| Title: | Normalize Gene Expression Data using Evaluated Methods |
|---|---|
| Description: | It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets. Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>. |
| Authors: | Zhenfeng Wu , Shan Gao |
| Maintainer: | Shan Gao <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 0.1.1 |
| Built: | 2025-02-14 03:24:28 UTC |
| Source: | https://github.com/cran/NormExpression |
Please refer to the file /inst/doc/readme.pdf.
data("bkRNA18")data("bkRNA18")
A data frame with 57955 observations on the following 18 variables.
col36l6_1a numeric vector
col38l6_3a numeric vector
col39l6_5a numeric vector
col40l6_7a numeric vector
col44l6_9a numeric vector
col45l6_11a numeric vector
col47l6_13a numeric vector
col48l6_97a numeric vector
col52l6_17a numeric vector
col36l6_2a numeric vector
col38l6_4a numeric vector
col39l6_6a numeric vector
col40l6_8a numeric vector
col44l6_10a numeric vector
col45l6_12a numeric vector
col47l6_14a numeric vector
col48l6_98a numeric vector
col52l6_18a numeric vector
data(bkRNA18) ## maybe str(bkRNA18) ; plot(bkRNA18) ...data(bkRNA18) ## maybe str(bkRNA18) ; plot(bkRNA18) ...
Please refer to the file /inst/doc/readme.pdf.
data("bkRNA18_factors")data("bkRNA18_factors")
A data frame with 18 observations on the following 13 variables.
HG7a numeric vector
ERCCa numeric vector
TNa numeric vector
TCa numeric vector
CRa numeric vector
NRa numeric vector
DESeqa numeric vector
UQa numeric vector
TMMa numeric vector
TUa numeric vector
NCSa numeric vector
ESa numeric vector
GAPDHa numeric vector
data(bkRNA18_factors) ## maybe str(bkRNA18_factors) ; plot(bkRNA18_factors) ...data(bkRNA18_factors) ## maybe str(bkRNA18_factors) ; plot(bkRNA18_factors) ...
Please refer to the file /inst/doc/readme.pdf.
calcFactorRLE(data, p = p)calcFactorRLE(data, p = p)
data |
Please refer to the file /inst/doc/readme.pdf. |
p |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, p = p) { gm <- exp(rowMeans(.log(data), na.rm = TRUE)) apply(data, 2, function(u) quantile((u/gm)[u != 0], na.rm = TRUE, p = p)) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, p = p) { gm <- exp(rowMeans(.log(data), na.rm = TRUE)) apply(data, 2, function(u) quantile((u/gm)[u != 0], na.rm = TRUE, p = p)) }
Please refer to the file /inst/doc/readme.pdf.
calcFactorUpperquartile(data, lib.size, p = p)calcFactorUpperquartile(data, lib.size, p = p)
data |
Please refer to the file /inst/doc/readme.pdf. |
lib.size |
Please refer to the file /inst/doc/readme.pdf. |
p |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, lib.size, p = p) { y <- t(t(data)/lib.size) f <- apply(y, 2, function(x) quantile(x[x != 0], p = p)) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, lib.size, p = p) { y <- t(t(data)/lib.size) f <- apply(y, 2, function(x) quantile(x[x != 0], p = p)) }
Please refer to the file /inst/doc/readme.pdf.
calcFactorWeighted(obs, ref, libsize.obs, libsize.ref, logratioTrim, sumTrim, doWeighting, Acutoff)calcFactorWeighted(obs, ref, libsize.obs, libsize.ref, logratioTrim, sumTrim, doWeighting, Acutoff)
obs |
Please refer to the file /inst/doc/readme.pdf. |
ref |
Please refer to the file /inst/doc/readme.pdf. |
libsize.obs |
Please refer to the file /inst/doc/readme.pdf. |
libsize.ref |
Please refer to the file /inst/doc/readme.pdf. |
logratioTrim |
Please refer to the file /inst/doc/readme.pdf. |
sumTrim |
Please refer to the file /inst/doc/readme.pdf. |
doWeighting |
Please refer to the file /inst/doc/readme.pdf. |
Acutoff |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (obs, ref, libsize.obs = NULL, libsize.ref = NULL, logratioTrim = 0.3, sumTrim = 0.05, doWeighting = TRUE, Acutoff = -1e+10) { if (all(obs == ref)) return(1) obs <- as.numeric(obs) ref <- as.numeric(ref) if (is.null(libsize.obs)) nO <- sum(obs) else nO <- libsize.obs if (is.null(libsize.ref)) nR <- sum(ref) else nR <- libsize.ref logR <- log2((obs/nO)/(ref/nR)) absE <- (log2(obs/nO) + log2(ref/nR))/2 v <- (nO - obs)/nO/obs + (nR - ref)/nR/ref fin <- is.finite(logR) & is.finite(absE) & (absE > Acutoff) logR <- logR[fin] absE <- absE[fin] v <- v[fin] n <- length(logR) loL <- floor(n * logratioTrim) + 1 hiL <- n + 1 - loL loS <- floor(n * sumTrim) + 1 hiS <- n + 1 - loS keep <- (rank(logR) >= loL & rank(logR) <= hiL) & (rank(absE) >= loS & rank(absE) <= hiS) if (doWeighting) { 2^(sum(logR[keep]/v[keep], na.rm = TRUE)/sum(1/v[keep], na.rm = TRUE)) } else { 2^(mean(logR[keep], na.rm = TRUE)) } }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (obs, ref, libsize.obs = NULL, libsize.ref = NULL, logratioTrim = 0.3, sumTrim = 0.05, doWeighting = TRUE, Acutoff = -1e+10) { if (all(obs == ref)) return(1) obs <- as.numeric(obs) ref <- as.numeric(ref) if (is.null(libsize.obs)) nO <- sum(obs) else nO <- libsize.obs if (is.null(libsize.ref)) nR <- sum(ref) else nR <- libsize.ref logR <- log2((obs/nO)/(ref/nR)) absE <- (log2(obs/nO) + log2(ref/nR))/2 v <- (nO - obs)/nO/obs + (nR - ref)/nR/ref fin <- is.finite(logR) & is.finite(absE) & (absE > Acutoff) logR <- logR[fin] absE <- absE[fin] v <- v[fin] n <- length(logR) loL <- floor(n * logratioTrim) + 1 hiL <- n + 1 - loL loS <- floor(n * sumTrim) + 1 hiS <- n + 1 - loS keep <- (rank(logR) >= loL & rank(logR) <= hiL) & (rank(absE) >= loS & rank(absE) <= hiS) if (doWeighting) { 2^(sum(logR[keep]/v[keep], na.rm = TRUE)/sum(1/v[keep], na.rm = TRUE)) } else { 2^(mean(logR[keep], na.rm = TRUE)) } }
Please refer to the file /inst/doc/readme.pdf.
change_colours(p, palette, type)change_colours(p, palette, type)
p |
Please refer to the file /inst/doc/readme.pdf. |
palette |
Please refer to the file /inst/doc/readme.pdf. |
type |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (p, palette, type) { n <- nlevels(p$data[[deparse(p$mapping$group)]]) tryCatch(as.character(palette), error = function(e) stop("be vector",call. = FALSE)) if (n > length(palette)) stop("Not enough colours in palette.") if (missing(type)) type <- grep("colour|fill", names(p$layers[[1]]$mapping), value = TRUE)[1] pal <- function(n) palette[seq_len(n)] p + discrete_scale(type, "foo", pal) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (p, palette, type) { n <- nlevels(p$data[[deparse(p$mapping$group)]]) tryCatch(as.character(palette), error = function(e) stop("be vector",call. = FALSE)) if (n > length(palette)) stop("Not enough colours in palette.") if (missing(type)) type <- grep("colour|fill", names(p$layers[[1]]$mapping), value = TRUE)[1] pal <- function(n) palette[seq_len(n)] p + discrete_scale(type, "foo", pal) }
Please refer to the file /inst/doc/readme.pdf.
CV2AUCVC(data, cvResolution = 0.005)CV2AUCVC(data, cvResolution = 0.005)
data |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cvResolution = 0.005) { cv_cutoff <- NULL uniform_genes_counts <- NULL for (i in seq(0, 1, cvResolution)) { cv_cutoff <- c(cv_cutoff, i) gene_number <- length(which(data <= i)) uniform_genes_counts <- c(uniform_genes_counts, gene_number) } getArea(cv_cutoff, uniform_genes_counts) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cvResolution = 0.005) { cv_cutoff <- NULL uniform_genes_counts <- NULL for (i in seq(0, 1, cvResolution)) { cv_cutoff <- c(cv_cutoff, i) gene_number <- length(which(data <= i)) uniform_genes_counts <- c(uniform_genes_counts, gene_number) } getArea(cv_cutoff, uniform_genes_counts) }
Please refer to the file /inst/doc/readme.pdf.
estimateSizeFactorsForMatrix(data, p = p)estimateSizeFactorsForMatrix(data, p = p)
data |
Please refer to the file /inst/doc/readme.pdf. |
p |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, p = p) { loggeomeans <- rowMeans(.log(data), na.rm = TRUE) apply(data, 2, function(cnts) exp(quantile(.log(cnts) - loggeomeans, na.rm = TRUE, p = p))) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, p = p) { loggeomeans <- rowMeans(.log(data), na.rm = TRUE) apply(data, 2, function(cnts) exp(quantile(.log(cnts) - loggeomeans, na.rm = TRUE, p = p))) }
Please refer to the file /inst/doc/readme.pdf.
