(object)[ROIs_high, "sizefact0_norm_sp"] <- result$sizefact0
# preci1
Biobase::notes(object)$preci1_norm_sp <- result$preci1
# skipping appending Im0 and Im from result for now.
# conv0
for (index in names(result$conv0)) {
Biobase::fData(object)[features_high, paste0("conv0_sp_", index)] <- result$conv0[[index]]
}
# conv
for (index in names(result$conv0)) {
Biobase::fData(object)[features_all, paste0("conv_sp_", index)] <- result$conv[[index]]
}
# features_high
Biobase::fData(object)[features_high, "features_high_sp"] <- 1
# features_all
Biobase::fData(object)[features_all, "features_all_sp"] <- 1
}
return(object)
}
)
#'
#' Poisson model based normalization and log2 transformation with threshold
#'
#' @param object count matrix with features in rows and samples in columns
#' @param probenum a vector of numbers of probes in each gene
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param calhes The user input whether to calculate hessian: calhes, default=FALSE
#'
#' @importFrom robust covRob
#'
#' @return a list of following items
#' \itemize{
#' \item para0, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows.
#' \item para, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
#' \item sizefact, estimated sizefact
#' \item sizefact0, estimated sizefact in iter=1
#' \item preci1, precision matrix 1
#' \item Im0, Information matrix of parameters in iter=1
#' \item Im, Information matrix of parameters in iter=2
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged
#' \item features_high, same as the input features_high
#' \item features_all, same as the input features_all
#' }
#'
#' @rdname fitPoisthNorm-methods
#' @aliases fitPoisthNorm,matrix-method
setMethod(
"fitPoisthNorm", "matrix",
function(object, probenum = rep(1, NROW(object)), features_high, features_all,
sizefact_start, sizefact_BG, threshold_mean, preci2=10000, iterations = 2,
prior_type = c("contrast", "equal"), sizefactrec = TRUE, size_scale = c("sum", "first"),
sizescalebythreshold = FALSE, covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1, calhes = FALSE) {
if (iterations != 2) {
stop("Only iterations=2 is allowed")
}
if (is.null(names(probenum))) names(probenum) <- rownames(object)
sizefact0 <- sizefact <- sizefact_start
n_para <- ncol(object)
X <- diag(1, n_para)
prior_type <- match.arg(prior_type)
if (prior_type == "equal") {
preci1 <- preci1con * t(X) %*% diag(1, n_para) %*% X
} else if (prior_type == "contrast") {
contrvec <- t(rep(1 / n_para, n_para)) %*% X
preci1 <- (preci1con) * t(contrvec) %*% contrvec
}
for (iter in seq_len(iterations)) {
if (iter == 1) {
modfit <- PoisthNorm_paraOptall(t(object[features_high, ]), X, sizefact_BG, sizefact, preci1, threshold_mean * probenum, preci2, sizescalebythreshold, calhes & (iter == iterations))
Im <- modfit$hes
Im0 <- Im
para <- modfit$par
colnames(para) <- features_high
para0 <- para
conv0 <- modfit$conv
} else {
modfit <- PoisthNorm_paraOptall(t(object[features_all, ]), X, sizefact_BG, sizefact, preci1, threshold_mean * probenum, preci2, sizescalebythreshold, calhes & (iter == iterations))
Im <- modfit$hes
para <- modfit$par
colnames(para) <- features_all
conv <- modfit$conv
}
message("probe finished")
if (iter == 1) {
features_remain <- names(which(colMeans(abs(para[seq_len(n_para), , drop = FALSE])) < cutoff))
if (prior_type == "equal") {
if (covrob) {
cov_mat <- robust::covRob(t(para[seq_len(n_para), features_remain]), na.action = na.omit)$cov
} else {
cov_mat <- cov(t(para[seq_len(n_para), features_remain]), use = "pairwise.complete.obs")
}
preci1 <- solve(cov_mat)
} else if (prior_type == "contrast") {
contrmat <- cbind(rep(1, n_para - 1), -diag(1, (n_para - 1)))
para_EB <- para[seq_len(n_para), features_remain]
contrpara <- contrmat %*% para_EB
if (covrob) {
cov_mat <- robust::covRob(t(contrpara), na.action = na.omit)$cov
} else {
cov_mat <- cov(t(contrpara), use = "pairwise.complete.obs")
}
preci1 <- t(contrmat) %*% solve(cov_mat) %*% contrmat
# avevec <- rep(1, n_para)/n_para
preci1 <- confac * preci1 + (preci1con) * t(contrvec) %*% contrvec
}
}
if (sizefactrec) {
size_scale <- match.arg(size_scale)
features_remain <- names(which(colMeans(abs(para[seq_len(n_para), , drop = FALSE])) < cutoff))
for (i in seq_len(length(sizefact))) {
fun <- PoisthNorm_scalenll(X[i, ], object[features_remain, i], probenum[features_remain], para[seq_len(n_para), features_remain], sizefact_BG[i], para[n_para + 1, features_remain], sizescalebythreshold, threshold_mean)
sizefact[i] <- optim(c(sizefact[i]), fun, lower = c(0), method = "L-BFGS-B")$par
}
if (size_scale == "first") {
scale_fac <- sizefact[1]
} else if (size_scale == "sum") {
scale_fac <- sum(sizefact)
}
sizefact <- sizefact / scale_fac
message(sprintf("Iteration = %s, squared error = %s", iter, sum((sizefact - sizefact0)^2)))
if (iter == 1) {
sizefact0 <- sizefact
}
}
}
normmat0 <- X %*% para0[seq_len(n_para), ]
normmat <- X %*% para[seq_len(n_para), ]
message("Model converged.")