filteredZero(data, nonzeroRatio)filteredZero(data, nonzeroRatio)
data |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio) { nozeroCount <- apply(data, 1, function(x) length(which(x != 0))) geneIndex <- which(nozeroCount >= ncol(data) * nonzeroRatio) return(geneIndex) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio) { nozeroCount <- apply(data, 1, function(x) length(which(x != 0))) geneIndex <- which(nozeroCount >= ncol(data) * nonzeroRatio) return(geneIndex) }
Please refer to the file /inst/doc/readme.pdf.
findGenes(g, qlower = NULL, qupper = NULL, pre_ratio = NULL)findGenes(g, qlower = NULL, qupper = NULL, pre_ratio = NULL)
g |
Please refer to the file /inst/doc/readme.pdf. |
qlower |
Please refer to the file /inst/doc/readme.pdf. |
qupper |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (g, qlower = NULL, qupper = NULL, pre_ratio = NULL) { gene_name <- rownames(g) g <- unlist(g) seen <- which(g >= qlower & g <= qupper) counts <- length(seen) if (counts >= pre_ratio * length(g)) { gene_name } }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (g, qlower = NULL, qupper = NULL, pre_ratio = NULL) { gene_name <- rownames(g) g <- unlist(g) seen <- which(g >= qlower & g <= qupper) counts <- length(seen) if (counts >= pre_ratio * length(g)) { gene_name } }
Please refer to the file /inst/doc/readme.pdf.
gatherCors(data, cor_method = c("spearman", "pearson", "kendall"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06)gatherCors(data, cor_method = c("spearman", "pearson", "kendall"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06)
data |
Please refer to the file /inst/doc/readme.pdf. |
cor_method |
Please refer to the file /inst/doc/readme.pdf. |
HG7 |
Please refer to the file /inst/doc/readme.pdf. |
ERCC |
Please refer to the file /inst/doc/readme.pdf. |
TN |
Please refer to the file /inst/doc/readme.pdf. |
TC |
Please refer to the file /inst/doc/readme.pdf. |
CR |
Please refer to the file /inst/doc/readme.pdf. |
NR |
Please refer to the file /inst/doc/readme.pdf. |
DESeq |
Please refer to the file /inst/doc/readme.pdf. |
UQ |
Please refer to the file /inst/doc/readme.pdf. |
TMM |
Please refer to the file /inst/doc/readme.pdf. |
TU |
Please refer to the file /inst/doc/readme.pdf. |
GAPDH |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
lower_trim |
Please refer to the file /inst/doc/readme.pdf. |
upper_trim |
Please refer to the file /inst/doc/readme.pdf. |
rounds |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cor_method = c("spearman", "pearson", "kendall"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06) { methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] numMethod <- length(specifiedMethods) method_range <- seq(1, numMethod, 1) ubq_genes <- identifyUbq(data, pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = 100) cor_value_method <- NULL for (j in method_range) { norm.matrix <- getNormMatrix(data, specifiedMethods[[j]]) dataUse2Cor <- norm.matrix[ubq_genes, ] cor.result <- getCor(dataUse2Cor, method = cor_method, rounds = rounds) cor_vm <- cbind(cor.result, rep(names(specifiedMethods)[j], times = round(rounds))) cor_value_method <- rbind(cor_value_method, cor_vm) } colnames(cor_value_method) <- c("Value", "Methods") return(cor_value_method) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cor_method = c("spearman", "pearson", "kendall"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06) { methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] numMethod <- length(specifiedMethods) method_range <- seq(1, numMethod, 1) ubq_genes <- identifyUbq(data, pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = 100) cor_value_method <- NULL for (j in method_range) { norm.matrix <- getNormMatrix(data, specifiedMethods[[j]]) dataUse2Cor <- norm.matrix[ubq_genes, ] cor.result <- getCor(dataUse2Cor, method = cor_method, rounds = rounds) cor_vm <- cbind(cor.result, rep(names(specifiedMethods)[j], times = round(rounds))) cor_value_method <- rbind(cor_value_method, cor_vm) } colnames(cor_value_method) <- c("Value", "Methods") return(cor_value_method) }
Please refer to the file /inst/doc/readme.pdf.
gatherCors4Matrices(..., raw_matrix, cor_method = c("spearman", "pearson", "kendall"), pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06)gatherCors4Matrices(..., raw_matrix, cor_method = c("spearman", "pearson", "kendall"), pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06)
... |
Please refer to the file /inst/doc/readme.pdf. |
raw_matrix |
Please refer to the file /inst/doc/readme.pdf. |
cor_method |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
lower_trim |
Please refer to the file /inst/doc/readme.pdf. |
upper_trim |
Please refer to the file /inst/doc/readme.pdf. |
rounds |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., raw_matrix, cor_method = c("spearman", "pearson", "kendall"), pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06) { matrices <- list(...) numMethod <- length(matrices) method_range <- seq(1, numMethod, 1) ubq_genes <- identifyUbq(raw_matrix, pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = 100) cor_value_method <- NULL for (j in method_range) { dataUse2Cor <- matrices[[j]][ubq_genes, ] cor.result <- getCor(dataUse2Cor, method = cor_method, rounds = rounds) cor_vm <- cbind(cor.result, rep(names(matrices)[j], times = round(rounds))) cor_value_method <- rbind(cor_value_method, cor_vm) } colnames(cor_value_method) <- c("Value", "Methods") return(cor_value_method) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., raw_matrix, cor_method = c("spearman", "pearson", "kendall"), pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, rounds = 1e+06) { matrices <- list(...) numMethod <- length(matrices) method_range <- seq(1, numMethod, 1) ubq_genes <- identifyUbq(raw_matrix, pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = 100) cor_value_method <- NULL for (j in method_range) { dataUse2Cor <- matrices[[j]][ubq_genes, ] cor.result <- getCor(dataUse2Cor, method = cor_method, rounds = rounds) cor_vm <- cbind(cor.result, rep(names(matrices)[j], times = round(rounds))) cor_value_method <- rbind(cor_value_method, cor_vm) } colnames(cor_value_method) <- c("Value", "Methods") return(cor_value_method) }
Please refer to the file /inst/doc/readme.pdf.
gatherCVs(data,nonzeroRatio,HG7,ERCC,TN,TC,CR,NR, DESeq,UQ,TMM,TU,GAPDH,cvNorm,cvResolution)gatherCVs(data,nonzeroRatio,HG7,ERCC,TN,TC,CR,NR, DESeq,UQ,TMM,TU,GAPDH,cvNorm,cvResolution)
data |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
HG7 |
Please refer to the file /inst/doc/readme.pdf. |
ERCC |
Please refer to the file /inst/doc/readme.pdf. |
TN |
Please refer to the file /inst/doc/readme.pdf. |
TC |
Please refer to the file /inst/doc/readme.pdf. |
CR |
Please refer to the file /inst/doc/readme.pdf. |
NR |
Please refer to the file /inst/doc/readme.pdf. |
DESeq |
Please refer to the file /inst/doc/readme.pdf. |
UQ |
Please refer to the file /inst/doc/readme.pdf. |
TMM |
Please refer to the file /inst/doc/readme.pdf. |
TU |
Please refer to the file /inst/doc/readme.pdf. |
GAPDH |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatio!") } methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] numMethod <- length(specifiedMethods) method_range_tmp <- seq(1, numMethod, 1) cv_range_tmp <- seq(0, 1, cvResolution) method_range_times <- length(cv_range_tmp) cv_range_times <- length(method_range_tmp) method_range <- rep(method_range_tmp, each = round(method_range_times)) cv_range <- rep(cv_range_tmp, times = round(cv_range_times)) nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) for (j in method_range_tmp) { norm.matrix <- getNormMatrix(data, specifiedMethods[[j]]) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(paste(names(specifiedMethods)[j], ".cv", sep = ""), cv.result) } cv_uniform <- NULL cv_uniform_all <- mapply(function(i, j) { cv.result <- paste(names(specifiedMethods)[j], ".cv", sep = "") gene_number <- length(which(get(cv.result) <= i)) cv_uniform_row <- c(i, gene_number, names(specifiedMethods)[j]) rbind(cv_uniform, cv_uniform_row) }, cv_range, method_range) cv_uniform_all <- t(cv_uniform_all) colnames(cv_uniform_all) <- c("Cutoff", "Counts", "Methods") return(cv_uniform_all) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = NULL, GAPDH = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatio!") } methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] numMethod <- length(specifiedMethods) method_range_tmp <- seq(1, numMethod, 1) cv_range_tmp <- seq(0, 1, cvResolution) method_range_times <- length(cv_range_tmp) cv_range_times <- length(method_range_tmp) method_range <- rep(method_range_tmp, each = round(method_range_times)) cv_range <- rep(cv_range_tmp, times = round(cv_range_times)) nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) for (j in method_range_tmp) { norm.matrix <- getNormMatrix(data, specifiedMethods[[j]]) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(paste(names(specifiedMethods)[j], ".cv", sep = ""), cv.result) } cv_uniform <- NULL cv_uniform_all <- mapply(function(i, j) { cv.result <- paste(names(specifiedMethods)[j], ".cv", sep = "") gene_number <- length(which(get(cv.result) <= i)) cv_uniform_row <- c(i, gene_number, names(specifiedMethods)[j]) rbind(cv_uniform, cv_uniform_row) }, cv_range, method_range) cv_uniform_all <- t(cv_uniform_all) colnames(cv_uniform_all) <- c("Cutoff", "Counts", "Methods") return(cv_uniform_all) }
Please refer to the file /inst/doc/readme.pdf.