return(list(
para0 = para0,
para = para,
normmat0 = normmat0,
normmat = normmat,
sizefact = sizefact,
sizefact0 = sizefact0,
preci1 = preci1,
Im0 = Im0,
Im = Im,
conv0 = conv0,
conv = conv,
features_high = features_high,
features_all = features_all
))
}
)
#' Poisson threshold model based normalization-log2 transformation for multiple slides
#'
#' Poisson threshold model based normalization-log2 transformation for multiple slides
#'
#' @param object count matrix with features in rows and samples in columns
#' @param probenum a vector of numbers of probes in each gene
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param id character vector of slide name of each sample
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param ... additional argument list that might be used
#'
#' @return a list of following items
#' \itemize{
#' \item threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows.
#' \item threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
#' \item sizefact, estimated sizefact
#' \item sizefact0, estimated sizefact in iter=1
#' \item preci1, precision matrix 1
#' \item Im0, Information matrix in iter=1
#' \item Im, Information matrix in iter=2
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged
#' \item features_high, same as the input features_high
#' \item features_all, same as the input features_all
#' }
#' @docType methods
#' @rdname fitPoisthNorm_sp-methods
setGeneric("fitPoisthNorm_sp",
signature = c("object"),
function(object, ...) standardGeneric("fitPoisthNorm_sp")
)
#' @rdname fitPoisthNorm_sp-methods
#' @aliases fitPoisthNorm_sp,matrix-method
setMethod(
"fitPoisthNorm_sp", "matrix",
function(object, probenum, features_high,
features_all = colnames(object), sizefact_start, sizefact_BG,
threshold_mean, preci2=10000, id, iterations = 2, prior_type = c("contrast", "equal"),
sizefactrec = TRUE, size_scale = c("sum", "first"), sizescalebythreshold = FALSE,
covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1) {
uniid <- unique(as.character(id))
loc <- lapply(uniid, function(x) which(id == x))
names(loc) <- uniid
normmod_ls <- lapply(uniid, function(x) {
fitPoisthNorm(object[, id == x],
probenum = probenum, features_high, features_all,
sizefact_start[id == x], sizefact_BG[id == x], threshold_mean, preci2, iterations, prior_type = prior_type, sizefactrec = sizefactrec, size_scale = size_scale, sizescalebythreshold = sizescalebythreshold,
covrob = covrob, preci1con = preci1con, cutoff = cutoff, confac = confac
)
})
names(normmod_ls) <- uniid
preci1 <- list()
threshold0 <- matrix(NA, length(uniid), length(features_high))
threshold <- matrix(NA, length(uniid), length(features_all))
rownames(threshold) <- rownames(threshold0) <- uniid
normmat0 <- matrix(NA, length(id), length(features_high))
normmat <- matrix(NA, length(id), length(features_all))
colnames(normmat) <- colnames(threshold) <- features_all
colnames(normmat0) <- colnames(threshold0) <- features_high
sizefact <- sizefact0 <- sizefact_BG
conv0 <- list()
conv <- list()
Im0 <- list()
Im <- list()
for (idname in uniid) {
threshold0[idname, ] <- normmod_ls[[idname]]$para0[length(loc[[idname]]) + 1, ]
threshold[idname, ] <- normmod_ls[[idname]]$para[length(loc[[idname]]) + 1, ]
normmat0[loc[[idname]], ] <- normmod_ls[[idname]]$normmat0
normmat[loc[[idname]], ] <- normmod_ls[[idname]]$normmat
sizefact[loc[[idname]]] <- normmod_ls[[idname]]$sizefact
sizefact0[loc[[idname]]] <- normmod_ls[[idname]]$sizefact0
Im0[[idname]] <- normmod_ls[[idname]]$Im0
Im[[idname]] <- normmod_ls[[idname]]$Im
conv0[[idname]] <- normmod_ls[[idname]]$conv0
conv[[idname]] <- normmod_ls[[idname]]$conv
preci1[[idname]] <- normmod_ls[[idname]]$preci1
}
return(list(
threshold0 = threshold0,
threshold = threshold,
normmat0 = normmat0,
normmat = normmat,
sizefact = sizefact,
sizefact0 = sizefact0,
preci1 = preci1,
Im0 = Im0,
Im = Im,
conv0 = conv0,
conv = conv,
features_high = features_high,
features_all = features_all
))
}
)
Nanostring-Biostats/GeoDiff documentation built on April 11, 2024, 5:31 a.m.