gatherCVs4Matrices(..., raw_matrix, nonzeroRatio , cvNorm , cvResolution = 0.005)gatherCVs4Matrices(..., raw_matrix, nonzeroRatio , cvNorm , cvResolution = 0.005)
... |
Please refer to the file /inst/doc/readme.pdf. |
raw_matrix |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., raw_matrix, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatio!") } matrices <- list(...) matrices_name <- names(matrices) numMethod <- length(matrices) method_range_tmp <- seq(1, numMethod, 1) cv_range_tmp <- seq(0, 1, cvResolution) method_range_times <- length(cv_range_tmp) cv_range_times <- length(method_range_tmp) method_range <- rep(method_range_tmp, each = round(method_range_times)) cv_range <- rep(cv_range_tmp, times = round(cv_range_times)) nozeroIndex <- filteredZero(raw_matrix, nonzeroRatio = nonzeroRatio) for (j in method_range_tmp) { dataUse2CV <- matrices[[j]][nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(paste(matrices_name[j], ".cv", sep = ""), cv.result) } cv_uniform <- NULL cv_uniform_all <- mapply(function(i, j) { cv.result <- paste(matrices_name[j], ".cv", sep = "") gene_number <- length(which(get(cv.result) <= i)) cv_uniform_row <- c(i, gene_number, matrices_name[j]) rbind(cv_uniform, cv_uniform_row) }, cv_range, method_range) cv_uniform_all <- t(cv_uniform_all) colnames(cv_uniform_all) <- c("Cutoff", "Counts", "Methods") return(cv_uniform_all) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., raw_matrix, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatio!") } matrices <- list(...) matrices_name <- names(matrices) numMethod <- length(matrices) method_range_tmp <- seq(1, numMethod, 1) cv_range_tmp <- seq(0, 1, cvResolution) method_range_times <- length(cv_range_tmp) cv_range_times <- length(method_range_tmp) method_range <- rep(method_range_tmp, each = round(method_range_times)) cv_range <- rep(cv_range_tmp, times = round(cv_range_times)) nozeroIndex <- filteredZero(raw_matrix, nonzeroRatio = nonzeroRatio) for (j in method_range_tmp) { dataUse2CV <- matrices[[j]][nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(paste(matrices_name[j], ".cv", sep = ""), cv.result) } cv_uniform <- NULL cv_uniform_all <- mapply(function(i, j) { cv.result <- paste(matrices_name[j], ".cv", sep = "") gene_number <- length(which(get(cv.result) <= i)) cv_uniform_row <- c(i, gene_number, matrices_name[j]) rbind(cv_uniform, cv_uniform_row) }, cv_range, method_range) cv_uniform_all <- t(cv_uniform_all) colnames(cv_uniform_all) <- c("Cutoff", "Counts", "Methods") return(cv_uniform_all) }
Please refer to the file /inst/doc/readme.pdf.
gatherFactors(data, methods = c("HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), HG7.size = NULL, ERCC.size = NULL, TN.size = NULL, TC.size = NULL, CR.size = NULL, NR.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)gatherFactors(data, methods = c("HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), HG7.size = NULL, ERCC.size = NULL, TN.size = NULL, TC.size = NULL, CR.size = NULL, NR.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)
data |
Please refer to the file /inst/doc/readme.pdf. |
methods |
Please refer to the file /inst/doc/readme.pdf. |
HG7.size |
Please refer to the file /inst/doc/readme.pdf. |
ERCC.size |
Please refer to the file /inst/doc/readme.pdf. |
TN.size |
Please refer to the file /inst/doc/readme.pdf. |
TC.size |
Please refer to the file /inst/doc/readme.pdf. |
CR.size |
Please refer to the file /inst/doc/readme.pdf. |
NR.size |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
lower_trim |
Please refer to the file /inst/doc/readme.pdf. |
upper_trim |
Please refer to the file /inst/doc/readme.pdf. |
min_ubq |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), HG7.size = NULL, ERCC.size = NULL, TN.size = NULL, TC.size = NULL, CR.size = NULL, NR.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { method1 <- as.list(methods) numMethod <- length(method1) method_range <- seq(1, numMethod, 1) for (i in method_range) { if (method1[[i]] == "HG7" || method1[[i]] == "ERCC" || method1[[i]] == "TN" || method1[[i]] == "TC" || method1[[i]] == "CR" || method1[[i]] == "NR") { size.name <- paste(method1[[i]], ".size", sep = "") out.name1 <- paste(method1[[i]], ".factors", sep = "") if (is.null(size.name)) { stop("Please provide", size.name, "!") } else { assign(out.name1, getFactors(data, method = "sizefactor", lib.size = get(size.name))) } } if (method1[[i]] == "DESeq" || method1[[i]] == "RLE" || method1[[i]] == "UQ" || method1[[i]] == "TMM") { out.name2 <- paste(method1[[i]], ".factors", sep = "") assign(out.name2, getFactors(data, method = method1[[i]])) } if (method1[[i]] == "TU") { TU.factors <- getFactors(data, method = "TU", pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = min_ubq) } } factors.list <- NULL for (m in methods) { m.factors <- paste(m, ".factors", sep = "") factors.list <- c(factors.list, m.factors) } factors.result <- NULL for (i in method_range) { factors.result <- cbind(factors.result, get(factors.list[i])) } colnames(factors.result) <- methods return(factors.result) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), HG7.size = NULL, ERCC.size = NULL, TN.size = NULL, TC.size = NULL, CR.size = NULL, NR.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { method1 <- as.list(methods) numMethod <- length(method1) method_range <- seq(1, numMethod, 1) for (i in method_range) { if (method1[[i]] == "HG7" || method1[[i]] == "ERCC" || method1[[i]] == "TN" || method1[[i]] == "TC" || method1[[i]] == "CR" || method1[[i]] == "NR") { size.name <- paste(method1[[i]], ".size", sep = "") out.name1 <- paste(method1[[i]], ".factors", sep = "") if (is.null(size.name)) { stop("Please provide", size.name, "!") } else { assign(out.name1, getFactors(data, method = "sizefactor", lib.size = get(size.name))) } } if (method1[[i]] == "DESeq" || method1[[i]] == "RLE" || method1[[i]] == "UQ" || method1[[i]] == "TMM") { out.name2 <- paste(method1[[i]], ".factors", sep = "") assign(out.name2, getFactors(data, method = method1[[i]])) } if (method1[[i]] == "TU") { TU.factors <- getFactors(data, method = "TU", pre_ratio = pre_ratio, lower_trim = lower_trim, upper_trim = upper_trim, min_ubq = min_ubq) } } factors.list <- NULL for (m in methods) { m.factors <- paste(m, ".factors", sep = "") factors.list <- c(factors.list, m.factors) } factors.result <- NULL for (i in method_range) { factors.result <- cbind(factors.result, get(factors.list[i])) } colnames(factors.result) <- methods return(factors.result) }
Please refer to the file /inst/doc/readme.pdf.
getArea(x, y)getArea(x, y)
x |
Please refer to the file /inst/doc/readme.pdf. |
y |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (x, y) { x <- x/max(x) y <- y/max(y) if (!(is.numeric(x) || is.complex(x)) || !(is.numeric(y) || is.complex(y))) { stop("Arguments 'x' and 'y' must be real or complex vectors.") } if (length(x) != length(y)) { stop("The length of two input vectors should be equal!") } m <- length(x) n <- 2 * m xp <- c(x, x[m:1]) yp <- c(numeric(m), y[m:1]) p1 <- sum(xp[1:(n - 1)] * yp[2:n]) + xp[n] * yp[1] p2 <- sum(xp[2:n] * yp[1:(n - 1)]) + xp[1] * yp[n] return(0.5 * (p1 - p2)) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (x, y) { x <- x/max(x) y <- y/max(y) if (!(is.numeric(x) || is.complex(x)) || !(is.numeric(y) || is.complex(y))) { stop("Arguments 'x' and 'y' must be real or complex vectors.") } if (length(x) != length(y)) { stop("The length of two input vectors should be equal!") } m <- length(x) n <- 2 * m xp <- c(x, x[m:1]) yp <- c(numeric(m), y[m:1]) p1 <- sum(xp[1:(n - 1)] * yp[2:n]) + xp[n] * yp[1] p2 <- sum(xp[2:n] * yp[1:(n - 1)]) + xp[1] * yp[n] return(0.5 * (p1 - p2)) }
Please refer to the file /inst/doc/readme.pdf.
getAUCVC(data, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)getAUCVC(data, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)
data |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) dataUse2CV <- data[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) CV2AUCVC(cv.result, cvResolution = cvResolution) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) dataUse2CV <- data[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) CV2AUCVC(cv.result, cvResolution = cvResolution) }
Please refer to the file /inst/doc/readme.pdf.
getAUCVCs(..., nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)getAUCVCs(..., nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)
... |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { matrices <- list(...) numMethod <- length(matrices) method_range <- seq(1, numMethod, 1) result <- NULL for (i in method_range) { AUCVC.result <- getAUCVC(matrices[[i]], nonzeroRatio = nonzeroRatio, cvNorm = cvNorm, cvResolution = cvResolution) result <- c(result, AUCVC.result) names(result)[i] <- names(matrices)[i] } sorted_AUCVCs <- sort(result, decreasing = TRUE) return(sorted_AUCVCs) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { matrices <- list(...) numMethod <- length(matrices) method_range <- seq(1, numMethod, 1) result <- NULL for (i in method_range) { AUCVC.result <- getAUCVC(matrices[[i]], nonzeroRatio = nonzeroRatio, cvNorm = cvNorm, cvResolution = cvResolution) result <- c(result, AUCVC.result) names(result)[i] <- names(matrices)[i] } sorted_AUCVCs <- sort(result, decreasing = TRUE) return(sorted_AUCVCs) }
Please refer to the file /inst/doc/readme.pdf.
getCor(data, method = c("spearman", "pearson", "kendall"), rounds = 1e+06)getCor(data, method = c("spearman", "pearson", "kendall"), rounds = 1e+06)
data |
Please refer to the file /inst/doc/readme.pdf. |
method |
Please refer to the file /inst/doc/readme.pdf. |
rounds |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("spearman", "pearson", "kendall"), rounds = 1e+06) { sp_result <- NULL method <- match.arg(method) for (i in 1:rounds) { rg1 <- sample(1:nrow(data), size = 1) rg2 <- sample(1:nrow(data), size = 1) while (rg1 == rg2) { rg2 <- sample(1:nrow(data), size = 1) } gene1 <- unlist(data[rg1, ]) gene2 <- unlist(data[rg2, ]) sp_value <- cor(gene1, gene2, method = method) sp_result <- c(sp_result, sp_value) } return(sp_result) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("spearman", "pearson", "kendall"), rounds = 1e+06) { sp_result <- NULL method <- match.arg(method) for (i in 1:rounds) { rg1 <- sample(1:nrow(data), size = 1) rg2 <- sample(1:nrow(data), size = 1) while (rg1 == rg2) { rg2 <- sample(1:nrow(data), size = 1) } gene1 <- unlist(data[rg1, ]) gene2 <- unlist(data[rg2, ]) sp_value <- cor(gene1, gene2, method = method) sp_result <- c(sp_result, sp_value) } return(sp_result) }
Please refer to the file /inst/doc/readme.pdf.
getCorMedians(data)getCorMedians(data)
data |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Value)) data$Value <- as.numeric(as.character(data$Value)) sorted_result <- sort(tapply(data$Value, data$Methods, median), decreasing = FALSE) return(sorted_result) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Value)) data$Value <- as.numeric(as.character(data$Value)) sorted_result <- sort(tapply(data$Value, data$Methods, median), decreasing = FALSE) return(sorted_result) }
Please refer to the file /inst/doc/readme.pdf.
getCV(data, cvNorm = TRUE)getCV(data, cvNorm = TRUE)
data |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cvNorm = TRUE) { if (!is.matrix(data)) data <- as.matrix(data) if (cvNorm) { rawCV <- apply(data, 1, function(x) { sd(log2(x[x != 0]))/mean(log2(x[x != 0])) }) (rawCV - min(rawCV))/(max(rawCV) - min(rawCV)) } else { apply(data, 1, function(x) { sd(x)/mean(x) }) } }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, cvNorm = TRUE) { if (!is.matrix(data)) data <- as.matrix(data) if (cvNorm) { rawCV <- apply(data, 1, function(x) { sd(log2(x[x != 0]))/mean(log2(x[x != 0])) }) (rawCV - min(rawCV))/(max(rawCV) - min(rawCV)) } else { apply(data, 1, function(x) { sd(x)/mean(x) }) } }
Please refer to the file /inst/doc/readme.pdf.
getFactors(data, method = c("sizefactor", "DESeq", "RLE", "UQ", "TMM", "TU"), lib.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)getFactors(data, method = c("sizefactor", "DESeq", "RLE", "UQ", "TMM", "TU"), lib.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)
data |
Please refer to the file /inst/doc/readme.pdf. |
method |
Please refer to the file /inst/doc/readme.pdf. |
lib.size |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
lower_trim |
Please refer to the file /inst/doc/readme.pdf. |
upper_trim |
Please refer to the file /inst/doc/readme.pdf. |
min_ubq |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("sizefactor", "DESeq", "RLE", "UQ", "TMM", "TU"), lib.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { if (!is.matrix(data)) data <- as.matrix(data) if (any(is.na(data))) stop("NA counts not permitted") if (is.null(lib.size)) libsize <- colSums(data) else libsize <- lib.size if (any(is.na(libsize))) stop("NA libsizes not permitted") method <- match.arg(method) i <- apply(data <= 0, 1, all) if (any(i)) data <- data[!i, , drop = FALSE] f <- switch(method, sizefactor = 1e+06/libsize, DESeq = 1/estimateSizeFactorsForMatrix(data, p = 0.5), RLE = calcFactorRLE(data, p = 0.5)/libsize, UQ = calcFactorUpperquartile(data, lib.size = libsize, p = 0.75), TMM = { fq <- calcFactorUpperquartile(data = data, lib.size = libsize, p = 0.75) refColumn <- which.min(abs(fq - mean(fq))) if (length(refColumn) == 0 | refColumn < 1 | refColumn > ncol(data)) refColumn <- 1 f <- rep(NA, ncol(data)) for (i in 1:ncol(data)) { f[i] <- calcFactorWeighted(obs = data[, i], ref = data[, refColumn], libsize.obs = libsize[i], libsize.ref = libsize[refColumn], logratioTrim = 0.3, sumTrim = 0.05, doWeighting = TRUE, Acutoff = -1e+10) } f }, TU = { if (!is.data.frame(data)) data <- data.frame(data) ubq_genes <- identifyUbq(data, lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = min_ubq) ubq_sums <- colSums(data[ubq_genes, ]) mean(ubq_sums)/ubq_sums }, ) if (method == "RLE" || method == "UQ" || method == "TMM") { f <- 1e+06/libsize/f } norm.factors <- f/exp(mean(base::log(f))) round(norm.factors, digits = 5) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("sizefactor", "DESeq", "RLE", "UQ", "TMM", "TU"), lib.size = NULL, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { if (!is.matrix(data)) data <- as.matrix(data) if (any(is.na(data))) stop("NA counts not permitted") if (is.null(lib.size)) libsize <- colSums(data) else libsize <- lib.size if (any(is.na(libsize))) stop("NA libsizes not permitted") method <- match.arg(method) i <- apply(data <= 0, 1, all) if (any(i)) data <- data[!i, , drop = FALSE] f <- switch(method, sizefactor = 1e+06/libsize, DESeq = 1/estimateSizeFactorsForMatrix(data, p = 0.5), RLE = calcFactorRLE(data, p = 0.5)/libsize, UQ = calcFactorUpperquartile(data, lib.size = libsize, p = 0.75), TMM = { fq <- calcFactorUpperquartile(data = data, lib.size = libsize, p = 0.75) refColumn <- which.min(abs(fq - mean(fq))) if (length(refColumn) == 0 | refColumn < 1 | refColumn > ncol(data)) refColumn <- 1 f <- rep(NA, ncol(data)) for (i in 1:ncol(data)) { f[i] <- calcFactorWeighted(obs = data[, i], ref = data[, refColumn], libsize.obs = libsize[i], libsize.ref = libsize[refColumn], logratioTrim = 0.3, sumTrim = 0.05, doWeighting = TRUE, Acutoff = -1e+10) } f }, TU = { if (!is.data.frame(data)) data <- data.frame(data) ubq_genes <- identifyUbq(data, lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = min_ubq) ubq_sums <- colSums(data[ubq_genes, ]) mean(ubq_sums)/ubq_sums }, ) if (method == "RLE" || method == "UQ" || method == "TMM") { f <- 1e+06/libsize/f } norm.factors <- f/exp(mean(base::log(f))) round(norm.factors, digits = 5) }
Please refer to the file /inst/doc/readme.pdf.
getNormMatrix(data, norm.factors)getNormMatrix(data, norm.factors)
data |
Please refer to the file /inst/doc/readme.pdf. |
norm.factors |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, norm.factors) { data * matrix(rep(norm.factors, dim(data)[1]), nrow = dim(data)[1], ncol = length(norm.factors), byrow = T) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, norm.factors) { data * matrix(rep(norm.factors, dim(data)[1]), nrow = dim(data)[1], ncol = length(norm.factors), byrow = T) }
Please refer to the file /inst/doc/readme.pdf.