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#' Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides
#'
#' Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides
#'
#' @param object a valid GeoMx S4 object
#' @param split indicator variable on whether it is for multiple slides (Yes, TRUE; No, FALSE)
#' @param ROIs_high ROIs with high expressions defined based on featfact and featfact
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param calhes The user input whether to calculate hessian: calhes, default=FALSE
#' @param ... additional argument list that might be used
#'
#' @importFrom Biobase pData
#' @importFrom Biobase fData
#' @importFrom Biobase exprs
#' @importFrom Biobase assayDataElement
#'
#'
#' @return if split is FALSE, a valid GeoMx S4 object including the following items
#' \itemize{
#' \item para0_norm, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows, in featureData.
#' \item para_norm, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows, in featureData.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay lot.
#' \item sizefact_norm, estimated sizefact, in phenoData.
#' \item sizefact0_norm, estimated sizefact in iter=1, in phenoData.
#' \item preci1, precision matrix 1, in experimentData.
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged, in featureData
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged, in featureData
#' \item features_high, same as the input features_high, in featureData
#' \item features_all, same as the input features_all, in featureData
#' }
#'
#' if split is TRUE, a valid GeoMx S4 object with the following items appended.
#' \itemize{
#' \item threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows, in featureData.
#' \item threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows, in featureData.
#' \item normmat0_sp, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.
#' \item normmat_sp, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay slot.
#' \item sizefact_norm_sp, estimated sizefact, in phenoData
#' \item sizefact0_norm_sp, estimated sizefact in iter=1, in phenoData
#' \item preci1, precision matrix 1, in experimentData
#' \item conv0_sp_XX, vector of convergence for each unique slide value for iter=1, 0 converged, 1 not converged, in featureData for each unique slide.
#' \item conv_sp_XX, vector of convergence for each unique slide value for iter=2, 0 converged, 1 not converged, in featureData for each unique slide.
#' \item features_high_sp, same as the input features_high, in featureData.
#' \item features_all_sp, same as the input features_all, in featureData.