gridAUCVC(data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatios = c(0.7, 0.8, 0.9, 1), cvNorm = TRUE, cvResolution = 0.005)gridAUCVC(data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatios = c(0.7, 0.8, 0.9, 1), cvNorm = TRUE, cvResolution = 0.005)
data |
Please refer to the file /inst/doc/readme.pdf. |
dataType |
Please refer to the file /inst/doc/readme.pdf. |
HG7 |
Please refer to the file /inst/doc/readme.pdf. |
ERCC |
Please refer to the file /inst/doc/readme.pdf. |
TN |
Please refer to the file /inst/doc/readme.pdf. |
TC |
Please refer to the file /inst/doc/readme.pdf. |
CR |
Please refer to the file /inst/doc/readme.pdf. |
NR |
Please refer to the file /inst/doc/readme.pdf. |
DESeq |
Please refer to the file /inst/doc/readme.pdf. |
UQ |
Please refer to the file /inst/doc/readme.pdf. |
TMM |
Please refer to the file /inst/doc/readme.pdf. |
TU |
Please refer to the file /inst/doc/readme.pdf. |
GAPDH |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatios |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatios = c(0.7, 0.8, 0.9, 1), cvNorm = TRUE, cvResolution = 0.005) { grid_result <- NULL if (length(TU) == 1 && TU == 1) { colnames_paraMatrix <- c("nonzeroRatio", "pre_ratio", "lower_trim", "upper_trim") write.table(t(as.matrix(colnames_paraMatrix)), file = "bestPara.txt", sep = "\t", row.names = FALSE, col.names = FALSE) } for (i in nonzeroRatios) { if (dataType == "sc") { if ((ncol(data) * i) <= 100) { cat("nonzeroRatio:", i, " is too small!\n") stop("We suggest that the minimal counts of nonzero samples should be greater than 100!") } } result <- nonzeroRatio2AUCVC(data = data, dataType = dataType, HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH, nonzeroRatio = i, cvNorm = cvNorm, cvResolution = cvResolution) nonzeroM <- matrix(i, 1, 1, TRUE) colnames(nonzeroM) <- "NonzeroRatio" grid_record <- cbind(nonzeroM, result) grid_result <- rbind(grid_result, grid_record) } return(grid_result) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatios = c(0.7, 0.8, 0.9, 1), cvNorm = TRUE, cvResolution = 0.005) { grid_result <- NULL if (length(TU) == 1 && TU == 1) { colnames_paraMatrix <- c("nonzeroRatio", "pre_ratio", "lower_trim", "upper_trim") write.table(t(as.matrix(colnames_paraMatrix)), file = "bestPara.txt", sep = "\t", row.names = FALSE, col.names = FALSE) } for (i in nonzeroRatios) { if (dataType == "sc") { if ((ncol(data) * i) <= 100) { cat("nonzeroRatio:", i, " is too small!\n") stop("We suggest that the minimal counts of nonzero samples should be greater than 100!") } } result <- nonzeroRatio2AUCVC(data = data, dataType = dataType, HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH, nonzeroRatio = i, cvNorm = cvNorm, cvResolution = cvResolution) nonzeroM <- matrix(i, 1, 1, TRUE) colnames(nonzeroM) <- "NonzeroRatio" grid_record <- cbind(nonzeroM, result) grid_result <- rbind(grid_result, grid_record) } return(grid_result) }
Please refer to the file /inst/doc/readme.pdf.
gridAUCVC4Matrices(..., nonzeroRatios = NULL, cvNorm = TRUE, cvResolution = 0.005)gridAUCVC4Matrices(..., nonzeroRatios = NULL, cvNorm = TRUE, cvResolution = 0.005)
... |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatios |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., nonzeroRatios = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatios)) { stop("Please provide nonzeroRatios!") } matrices <- list(...) numMethod <- length(matrices) grid_result <- NULL for (i in nonzeroRatios) { result.sorted <- getAUCVCs(..., nonzeroRatio = i, cvNorm = cvNorm, cvResolution = cvResolution) grid_record <- c(i, result.sorted) names(grid_record)[1] <- "NonzeroRatio" grid_result <- c(grid_result, names(grid_record), grid_record) } grid_result2 <- matrix(grid_result, ncol = numMethod + 1, byrow = TRUE) return(grid_result2) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (..., nonzeroRatios = NULL, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatios)) { stop("Please provide nonzeroRatios!") } matrices <- list(...) numMethod <- length(matrices) grid_result <- NULL for (i in nonzeroRatios) { result.sorted <- getAUCVCs(..., nonzeroRatio = i, cvNorm = cvNorm, cvResolution = cvResolution) grid_record <- c(i, result.sorted) names(grid_record)[1] <- "NonzeroRatio" grid_result <- c(grid_result, names(grid_record), grid_record) } grid_result2 <- matrix(grid_result, ncol = numMethod + 1, byrow = TRUE) return(grid_result2) }
Please refer to the file /inst/doc/readme.pdf.
identifyUbq(data, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)identifyUbq(data, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100)
data |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio |
Please refer to the file /inst/doc/readme.pdf. |
lower_trim |
Please refer to the file /inst/doc/readme.pdf. |
upper_trim |
Please refer to the file /inst/doc/readme.pdf. |
min_ubq |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { qlower <- apply(data, 2, function(x) quantile(x[x != 0], p = lower_trim)) qupper <- apply(data, 2, function(x) quantile(x[x != 0], p = upper_trim)) ubq_genes <- NULL for (i in 1:nrow(data)) { genes_finded <- findGenes(data[i, ], qlower = qlower, qupper = qupper, pre_ratio = pre_ratio) ubq_genes <- c(ubq_genes, genes_finded) } if (length(ubq_genes) < min_ubq) { cat("Parameters range", lower_trim, "-", upper_trim, "...identified too few ubiquitous genes (", length(ubq_genes), "), trying range 5-95 instead", "\n") ubq_genes <- identifyUbqRepeat(data, pre_ratioC = pre_ratio, lower_trimC = 0.05, upper_trimC = 0.95) } return(ubq_genes) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, pre_ratio = 0.5, lower_trim = 0.05, upper_trim = 0.65, min_ubq = 100) { qlower <- apply(data, 2, function(x) quantile(x[x != 0], p = lower_trim)) qupper <- apply(data, 2, function(x) quantile(x[x != 0], p = upper_trim)) ubq_genes <- NULL for (i in 1:nrow(data)) { genes_finded <- findGenes(data[i, ], qlower = qlower, qupper = qupper, pre_ratio = pre_ratio) ubq_genes <- c(ubq_genes, genes_finded) } if (length(ubq_genes) < min_ubq) { cat("Parameters range", lower_trim, "-", upper_trim, "...identified too few ubiquitous genes (", length(ubq_genes), "), trying range 5-95 instead", "\n") ubq_genes <- identifyUbqRepeat(data, pre_ratioC = pre_ratio, lower_trimC = 0.05, upper_trimC = 0.95) } return(ubq_genes) }
Please refer to the file /inst/doc/readme.pdf.
identifyUbqRepeat(data, pre_ratioC = NULL, lower_trimC = NULL, upper_trimC = NULL)identifyUbqRepeat(data, pre_ratioC = NULL, lower_trimC = NULL, upper_trimC = NULL)
data |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratioC |
Please refer to the file /inst/doc/readme.pdf. |
lower_trimC |
Please refer to the file /inst/doc/readme.pdf. |
upper_trimC |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, pre_ratioC = NULL, lower_trimC = NULL, upper_trimC = NULL) { qlower <- apply(data, 2, function(x) quantile(x[x != 0], p = lower_trimC)) qupper <- apply(data, 2, function(x) quantile(x[x != 0], p = upper_trimC)) ubq_genes <- NULL for (i in 1:nrow(data)) { genes_finded <- findGenes(data[i, ], qlower = qlower, qupper = qupper, pre_ratio = pre_ratioC) ubq_genes <- c(ubq_genes, genes_finded) } return(ubq_genes) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, pre_ratioC = NULL, lower_trimC = NULL, upper_trimC = NULL) { qlower <- apply(data, 2, function(x) quantile(x[x != 0], p = lower_trimC)) qupper <- apply(data, 2, function(x) quantile(x[x != 0], p = upper_trimC)) ubq_genes <- NULL for (i in 1:nrow(data)) { genes_finded <- findGenes(data[i, ], qlower = qlower, qupper = qupper, pre_ratio = pre_ratioC) ubq_genes <- c(ubq_genes, genes_finded) } return(ubq_genes) }
Please refer to the file /inst/doc/readme.pdf.