#' }
#'
#' @importFrom Biobase sampleNames
#' @importFrom Biobase featureNames
#' @importFrom Biobase annotation
#'
#' @examples
#'
#' library(Biobase)
#' library(dplyr)
#' data(demoData)
#' demoData <- fitPoisBG(demoData, size_scale = "sum")
#' demoData <- aggreprobe(demoData, use = "cor")
#' demoData <- BGScoreTest(demoData)
#' thmean <- 1 * mean(fData(demoData)$featfact, na.rm = TRUE)
#' demo_pos <- demoData[which(!fData(demoData)$CodeClass == "Negative"), ]
#' demo_neg <- demoData[which(fData(demoData)$CodeClass == "Negative"), ]
#' sc1_scores <- fData(demo_pos)[, "scores"]
#' names(sc1_scores) <- fData(demo_pos)[, "TargetName"]
#' features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) &
#' (sc1_scores < quantile(sc1_scores, probs = 0.95))) |>
#' which() |>
#' names()
#' set.seed(123)
#' features_high <- sample(features_high, 100)
#' demoData <- fitNBth(demoData,
#' features_high = features_high,
#' sizefact_BG = demo_neg$sizefact,
#' threshold_start = thmean,
#' iterations = 5,
#' start_para = c(200, 1),
#' lower_sizefact = 0,
#' lower_threshold = 100,
#' tol = 1e-8)
#' ROIs_high <- sampleNames(demoData)[which((quantile(fData(demoData)[["para"]][, 1],
#' probs = 0.90, na.rm = TRUE) -
#' notes(demoData)[["threshold"]]) * demoData$sizefact_fitNBth > 2)]
#' features_all <- rownames(demo_pos)
#' thmean <- mean(fData(demo_neg)[["featfact"]])
#' demoData <- fitPoisthNorm(
#' object = demoData,
#' split = FALSE,
#' ROIs_high = ROIs_high,
#' features_high = features_high,
#' features_all = features_all,
#' sizefact_start = demoData[, ROIs_high][["sizefact_fitNBth"]],
#' sizefact_BG = demoData[, ROIs_high][["sizefact"]],
#' threshold_mean = thmean,
#' preci2 = 10000,
#' prior_type = "contrast",
#' covrob = FALSE,
#' preci1con = 1 / 25
#' )
#'
#' @export
#' @docType methods
#' @rdname fitPoisthNorm-methods
setGeneric("fitPoisthNorm",
signature = c("object"),
function(object, ...) standardGeneric("fitPoisthNorm")
)
#' @rdname fitPoisthNorm-methods
#' @aliases fitPoisthNorm,NanoStringGeoMxSet-method
setMethod(
"fitPoisthNorm", "NanoStringGeoMxSet",
function(object, split = FALSE, ROIs_high = NULL, features_high = NULL,
features_all = NULL, sizefact_start = NULL, sizefact_BG = NULL,
threshold_mean = NULL, preci2=10000, iterations = 2, prior_type = c("contrast", "equal"),
sizefactrec = TRUE, size_scale = c("sum", "first"), sizescalebythreshold = FALSE,
covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1, calhes = FALSE) {
# calculate backmean
# setting default values for sizefact_BG
fDat <- Biobase::fData(object)
fDatNeg <- fDat[which(fDat$CodeClass == "Negative"), ]
# only calculate backmean if any of the three params are missing
if (any(c(is.null(sizefact_BG), is.null(sizefact_start), is.null(ROIs_high), is.null(threshold_mean)))) {
if (isFALSE(split)) {
# single slide
if (!("sizefact" %in% varLabels(object))) {
stop("Please run `fitPoisBG` first.")
} else {
# calculate the backmean for WTA or CTA data
thmean <- mean(fDatNeg[["featfact"]])
}
} else {
# multiple slides
if (!("sizefact_sp" %in% varLabels(object))) {
stop("Please run `fitPoisBG` first with `groupvar`.")
} else {
# calculate the backmean for WTA or CTA data
thmean <- colMeans(fDatNeg[, grep("featfact_", fvarLabels(object))])[1]
}
}
}
# setting default value for ROIs_high
if (is.null(ROIs_high)) {
if (!("sizefact_fitNBth" %in% varLabels(object))) {
stop("Please run `fitNBth` first.")
} else {
ROIs_high <- Biobase::sampleNames(object)[which((quantile(fData(object)[["para"]][, 1],
probs = 0.90, na.rm = TRUE
) - notes(object)[["threshold"]]) * object$sizefact_fitNBth > 2)]
}
}
object_high <- object[, ROIs_high]
fDat <- Biobase::fData(object_high)
pDat <- Biobase::pData(object_high)
posdat <- object_high[-which(fDat$CodeClass == "Negative"), ]
countmat <- Biobase::exprs(posdat)
# calculate probenum for the dataset
if ("probenum" %in% fvarLabels(posdat)) {
probenum <- fData(posdat)[["probenum"]]
} else {
stop("No `probenum` is found. Run `aggreprobe` first.")
}
names(probenum) <- rownames(fData(posdat))
# setting default value for sizefact_BG
if (is.null(sizefact_BG)) {
if (isFALSE(split)) {
# single slide
sizefact_BG <- pDat[["sizefact"]]
} else {
# multiple slides
sizefact_BG <- pDat[["sizefact_sp"]]
}
}
# setting default value for sizefact_start
if (is.null(sizefact_start)) {
if (!("sizefact_fitNBth" %in% colnames(pDat))) {
stop("Please run `fitNBth` first.")