nonzeroRatio2AUCVC(data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)nonzeroRatio2AUCVC(data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005)
data |
Please refer to the file /inst/doc/readme.pdf. |
dataType |
Please refer to the file /inst/doc/readme.pdf. |
HG7 |
Please refer to the file /inst/doc/readme.pdf. |
ERCC |
Please refer to the file /inst/doc/readme.pdf. |
TN |
Please refer to the file /inst/doc/readme.pdf. |
TC |
Please refer to the file /inst/doc/readme.pdf. |
CR |
Please refer to the file /inst/doc/readme.pdf. |
NR |
Please refer to the file /inst/doc/readme.pdf. |
DESeq |
Please refer to the file /inst/doc/readme.pdf. |
UQ |
Please refer to the file /inst/doc/readme.pdf. |
TMM |
Please refer to the file /inst/doc/readme.pdf. |
TU |
Please refer to the file /inst/doc/readme.pdf. |
GAPDH |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] if (length(TU) == 1 && TU == 0) { specifiedMethods$TU <- NULL } if (length(TU) == 1 && TU == 1) { if (dataType == "bk") { optimalPara <- optTU(data, nonzeroRatio = nonzeroRatio, pre_ratio_range = c(1, 1), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 1000, cvNorm = cvNorm, cvResolution = cvResolution) } else { optimalPara <- optTU(data, nonzeroRatio = nonzeroRatio, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = cvNorm, cvResolution = cvResolution) } optimalPara <- as.matrix(optimalPara) lower_trim <- optimalPara["lower", 1] upper_trim <- optimalPara["upper", 1] pre_ratio <- optimalPara["ratio", 1] para <- c(nonzeroRatio, pre_ratio, lower_trim, upper_trim) names(para)[1] <- "nonzeroRatio" paraMatrix <- t(as.matrix(para)) write.table(paraMatrix, file = "bestPara.txt", sep = "\t", row.names = FALSE, col.names = FALSE, append = TRUE) TU.factors <- getFactors(data, method = "TU", lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = 100) norm.matrix <- getNormMatrix(data, TU.factors) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) TU.AUCVC <- CV2AUCVC(cv.result, cvResolution = cvResolution) specifiedMethods$TU <- NULL } numMethod <- length(specifiedMethods) if (numMethod >= 1) { method_range <- seq(1, numMethod, 1) for (i in method_range) { norm.matrix <- getNormMatrix(data, specifiedMethods[[i]]) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(names(specifiedMethods)[i], CV2AUCVC(cv.result, cvResolution = cvResolution)) } AUCVC.result <- NULL for (i in method_range) { AUCVC.result <- cbind(AUCVC.result, get(names(specifiedMethods)[i])) } colnames(AUCVC.result) <- names(specifiedMethods) if (length(TU) == 1 && TU == 1) { AUCVC.result <- cbind(AUCVC.result, TU.AUCVC) colnames(AUCVC.result) <- c(names(specifiedMethods), "TU") } } if (numMethod == 0 && TU == 0) stop("Please specify at least one method!") if (numMethod == 0 && TU == 1) { AUCVC.result <- as.matrix(TU.AUCVC) colnames(AUCVC.result) <- "TU" } return(AUCVC.result) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, dataType = c("bk", "sc"), HG7 = NULL, ERCC = NULL, TN = NULL, TC = NULL, CR = NULL, NR = NULL, DESeq = NULL, UQ = NULL, TMM = NULL, TU = 0, GAPDH = NULL, nonzeroRatio = NULL, cvNorm = TRUE, cvResolution = 0.005) { nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) methodsList <- list(HG7 = HG7, ERCC = ERCC, TN = TN, TC = TC, CR = CR, NR = NR, DESeq = DESeq, UQ = UQ, TMM = TMM, TU = TU, GAPDH = GAPDH) specifiedMethods <- methodsList[!unlist(lapply(methodsList, is.null))] if (length(TU) == 1 && TU == 0) { specifiedMethods$TU <- NULL } if (length(TU) == 1 && TU == 1) { if (dataType == "bk") { optimalPara <- optTU(data, nonzeroRatio = nonzeroRatio, pre_ratio_range = c(1, 1), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 1000, cvNorm = cvNorm, cvResolution = cvResolution) } else { optimalPara <- optTU(data, nonzeroRatio = nonzeroRatio, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = cvNorm, cvResolution = cvResolution) } optimalPara <- as.matrix(optimalPara) lower_trim <- optimalPara["lower", 1] upper_trim <- optimalPara["upper", 1] pre_ratio <- optimalPara["ratio", 1] para <- c(nonzeroRatio, pre_ratio, lower_trim, upper_trim) names(para)[1] <- "nonzeroRatio" paraMatrix <- t(as.matrix(para)) write.table(paraMatrix, file = "bestPara.txt", sep = "\t", row.names = FALSE, col.names = FALSE, append = TRUE) TU.factors <- getFactors(data, method = "TU", lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = 100) norm.matrix <- getNormMatrix(data, TU.factors) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) TU.AUCVC <- CV2AUCVC(cv.result, cvResolution = cvResolution) specifiedMethods$TU <- NULL } numMethod <- length(specifiedMethods) if (numMethod >= 1) { method_range <- seq(1, numMethod, 1) for (i in method_range) { norm.matrix <- getNormMatrix(data, specifiedMethods[[i]]) dataUse2CV <- norm.matrix[nozeroIndex, ] cv.result <- getCV(dataUse2CV, cvNorm = cvNorm) assign(names(specifiedMethods)[i], CV2AUCVC(cv.result, cvResolution = cvResolution)) } AUCVC.result <- NULL for (i in method_range) { AUCVC.result <- cbind(AUCVC.result, get(names(specifiedMethods)[i])) } colnames(AUCVC.result) <- names(specifiedMethods) if (length(TU) == 1 && TU == 1) { AUCVC.result <- cbind(AUCVC.result, TU.AUCVC) colnames(AUCVC.result) <- c(names(specifiedMethods), "TU") } } if (numMethod == 0 && TU == 0) stop("Please specify at least one method!") if (numMethod == 0 && TU == 1) { AUCVC.result <- as.matrix(TU.AUCVC) colnames(AUCVC.result) <- "TU" } return(AUCVC.result) }
Please refer to the file /inst/doc/readme.pdf.
optTU(data, nonzeroRatio = NULL, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = TRUE, cvResolution = 0.005)optTU(data, nonzeroRatio = NULL, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = TRUE, cvResolution = 0.005)
data |
Please refer to the file /inst/doc/readme.pdf. |
nonzeroRatio |
Please refer to the file /inst/doc/readme.pdf. |
pre_ratio_range |
Please refer to the file /inst/doc/readme.pdf. |
prResolution |
Please refer to the file /inst/doc/readme.pdf. |
lower_range |
Please refer to the file /inst/doc/readme.pdf. |
upper_range |
Please refer to the file /inst/doc/readme.pdf. |
qResolution |
Please refer to the file /inst/doc/readme.pdf. |
min_ubq |
Please refer to the file /inst/doc/readme.pdf. |
cvNorm |
Please refer to the file /inst/doc/readme.pdf. |
cvResolution |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatios!") } pre_ratio_times <- (pre_ratio_range[2] - pre_ratio_range[1] + prResolution) * 10 lower_times <- (upper_range[2] - upper_range[1] + qResolution)/qResolution lower_range_tmp <- rep(seq(lower_range[1], lower_range[2], qResolution), each = round(lower_times)) lower_range2 <- rep(lower_range_tmp, times = round(pre_ratio_times)) upper_times <- (lower_range[2] - lower_range[1] + qResolution)/qResolution upper_range_tmp <- rep(seq(upper_range[1], upper_range[2], qResolution), times = round(upper_times)) upper_range2 <- rep(upper_range_tmp, times = round(pre_ratio_times)) lower_upper_tmp_len <- length(lower_range_tmp) pre_ratio_range2 <- rep(seq(pre_ratio_range[1], pre_ratio_range[2], 0.1), each = round(lower_upper_tmp_len)) nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) all_aucvc <- mapply(function(lower_trim, upper_trim, pre_ratio) { factors.TU <- getFactors(data, method = "TU", lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = min_ubq) norm.TU <- getNormMatrix(data, factors.TU) dataUse2CV <- norm.TU[nozeroIndex, ] cv.TU <- getCV(dataUse2CV, cvNorm = cvNorm) TU.AUCVC <- CV2AUCVC(cv.TU, cvResolution = cvResolution) return(c(TU.AUCVC = TU.AUCVC, lower = lower_trim, upper = upper_trim, ratio = pre_ratio)) }, lower_range2, upper_range2, pre_ratio_range2) all_aucvc2 <- t(all_aucvc) max_index <- which(max(all_aucvc2[, "TU.AUCVC"]) == all_aucvc2[, "TU.AUCVC"]) return(all_aucvc2[max_index, ]) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, nonzeroRatio = NULL, pre_ratio_range = c(0.2, 0.6), prResolution = 0.1, lower_range = c(0.05, 0.4), upper_range = c(0.6, 0.95), qResolution = 0.05, min_ubq = 100, cvNorm = TRUE, cvResolution = 0.005) { if (is.null(nonzeroRatio)) { stop("Please provide nonzeroRatios!") } pre_ratio_times <- (pre_ratio_range[2] - pre_ratio_range[1] + prResolution) * 10 lower_times <- (upper_range[2] - upper_range[1] + qResolution)/qResolution lower_range_tmp <- rep(seq(lower_range[1], lower_range[2], qResolution), each = round(lower_times)) lower_range2 <- rep(lower_range_tmp, times = round(pre_ratio_times)) upper_times <- (lower_range[2] - lower_range[1] + qResolution)/qResolution upper_range_tmp <- rep(seq(upper_range[1], upper_range[2], qResolution), times = round(upper_times)) upper_range2 <- rep(upper_range_tmp, times = round(pre_ratio_times)) lower_upper_tmp_len <- length(lower_range_tmp) pre_ratio_range2 <- rep(seq(pre_ratio_range[1], pre_ratio_range[2], 0.1), each = round(lower_upper_tmp_len)) nozeroIndex <- filteredZero(data, nonzeroRatio = nonzeroRatio) all_aucvc <- mapply(function(lower_trim, upper_trim, pre_ratio) { factors.TU <- getFactors(data, method = "TU", lower_trim = lower_trim, upper_trim = upper_trim, pre_ratio = pre_ratio, min_ubq = min_ubq) norm.TU <- getNormMatrix(data, factors.TU) dataUse2CV <- norm.TU[nozeroIndex, ] cv.TU <- getCV(dataUse2CV, cvNorm = cvNorm) TU.AUCVC <- CV2AUCVC(cv.TU, cvResolution = cvResolution) return(c(TU.AUCVC = TU.AUCVC, lower = lower_trim, upper = upper_trim, ratio = pre_ratio)) }, lower_range2, upper_range2, pre_ratio_range2) all_aucvc2 <- t(all_aucvc) max_index <- which(max(all_aucvc2[, "TU.AUCVC"]) == all_aucvc2[, "TU.AUCVC"]) return(all_aucvc2[max_index, ]) }
Please refer to the file /inst/doc/readme.pdf.
plotCors(data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.15, 0.56))plotCors(data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.15, 0.56))
data |
Please refer to the file /inst/doc/readme.pdf. |
methods |
Please refer to the file /inst/doc/readme.pdf. |
legend.position |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.15, 0.56)) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Value)) data$Value <- as.numeric(as.character(data$Value)) data$Methods <- factor(data$Methods, levels = methods, labels = methods) change_colours(ggplot(data = data, aes(x = Value, y = ..count../sum(..count..))) + geom_freqpoly(aes(group = Methods, color = Methods), size = 3, bins = 50) + xlab("Spearman correlation") + ylab("Fraction of gene pairs") + theme_bw() + theme(panel.grid.minor = element_blank(), axis.title.x = element_text(size = 48), axis.title.y = element_text(size = 48), axis.text.x = element_text(size = 38), axis.text.y = element_text(size = 38), legend.text = element_text(size = 39), legend.title = element_text(size = 43), legend.position = legend.position, legend.background = element_blank(), legend.key = element_blank(), legend.key.height = unit(1.8, "cm"), plot.margin = unit(c(0.5, 1, 0.5, 0.5), "cm")) + scale_x_continuous(expand = c(0.01, 0.01), breaks = round(seq(-1, 1, 0.25), 2)) + scale_y_continuous(expand = c(0.01, 0)) + guides(color = guide_legend(title = NULL)), c("olivedrab", "blue", "red", "violet", "orange", "yellow", "magenta", "peru", "black", "maroon", "lightblue", "darkslateblue", "seashell4", "tan2", "darkgreen", "springgreen")) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.15, 0.56)) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Value)) data$Value <- as.numeric(as.character(data$Value)) data$Methods <- factor(data$Methods, levels = methods, labels = methods) change_colours(ggplot(data = data, aes(x = Value, y = ..count../sum(..count..))) + geom_freqpoly(aes(group = Methods, color = Methods), size = 3, bins = 50) + xlab("Spearman correlation") + ylab("Fraction of gene pairs") + theme_bw() + theme(panel.grid.minor = element_blank(), axis.title.x = element_text(size = 48), axis.title.y = element_text(size = 48), axis.text.x = element_text(size = 38), axis.text.y = element_text(size = 38), legend.text = element_text(size = 39), legend.title = element_text(size = 43), legend.position = legend.position, legend.background = element_blank(), legend.key = element_blank(), legend.key.height = unit(1.8, "cm"), plot.margin = unit(c(0.5, 1, 0.5, 0.5), "cm")) + scale_x_continuous(expand = c(0.01, 0.01), breaks = round(seq(-1, 1, 0.25), 2)) + scale_y_continuous(expand = c(0.01, 0)) + guides(color = guide_legend(title = NULL)), c("olivedrab", "blue", "red", "violet", "orange", "yellow", "magenta", "peru", "black", "maroon", "lightblue", "darkslateblue", "seashell4", "tan2", "darkgreen", "springgreen")) }
Please refer to the file /inst/doc/readme.pdf.
plotCVs(data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.85, 0.48))plotCVs(data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.85, 0.48))
data |
Please refer to the file /inst/doc/readme.pdf. |
methods |
Please refer to the file /inst/doc/readme.pdf. |
legend.position |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.85, 0.48)) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Cutoff)) data$Cutoff <- as.numeric(as.character(data$Cutoff)) if (is.factor(data$Counts)) data$Counts <- as.numeric(as.character(data$Counts)) data$Methods <- factor(data$Methods, levels = methods, labels = methods) change_colours(ggplot(data = data, aes(x = Cutoff, y = Counts)) + geom_line(aes(group = Methods, color = Methods), size = 3) + xlab("Normalized CV cutoff") + ylab("Number of uniform genes") + theme_bw() + theme(panel.grid.minor = element_blank(), axis.title.x = element_text(size = 48), axis.title.y = element_text(size = 48), axis.text.x = element_text(size = 38), axis.text.y = element_text(size = 38), legend.text = element_text(size = 39), legend.title = element_text(size = 43), legend.position = legend.position, legend.background = element_blank(), legend.key = element_blank(), legend.key.height = unit(1.8, "cm"), plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm")) + scale_x_continuous(breaks = seq(0, 1, 0.2)) + scale_y_continuous() + guides(color = guide_legend(title = NULL)), c("olivedrab", "blue", "red", "violet", "orange", "yellow", "magenta", "peru", "black", "maroon", "lightblue", "darkslateblue", "seashell4", "tan2", "darkgreen", "springgreen")) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, methods = c("None", "HG7", "ERCC", "TN", "TC", "CR", "NR", "DESeq", "UQ", "TMM", "TU"), legend.position = c(0.85, 0.48)) { if (!is.data.frame(data)) data <- data.frame(data) if (is.factor(data$Cutoff)) data$Cutoff <- as.numeric(as.character(data$Cutoff)) if (is.factor(data$Counts)) data$Counts <- as.numeric(as.character(data$Counts)) data$Methods <- factor(data$Methods, levels = methods, labels = methods) change_colours(ggplot(data = data, aes(x = Cutoff, y = Counts)) + geom_line(aes(group = Methods, color = Methods), size = 3) + xlab("Normalized CV cutoff") + ylab("Number of uniform genes") + theme_bw() + theme(panel.grid.minor = element_blank(), axis.title.x = element_text(size = 48), axis.title.y = element_text(size = 48), axis.text.x = element_text(size = 38), axis.text.y = element_text(size = 38), legend.text = element_text(size = 39), legend.title = element_text(size = 43), legend.position = legend.position, legend.background = element_blank(), legend.key = element_blank(), legend.key.height = unit(1.8, "cm"), plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm")) + scale_x_continuous(breaks = seq(0, 1, 0.2)) + scale_y_continuous() + guides(color = guide_legend(title = NULL)), c("olivedrab", "blue", "red", "violet", "orange", "yellow", "magenta", "peru", "black", "maroon", "lightblue", "darkslateblue", "seashell4", "tan2", "darkgreen", "springgreen")) }
Please refer to the file /inst/doc/readme.pdf.
plotHC(data, method = c("spearman", "pearson", "kendall"), mar = c(9, 1, 0, 20))plotHC(data, method = c("spearman", "pearson", "kendall"), mar = c(9, 1, 0, 20))
data |
Please refer to the file /inst/doc/readme.pdf. |
method |
Please refer to the file /inst/doc/readme.pdf. |
mar |
Please refer to the file /inst/doc/readme.pdf. |
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("spearman", "pearson", "kendall"), mar = c(9, 1, 0, 20)) { if (!is.data.frame(data)) data <- data.frame(data) method <- match.arg(method) hc <- hclust(as.dist(1 - cor(data, method = method))) dend <- as.dendrogram(hc) dend <- dend %>% set("labels_cex", 6.5) %>% set("branches_lwd", 6.5) par(mar = mar, mgp = c(10, 5, 0), cex.axis = 6) plot(dend, horiz = TRUE) axis(side = 1, lwd = 8) }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (data, method = c("spearman", "pearson", "kendall"), mar = c(9, 1, 0, 20)) { if (!is.data.frame(data)) data <- data.frame(data) method <- match.arg(method) hc <- hclust(as.dist(1 - cor(data, method = method))) dend <- as.dendrogram(hc) dend <- dend %>% set("labels_cex", 6.5) %>% set("branches_lwd", 6.5) par(mar = mar, mgp = c(10, 5, 0), cex.axis = 6) plot(dend, horiz = TRUE) axis(side = 1, lwd = 8) }
Please refer to the file /inst/doc/readme.pdf.
data("scRNA663")data("scRNA663")
A data frame with 57955 observations on the following 663 variables.