} else {
sizefact_start <- pDat[["sizefact_fitNBth"]]
}
}
# setting default value for features_all
if (is.null(features_all)) {
gene_sum <- rowSums(countmat)
if (any(grepl("WTA", toupper(Biobase::annotation(object))))) {
features_all <- rownames(countmat)
} else if (any(grepl("CTA", toupper(Biobase::annotation(object))))) {
features_all <- rownames(countmat)
} else {
stop("No information is found to determine the data type (CTA or WTA).")
}
}
# setting default value for sizefact_start
if (is.null(threshold_mean)) {
threshold_mean <- thmean
}
# setting default value for features_high
if (is.null(features_high)) {
gene_sum <- rowSums(countmat)
if (any(grepl("WTA", toupper(Biobase::annotation(object))))) {
features_high <- names(which(((gene_sum > quantile(gene_sum, probs = 0.5)) & (gene_sum < quantile(gene_sum, probs = 0.95)))))
features_high <- sort(sample(features_high, 1500))
} else if (any(grepl("CTA", toupper(Biobase::annotation(object))))) {
if (any(grepl("scores", fvarLabels(object)))) {
if (split == TRUE) {
sc1_scores <- fData(object)[-which(fData(object)$Negative), grepl("scores_", fvarLabels(object))]
rownames(sc1_scores) <- fData(object)[-which(fData(object)$Negative), "TargetName"]
features_high <- apply(sc1_scores, 2, function(x) {
((x > quantile(x, probs = 0.4)) & (x < quantile(x, probs = 0.95)))
})
features_high <- names(which(apply(features_high, 1, all)))
} else {
sc1_scores <- fData(object)[-which(fData(object)$Negative), "scores"]
names(sc1_scores) <- fData(object)[-which(fData(object)$Negative), "TargetName"]
features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) & (sc1_scores < quantile(sc1_scores, probs = 0.95)))
features_high <- names(which(features_high))
}
} else {
stop("Please run score test first using `BGScoreTest`.")
}
} else {
stop("No information is found to determine the data type (CTA or WTA).")
}
}
if (isFALSE(split)) {
result <- fitPoisthNorm(
object = countmat,
probenum = probenum,
features_high = features_high,
features_all = features_all,
sizefact_start = sizefact_start,
sizefact_BG = sizefact_BG,
threshold_mean = threshold_mean,
preci2 = preci2,
iterations = iterations,
prior_type = prior_type,
sizefactrec = sizefactrec,
size_scale = size_scale,
sizescalebythreshold = sizescalebythreshold,
covrob = covrob,
preci1con = preci1con,
cutoff = cutoff,
confac = confac,
calhes = calhes
)
# para0
Biobase::fData(object)[["para0_norm"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$para0),
dimnames = list(Biobase::featureNames(object), paste0("var", seq_len((nrow(result$para0)))))
)
Biobase::fData(object)[["para0_norm"]][colnames(result$para0), ] <- t(result$para0)
# para
Biobase::fData(object)[["para_norm"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$para),
dimnames = list(Biobase::featureNames(object), paste0("var", seq_len((nrow(result$para)))))
)
Biobase::fData(object)[["para_norm"]][colnames(result$para), ] <- t(result$para)
# normmat0
normmat0_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat0_mat[colnames(result$normmat0), ROIs_high] <- t(result$normmat0)
Biobase::assayDataElement(object, "normmat0") <- normmat0_mat
# normmat
normmat_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat_mat[colnames(result$normmat), ROIs_high] <- t(result$normmat)
Biobase::assayDataElement(object, "normmat") <- normmat_mat
# sizefact
Biobase::pData(object)[ROIs_high, "sizefact_norm"] <- result$sizefact
# sizefac0
Biobase::pData(object)[ROIs_high, "sizefact0_norm"] <- result$sizefact0
# preci1
Biobase::notes(object)$preci1_norm <- result$preci1
# skipping appending Im0 and Im from result for now.
# conv0
Biobase::fData(object)[features_high, "conv0"] <- result$conv0
# conv
Biobase::fData(object)[features_all, "conv"] <- result$conv
# features_high
Biobase::fData(object)[features_high, "features_high"] <- 1
# features_all
Biobase::fData(object)[features_all, "features_all"] <- 1
} else {
if (is.null(Biobase::notes(object)$fitPoisBG_sp_var)) {
stop("Please run `fitPoisBG` first with `groupvar`.")
} else {
idvar <- Biobase::notes(object)$fitPoisBG_sp_var
id <- Biobase::pData(object)[[idvar]][match(ROIs_high, colnames(object))]
message(sprintf("The results are based on stored `groupvar`, %s", idvar))
}
result <- fitPoisthNorm_sp(
object = countmat,
probenum = probenum,
features_high = features_high,
features_all = features_all,
sizefact_start = sizefact_start,
sizefact_BG = sizefact_BG,
threshold_mean = threshold_mean,
preci2 = preci2,
id = id,
iterations = iterations,
prior_type = prior_type,
sizefactrec = sizefactrec,
size_scale = size_scale,
sizescalebythreshold = sizescalebythreshold,
covrob = covrob,
preci1con = preci1con,
cutoff = cutoff,
confac = confac
)
# threshold0
Biobase::fData(object)[["threshold0"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$threshold0),
dimnames = list(Biobase::featureNames(object), rownames(result$threshold0))
)
Biobase::fData(object)[["threshold0"]][colnames(result$threshold0), ] <- t(result$threshold0)
# threshold
Biobase::fData(object)[["threshold"]] <- matrix(NA,
nrow = nrow(object), ncol = nrow(result$threshold),
dimnames = list(Biobase::featureNames(object), rownames(result$threshold))
)
Biobase::fData(object)[["threshold"]][colnames(result$threshold), ] <- t(result$threshold)
# normmat0
normmat0_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat0_mat[colnames(result$normmat0), ROIs_high] <- t(result$normmat0)
Biobase::assayDataElement(object, "normmat0_sp") <- normmat0_mat
# normmat
normmat_mat <- matrix(NA,
nrow = nrow(object), ncol = ncol(object),
dimnames = dimnames(object)
)
normmat_mat[colnames(result$normmat), ROIs_high] <- t(result$normmat)
Biobase::assayDataElement(object, "normmat_sp") <- normmat_mat
# sizefact
Biobase::pData(object)[ROIs_high, "sizefact_norm_sp"] <- result$sizefact
# sizefact0
Biobase::pData(object)[ROIs_high, "sizefact0_norm_sp"] <- result$sizefact0
# preci1
Biobase::notes(object)$preci1_norm_sp <- result$preci1
# skipping appending Im0 and Im from result for now.
# conv0
for (index in names(result$conv0)) {
Biobase::fData(object)[features_high, paste0("conv0_sp_", index)] <- result$conv0[[index]]
}
# conv
for (index in names(result$conv0)) {
Biobase::fData(object)[features_all, paste0("conv_sp_", index)] <- result$conv[[index]]
}
# features_high
Biobase::fData(object)[features_high, "features_high_sp"] <- 1
# features_all
Biobase::fData(object)[features_all, "features_all_sp"] <- 1
}
return(object)
}
)
#'
#' Poisson model based normalization and log2 transformation with threshold
#'
#' @param object count matrix with features in rows and samples in columns
#' @param probenum a vector of numbers of probes in each gene
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param calhes The user input whether to calculate hessian: calhes, default=FALSE
#'
#' @importFrom robust covRob
#'
#' @return a list of following items
#' \itemize{
#' \item para0, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows.