col36l_1a numeric vector
col36l_2a numeric vector
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col44l1_30a numeric vector
col44l1_32a numeric vector
col44l1_33a numeric vector
col44l1_36a numeric vector
col44l1_38a numeric vector
col44l1_40a numeric vector
col44l1_41a numeric vector
col44l1_42a numeric vector
col44l1_43a numeric vector
col44l1_47a numeric vector
col44l1_48a numeric vector
col44l1_50a numeric vector
col44l1_53a numeric vector
col44l1_58a numeric vector
col44l1_59a numeric vector
col44l1_60a numeric vector
col44l1_64a numeric vector
col44l1_66a numeric vector
col44l1_67a numeric vector
col44l1_68a numeric vector
col44l1_69a numeric vector
col44l1_70a numeric vector
col44l1_71a numeric vector
col44l1_73a numeric vector
col44l1_74a numeric vector
col44l1_75a numeric vector
col44l1_76a numeric vector
col44l1_77a numeric vector
col44l1_79a numeric vector
col44l1_80a numeric vector
col44l1_82a numeric vector
col44l1_83a numeric vector
col44l1_84a numeric vector
col44l1_85a numeric vector
col44l1_86a numeric vector
col44l1_87a numeric vector
col44l1_89a numeric vector
col44l1_90a numeric vector
col44l1_91a numeric vector
col44l1_92a numeric vector
col44l1_93a numeric vector
col44l1_94a numeric vector
col44l2_1a numeric vector
col44l2_2a numeric vector
col44l2_3a numeric vector
col44l2_4a numeric vector
col44l2_5a numeric vector
col44l2_6a numeric vector
col44l2_9a numeric vector
col44l2_10a numeric vector
col44l2_12a numeric vector
col44l2_14a numeric vector
col44l2_17a numeric vector
col44l2_18a numeric vector
col44l2_19a numeric vector
col44l2_20a numeric vector
col44l2_23a numeric vector
col44l2_26a numeric vector
col44l2_27a numeric vector
col44l2_28a numeric vector
col44l2_30a numeric vector
col44l2_31a numeric vector
col44l2_32a numeric vector
col44l2_35a numeric vector
col44l2_36a numeric vector
col44l2_37a numeric vector
col44l2_38a numeric vector
col44l2_40a numeric vector
col44l7_67a numeric vector
col44l7_68a numeric vector
col44l7_69a numeric vector
col44l7_70a numeric vector
col44l7_71a numeric vector
col44l7_75a numeric vector
col44l7_76a numeric vector
col44l7_77a numeric vector
col44l7_78a numeric vector
col44l7_79a numeric vector
col44l7_80a numeric vector
col44l7_82a numeric vector
col44l7_83a numeric vector
col44l7_84a numeric vector
col44l7_85a numeric vector
col44l7_86a numeric vector
col44l7_87a numeric vector
col44l7_88a numeric vector
col44l7_89a numeric vector
col44l7_90a numeric vector
col44l7_91a numeric vector
col44l7_92a numeric vector
col44l7_93a numeric vector
col44l7_94a numeric vector
col44l7_95a numeric vector
col44l7_96a numeric vector
col44l8_17a numeric vector
col44l8_19a numeric vector
col44l8_21a numeric vector
col44l8_22a numeric vector
col44l8_23a numeric vector
col44l8_24a numeric vector
col44l8_25a numeric vector
col44l8_26a numeric vector
col44l8_28a numeric vector
col44l8_29a numeric vector
col44l8_31a numeric vector
col44l8_32a numeric vector
col44l8_33a numeric vector
col44l8_34a numeric vector
col44l8_43a numeric vector
col44l8_45a numeric vector
col44l8_46a numeric vector
col44l8_47a numeric vector
col44l8_48a numeric vector
col44l8_50a numeric vector
col44l8_51a numeric vector
col44l8_52a numeric vector
col44l8_53a numeric vector
col44l8_54a numeric vector
col44l8_55a numeric vector
col44l8_56a numeric vector
col44l8_58a numeric vector
col44l8_59a numeric vector
col44l8_60a numeric vector
col44l8_61a numeric vector
col44l8_62a numeric vector
col44l8_63a numeric vector
col44l8_67a numeric vector
col44l8_68a numeric vector
col44l8_70a numeric vector
col44l8_71a numeric vector
col44l8_72a numeric vector
col44l8_73a numeric vector
col44l8_74a numeric vector
col44l8_76a numeric vector
col44l8_78a numeric vector
col44l8_81a numeric vector
col44l8_85a numeric vector
col44l8_86a numeric vector
col44l8_88a numeric vector
col44l8_94a numeric vector
col45l2_42a numeric vector
col45l2_43a numeric vector
col45l2_45a numeric vector
col45l2_47a numeric vector
col45l2_48a numeric vector
col45l2_50a numeric vector
col45l2_51a numeric vector
col45l2_52a numeric vector
col45l2_54a numeric vector
col45l2_55a numeric vector
col45l2_56a numeric vector
col45l2_57a numeric vector
col45l2_58a numeric vector
col45l2_59a numeric vector
col45l2_60a numeric vector
col45l2_61a numeric vector
col45l2_62a numeric vector
col45l2_63a numeric vector
col45l2_64a numeric vector
col45l2_65a numeric vector
col45l2_66a numeric vector
col45l2_67a numeric vector
col45l2_68a numeric vector
col45l2_69a numeric vector
col45l2_70a numeric vector
col45l2_71a numeric vector
col45l2_76a numeric vector
col45l2_77a numeric vector
col45l2_78a numeric vector
col45l2_81a numeric vector
col45l2_83a numeric vector
col45l2_84a numeric vector
col45l2_85a numeric vector
col45l2_88a numeric vector
col45l2_89a numeric vector
col45l2_90a numeric vector
col45l2_91a numeric vector
col45l2_92a numeric vector
col45l2_93a numeric vector
col45l2_94a numeric vector
col45l2_95a numeric vector
col45l7_24a numeric vector
col45l7_25a numeric vector
col45l8_2a numeric vector
col45l8_3a numeric vector
col45l8_4a numeric vector
col45l8_5a numeric vector
col45l8_6a numeric vector
col45l8_7a numeric vector
col45l8_8a numeric vector
col45l8_9a numeric vector
col45l8_11a numeric vector
col45l8_12a numeric vector
col45l8_13a numeric vector
col45l8_14a numeric vector
col45l8_35a numeric vector
col47l7_26a numeric vector
col47l7_27a numeric vector
col47l7_28a numeric vector
col47l7_29a numeric vector
col47l7_30a numeric vector
col47l7_31a numeric vector
col47l7_32a numeric vector
col47l7_33a numeric vector
col47l7_34a numeric vector
col47l7_35a numeric vector
col47l7_36a numeric vector
col47l7_37a numeric vector
col47l7_38a numeric vector
col47l7_41a numeric vector
col47l7_42a numeric vector
col47l7_44a numeric vector
col47l7_45a numeric vector
col47l7_47a numeric vector
col47l7_48a numeric vector
col47l7_49a numeric vector
col47l7_50a numeric vector
col47l7_51a numeric vector
col47l7_54a numeric vector
col47l7_57a numeric vector
col47l7_58a numeric vector
col47l7_59a numeric vector
col47l7_60a numeric vector
col47l7_63a numeric vector
col47l7_64a numeric vector
col47l7_65a numeric vector
col47l7_66a numeric vector
col48l6_2a numeric vector
col48l6_4a numeric vector
col48l6_5a numeric vector
col48l6_6a numeric vector
col48l6_7a numeric vector
col48l6_8a numeric vector
col48l6_9a numeric vector
col48l6_10a numeric vector
col48l6_11a numeric vector
col48l6_12a numeric vector
col48l6_13a numeric vector
col48l6_14a numeric vector
col48l6_16a numeric vector
col48l6_17a numeric vector
col48l6_18a numeric vector
col48l6_19a numeric vector
col48l6_20a numeric vector
col48l6_21a numeric vector
col48l6_22a numeric vector
col48l6_24a numeric vector
col48l6_26a numeric vector
col48l6_27a numeric vector
col48l6_28a numeric vector
col48l6_30a numeric vector
col48l6_31a numeric vector
col48l6_34a numeric vector
col48l6_35a numeric vector
col48l6_36a numeric vector
col48l6_39a numeric vector
col48l6_40a numeric vector
col48l6_42a numeric vector
col48l6_43a numeric vector
col48l6_44a numeric vector
col48l6_46a numeric vector
col48l6_47a numeric vector
col48l6_48a numeric vector
col48l6_50a numeric vector
col48l6_51a numeric vector
col48l6_52a numeric vector
col48l6_53a numeric vector
col48l6_54a numeric vector
col48l6_55a numeric vector
col48l6_56a numeric vector
col48l6_58a numeric vector
col48l6_60a numeric vector
col48l6_61a numeric vector
col48l6_62a numeric vector
col48l6_63a numeric vector
col48l6_64a numeric vector
col48l6_65a numeric vector
col48l6_66a numeric vector
col48l6_67a numeric vector
col48l6_68a numeric vector
col48l6_69a numeric vector
col48l6_70a numeric vector
col48l6_71a numeric vector
col48l6_72a numeric vector
col48l6_73a numeric vector
col48l6_74a numeric vector
col48l6_75a numeric vector
col48l6_76a numeric vector
col48l6_77a numeric vector
col48l6_78a numeric vector
col48l6_79a numeric vector
col48l6_80a numeric vector
col48l6_81a numeric vector
col48l6_82a numeric vector
col48l6_85a numeric vector
col48l6_86a numeric vector
col48l6_88a numeric vector
col48l6_90a numeric vector
col48l6_92a numeric vector
col48l6_93a numeric vector
col48l6_94a numeric vector
col48l6_95a numeric vector
col48l6_96a numeric vector
col48l7_1a numeric vector
col48l7_2a numeric vector
col48l7_3a numeric vector
col48l7_4a numeric vector
col48l7_5a numeric vector
col48l7_7a numeric vector
col48l7_9a numeric vector
col48l7_11a numeric vector
col48l7_12a numeric vector
col48l7_13a numeric vector
col48l7_15a numeric vector
col48l7_16a numeric vector
col48l7_18a numeric vector
col48l7_20a numeric vector
col48l7_21a numeric vector
data(scRNA663) ## maybe str(scRNA663) ; plot(scRNA663) ...data(scRNA663) ## maybe str(scRNA663) ; plot(scRNA663) ...
Please refer to the file /inst/doc/readme.pdf.
data("scRNA663_factors")data("scRNA663_factors")
A data frame with 663 observations on the following 12 variables.
HG7a numeric vector
ERCCa numeric vector
TNa numeric vector
TCa numeric vector
CRa numeric vector
NRa numeric vector
DESeqa numeric vector
UQa numeric vector
TMMa numeric vector
TUa numeric vector
NCSa numeric vector
ESa numeric vector
data(scRNA663_factors) ## maybe str(scRNA663_factors) ; plot(scRNA663_factors) ...data(scRNA663_factors) ## maybe str(scRNA663_factors) ; plot(scRNA663_factors) ...