#' \item para, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
#' \item sizefact, estimated sizefact
#' \item sizefact0, estimated sizefact in iter=1
#' \item preci1, precision matrix 1
#' \item Im0, Information matrix of parameters in iter=1
#' \item Im, Information matrix of parameters in iter=2
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged
#' \item features_high, same as the input features_high
#' \item features_all, same as the input features_all
#' }
#'
#' @rdname fitPoisthNorm-methods
#' @aliases fitPoisthNorm,matrix-method
setMethod(
"fitPoisthNorm", "matrix",
function(object, probenum = rep(1, NROW(object)), features_high, features_all,
sizefact_start, sizefact_BG, threshold_mean, preci2=10000, iterations = 2,
prior_type = c("contrast", "equal"), sizefactrec = TRUE, size_scale = c("sum", "first"),
sizescalebythreshold = FALSE, covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1, calhes = FALSE) {
if (iterations != 2) {
stop("Only iterations=2 is allowed")
}
if (is.null(names(probenum))) names(probenum) <- rownames(object)
sizefact0 <- sizefact <- sizefact_start
n_para <- ncol(object)
X <- diag(1, n_para)
prior_type <- match.arg(prior_type)
if (prior_type == "equal") {
preci1 <- preci1con * t(X) %*% diag(1, n_para) %*% X
} else if (prior_type == "contrast") {
contrvec <- t(rep(1 / n_para, n_para)) %*% X
preci1 <- (preci1con) * t(contrvec) %*% contrvec
}
for (iter in seq_len(iterations)) {
if (iter == 1) {
modfit <- PoisthNorm_paraOptall(t(object[features_high, ]), X, sizefact_BG, sizefact, preci1, threshold_mean * probenum, preci2, sizescalebythreshold, calhes & (iter == iterations))
Im <- modfit$hes
Im0 <- Im
para <- modfit$par
colnames(para) <- features_high
para0 <- para
conv0 <- modfit$conv
} else {
modfit <- PoisthNorm_paraOptall(t(object[features_all, ]), X, sizefact_BG, sizefact, preci1, threshold_mean * probenum, preci2, sizescalebythreshold, calhes & (iter == iterations))
Im <- modfit$hes
para <- modfit$par
colnames(para) <- features_all
conv <- modfit$conv
}
message("probe finished")
if (iter == 1) {
features_remain <- names(which(colMeans(abs(para[seq_len(n_para), , drop = FALSE])) < cutoff))
if (prior_type == "equal") {
if (covrob) {
cov_mat <- robust::covRob(t(para[seq_len(n_para), features_remain]), na.action = na.omit)$cov
} else {
cov_mat <- cov(t(para[seq_len(n_para), features_remain]), use = "pairwise.complete.obs")
}
preci1 <- solve(cov_mat)
} else if (prior_type == "contrast") {
contrmat <- cbind(rep(1, n_para - 1), -diag(1, (n_para - 1)))
para_EB <- para[seq_len(n_para), features_remain]
contrpara <- contrmat %*% para_EB
if (covrob) {
cov_mat <- robust::covRob(t(contrpara), na.action = na.omit)$cov
} else {
cov_mat <- cov(t(contrpara), use = "pairwise.complete.obs")
}
preci1 <- t(contrmat) %*% solve(cov_mat) %*% contrmat
# avevec <- rep(1, n_para)/n_para
preci1 <- confac * preci1 + (preci1con) * t(contrvec) %*% contrvec
}
}
if (sizefactrec) {
size_scale <- match.arg(size_scale)
features_remain <- names(which(colMeans(abs(para[seq_len(n_para), , drop = FALSE])) < cutoff))
for (i in seq_len(length(sizefact))) {
fun <- PoisthNorm_scalenll(X[i, ], object[features_remain, i], probenum[features_remain], para[seq_len(n_para), features_remain], sizefact_BG[i], para[n_para + 1, features_remain], sizescalebythreshold, threshold_mean)
sizefact[i] <- optim(c(sizefact[i]), fun, lower = c(0), method = "L-BFGS-B")$par
}
if (size_scale == "first") {
scale_fac <- sizefact[1]
} else if (size_scale == "sum") {
scale_fac <- sum(sizefact)
}
sizefact <- sizefact / scale_fac
message(sprintf("Iteration = %s, squared error = %s", iter, sum((sizefact - sizefact0)^2)))
if (iter == 1) {
sizefact0 <- sizefact
}
}
}
normmat0 <- X %*% para0[seq_len(n_para), ]
normmat <- X %*% para[seq_len(n_para), ]
message("Model converged.")
return(list(
para0 = para0,
para = para,
normmat0 = normmat0,
normmat = normmat,
sizefact = sizefact,
sizefact0 = sizefact0,
preci1 = preci1,
Im0 = Im0,
Im = Im,
conv0 = conv0,
conv = conv,
features_high = features_high,
features_all = features_all
))
}
)
#' Poisson threshold model based normalization-log2 transformation for multiple slides
#'
#' Poisson threshold model based normalization-log2 transformation for multiple slides
#'
#' @param object count matrix with features in rows and samples in columns
#' @param probenum a vector of numbers of probes in each gene
#' @param features_high subset of features which are well above the background
#' @param features_all full feature vector to apply the normalization on
#' @param sizefact_start initial value for size factors
#' @param sizefact_BG size factor for background
#' @param threshold_mean average threshold level
#' @param preci2 precision for threshold, default=10000
#' @param id character vector of slide name of each sample
#' @param iterations iteration number, default=2,
#' the first iteration using the features_high to construct the prior for parameters then refit the model on all features.
#' precision matrix for threshold: preci2
#' @param prior_type prior type for preci1, "equal" or "contrast", default="contrast"
#' @param sizefactrec XXXX, default = TRUE
#' @param size_scale method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
#' @param sizescalebythreshold XXXX, default = FALSE
#' @param covrob whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
#' @param preci1con The user input constant term in specifying precision matrix 1, default=1/25
#' @param cutoff term in calculating precision matrix 1, default=15
#' @param confac The user input factor for contrast in precision matrix 1, default=1
#' @param ... additional argument list that might be used
#'
#' @return a list of following items
#' \itemize{
#' \item threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows.
#' \item threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows.
#' \item normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
#' \item normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
#' \item sizefact, estimated sizefact
#' \item sizefact0, estimated sizefact in iter=1
#' \item preci1, precision matrix 1
#' \item Im0, Information matrix in iter=1
#' \item Im, Information matrix in iter=2
#' \item conv0, vector of convergence for iter=1, 0 converged, 1 not converged
#' \item conv, vector of convergence for iter=2, 0 converged, 1 not converged
#' \item features_high, same as the input features_high
#' \item features_all, same as the input features_all
#' }
#' @docType methods
#' @rdname fitPoisthNorm_sp-methods
setGeneric("fitPoisthNorm_sp",
signature = c("object"),
function(object, ...) standardGeneric("fitPoisthNorm_sp")
)
#' @rdname fitPoisthNorm_sp-methods
#' @aliases fitPoisthNorm_sp,matrix-method
setMethod(
"fitPoisthNorm_sp", "matrix",
function(object, probenum, features_high,
features_all = colnames(object), sizefact_start, sizefact_BG,
threshold_mean, preci2=10000, id, iterations = 2, prior_type = c("contrast", "equal"),
sizefactrec = TRUE, size_scale = c("sum", "first"), sizescalebythreshold = FALSE,
covrob = FALSE, preci1con = 1 / 25, cutoff = 15, confac = 1) {
uniid <- unique(as.character(id))
loc <- lapply(uniid, function(x) which(id == x))
names(loc) <- uniid
normmod_ls <- lapply(uniid, function(x) {
fitPoisthNorm(object[, id == x],
probenum = probenum, features_high, features_all,
sizefact_start[id == x], sizefact_BG[id == x], threshold_mean, preci2, iterations, prior_type = prior_type, sizefactrec = sizefactrec, size_scale = size_scale, sizescalebythreshold = sizescalebythreshold,
covrob = covrob, preci1con = preci1con, cutoff = cutoff, confac = confac
)
})
names(normmod_ls) <- uniid
preci1 <- list()
threshold0 <- matrix(NA, length(uniid), length(features_high))
threshold <- matrix(NA, length(uniid), length(features_all))
rownames(threshold) <- rownames(threshold0) <- uniid
normmat0 <- matrix(NA, length(id), length(features_high))
normmat <- matrix(NA, length(id), length(features_all))
colnames(normmat) <- colnames(threshold) <- features_all
colnames(normmat0) <- colnames(threshold0) <- features_high
sizefact <- sizefact0 <- sizefact_BG
conv0 <- list()
conv <- list()
Im0 <- list()
Im <- list()
for (idname in uniid) {
threshold0[idname, ] <- normmod_ls[[idname]]$para0[length(loc[[idname]]) + 1, ]
threshold[idname, ] <- normmod_ls[[idname]]$para[length(loc[[idname]]) + 1, ]
normmat0[loc[[idname]], ] <- normmod_ls[[idname]]$normmat0
normmat[loc[[idname]], ] <- normmod_ls[[idname]]$normmat
sizefact[loc[[idname]]] <- normmod_ls[[idname]]$sizefact
sizefact0[loc[[idname]]] <- normmod_ls[[idname]]$sizefact0
Im0[[idname]] <- normmod_ls[[idname]]$Im0
Im[[idname]] <- normmod_ls[[idname]]$Im
conv0[[idname]] <- normmod_ls[[idname]]$conv0
conv[[idname]] <- normmod_ls[[idname]]$conv
preci1[[idname]] <- normmod_ls[[idname]]$preci1
}
return(list(
threshold0 = threshold0,
threshold = threshold,
normmat0 = normmat0,
normmat = normmat,
sizefact = sizefact,
sizefact0 = sizefact0,
preci1 = preci1,
Im0 = Im0,
Im = Im,
conv0 = conv0,
conv = conv,
features_high = features_high,
features_all = features_all
))
}
)
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