From 3b17571b6530ea1fa4870e38ae7566030df3e6d0 Mon Sep 17 00:00:00 2001 From: soroorh <soroorh@student.unimelb.edu.au> Date: Wed, 12 Aug 2020 14:47:26 +1000 Subject: [PATCH] release v1.3 --- DESCRIPTION | 2 +- NAMESPACE | 3 ++ R/CPD.R | 4 +- R/KNC.R | 4 +- R/KNN.R | 4 +- R/computeStructuralMetrics.R | 83 +++++++++++++++++++++++--------- R/findVariableFeatures.R | 25 ++++++++++ R/msImpute.R | 18 ++++--- R/scaleData.R | 16 +++--- R/selectFeatures.R | 15 ++++-- README.md | 14 +++++- man/CPD.Rd | 4 +- man/KNC.Rd | 4 +- man/KNN.Rd | 4 +- man/computeStructuralMetrics.Rd | 43 +++++++++++------ man/findVariableFeatures.Rd | 24 +++++++++ man/msImpute.Rd | 13 +++-- man/scaleData.Rd | 14 ++++-- man/selectFeatures.Rd | 13 +++-- msImpute_1.2.0.pdf | Bin 89001 -> 0 bytes msImpute_1.3.0.pdf | Bin 0 -> 93550 bytes 21 files changed, 222 insertions(+), 85 deletions(-) create mode 100644 R/findVariableFeatures.R create mode 100644 man/findVariableFeatures.Rd delete mode 100644 msImpute_1.2.0.pdf create mode 100644 msImpute_1.3.0.pdf diff --git a/DESCRIPTION b/DESCRIPTION index 09c20d9..b9bdfa3 100755 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: msImpute Type: Package Title: Peptide imputation in label-free proteomics -Version: 1.2.0 +Version: 1.3.0 Authors@R: person(given = "Soroor", family = "Hediyeh-zadeh", diff --git a/NAMESPACE b/NAMESPACE index 7b10eaa..890a15b 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,8 +5,11 @@ export(KNC) export(KNN) export(betweenness) export(computeStructuralMetrics) +export(findVariableFeatures) export(gromov_wasserstein) export(msImpute) export(scaleData) export(selectFeatures) export(withinness) +importFrom(scran,decomposeVar) +importFrom(scran,trendVar) diff --git a/R/CPD.R b/R/CPD.R index a1d9b89..5acc939 100755 --- a/R/CPD.R +++ b/R/CPD.R @@ -4,8 +4,8 @@ #' CPD quantifies preservation of the global structure after imputation. #' Requires complete datasets - for developers/use in benchmark studies only. #' -#' @param xorigin numeric matrix. The original data. Can not contain missing values. -#' @param ximputed numeric matrix. The imputed data. Can not contain missing values. +#' @param xorigin numeric matrix. The original log-intensity data. Can not contain missing values. +#' @param ximputed numeric matrix. The imputed log-intensity data. Can not contain missing values. #' #' @return numeric #' diff --git a/R/KNC.R b/R/KNC.R index fd2003c..85e9d26 100755 --- a/R/KNC.R +++ b/R/KNC.R @@ -4,8 +4,8 @@ #' quantifies preservation of the mesoscopic structure after imputation. #' Requires complete datasets - for developers/use in benchmark studies only. #' -#' @param xorigin numeric matrix. The original data. Can contain missing values. -#' @param ximputed numeric matrix. The imputed data. +#' @param xorigin numeric matrix. The original log-intensity data. Can contain missing values. +#' @param ximputed numeric matrix. The imputed log-intensity data. #' @param class factor. A vector of length number of columns (samples) in the data specifying the class/label (i.e. experimental group) of each sample. #' @param k number of nearest class means. default to k=3. #' diff --git a/R/KNN.R b/R/KNN.R index 64b1e5c..15a59b6 100755 --- a/R/KNN.R +++ b/R/KNN.R @@ -4,8 +4,8 @@ #' KNN quantifies preservation of the local, or microscopic structure. #' Requires complete datasets - for developers/use in benchmark studies only. #' -#' @param xorigin numeric matrix. The original data. Can not contain missing values. -#' @param ximputed numeric matrix. The imputed data. Can not contain missing values. +#' @param xorigin numeric matrix. The original log-intensity data. Can not contain missing values. +#' @param ximputed numeric matrix. The imputed log-intensity data. Can not contain missing values. #' @param k number of nearest neighbours. default to k=3. #' #' @return numeric The proportion of preserved k-nearest neighbours in imputed data. diff --git a/R/computeStructuralMetrics.R b/R/computeStructuralMetrics.R index b27d0c7..2d3d6e1 100644 --- a/R/computeStructuralMetrics.R +++ b/R/computeStructuralMetrics.R @@ -1,12 +1,13 @@ #' Metrics for the assessment of post-imputation structural preservation #' #' For an imputed dataset, it computes within phenotype/experimental condition similarity (i.e. preservation of local structures), -#' between phenotype distances (preservation of global structures), and the Gromov-Wasserstein (GW) distance between original and +#' between phenotype distances (preservation of global structures), and the Gromov-Wasserstein (GW) distance between original (source) and #' imputed data. #' -#' @param x numeric matrix. An imputed data matrix. +#' @param x numeric matrix. An imputed data matrix of log-intensity. #' @param group factor. A vector of biological groups, experimental conditions or phenotypes (e.g. control, treatment). -#' @param xna numeric matrix. Data matrix with missing values (i.e. the original intensity matrix with NAs) +#' @param y numeric matrix. The source data (i.e. the original log-intensity matrix), preferably subsetted on highly variable peptides (see \code{findVariableFeatures}). +#' @param k numeric. Number of Principal Components used to compute the GW distance. default to 2. #' #' @details For each group of experimental conditions (e.g. treatment and control), the group centroid is calculated as the average #' of observed peptide intensities. Withinness for each group is computed as sum of the squared distances between samples in that group and @@ -16,15 +17,21 @@ #' The GW metric considers preservation of both local and global structures simultaneously. A small GW distance suggests that #' imputation has introduced small distortions to global and local structures overall, whereas a large distance implies significant #' distortions. When comparing two or more imputation methods, the optimal method is the method with smallest GW distance. -#' To compute the GW distance, the missing values in each column of \code{xna} are replaced by mean of observed values in that column. -#' This is equivalent to imputation by KNN, where k is set to the total number of identified peptides (i.e. number of rows in the input matrix). -#' GW distance estimation requires \code{python}. See example. -#' All metrics are on log scale. +#' The GW distance is computed on Principal Components (PCs) of the source and imputed data, instead of peptides. Principal components capture the +#' geometry of the data, hence GW computed on PCs is a better measure of preservation of local and global structures. The PCs in the source data are +#' recommended to be computed on peptides with high biological variance. Hence, users are recommended to subset the source data only on highly variable peptides (hvp) +#' (see \code{findVariableFeatures}). Since the hvp peptides have high biological variance, they are likely to have enough information to discriminate samples +#' from different experimental groups. Hence, PCs computed on those peptides should be representative of the original source data with missing values. +#' If the samples cluster by experimental group in the first couple of PCs, then a choice of k=2 is reasonable. If the desired separation/clustering of samples +#' occurs in later PCs (i.e. the first few PCs are dominated by batches or unwanted variability), then it is recommended to use a larger number of PCs to compute the +#' GW metric. If you are interested in how well the imputed data represent the original data in all possible dimensions, then set k to the number of samples +#' in the data (i.e. the number of columns in the intensity matrix). +#' GW distance estimation requires \code{python}. See example. All metrics are on log scale. #' #' #' @return list of three metrics: withinness (sum of squared distances within a phenotype group), #' betweenness (sum of squared distances between the phenotypes), and gromov-wasserstein distance (if \code{xna} is not NULL). -#' All metrics are on log scale. +#' if \code{group} is NULL only the GW distance is returned. All metrics are on log scale. #' #' #' @examples @@ -49,28 +56,35 @@ #' # you can then run the computeStructuralMetrics() function. #' # Note that the reticulate package should be loaded before loading msImpute. #' set.seed(101) -#' n=200 -#' p=100 -#' J=50 +#' n=12000 +#' p=10 +#' J=5 #' np=n*p #' missfrac=0.3 -#' x=matrix(rnorm(n*J),n,J)%*%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +#' x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)%*%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ +#' matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 #' ix=seq(np) #' imiss=sample(ix,np*missfrac,replace=FALSE) #' xna=x #' xna[imiss]=NA +#' keep <- (rowSums(!is.na(xna)) >= 4) +#' xna <- xna[keep,] +#' rownames(xna) <- 1:nrow(xna) #' y <- xna #' xna <- scaleData(xna) #' xcomplete <- msImpute(object=xna) -#' G <- as.factor(sample(1:5, 100, replace = TRUE)) -#' computeStructuralMetrics(xcomplete, G, y) +#' G <- as.factor(sample(1:3, p, replace = TRUE)) +#' top.hvp <- findVariableFeatures(y) +#' computeStructuralMetrics(xcomplete, G, y[rownames(top.hvp)[1:50],], k = 2) #' @export -computeStructuralMetrics <- function(x, group, xna = NULL){ - out <- list(withinness = log(withinness(x, group)), - betweenness = log(betweenness(x,group))) +computeStructuralMetrics <- function(x, group=NULL, y = NULL, k=2){ + if(!is.null(group)){ + out <- list(withinness = log(withinness(x, group)), + betweenness = log(betweenness(x,group))) + } - if(!is.null(xna)){ - GW <- gromov_wasserstein(xna, x) + if(!is.null(y)){ + GW <- gromov_wasserstein(x, y, k=k) out[['gw_dist']] <- GW[[2]]$gw_dist } return(out) @@ -101,8 +115,33 @@ betweenness <- function(x, class_label){ #' @export -gromov_wasserstein <- function(xna, ximputed){ +gromov_wasserstein <- function(x, y, k, min.mean = 0.1){ + if (k > ncol(x)) stop("Number of Principal Components cannot be greater than number of columns (samples) in the data.") + if (any(!is.finite(x))) stop("Non-finite values (NA, Inf, NaN) encountered in imputed data") + if (any(!is.finite(y))) stop("Non-finite values (NA, Inf, NaN) encountered in source data") + + means <- rowMeans(x) + vars <- matrixStats::rowSds(x) + + # Filtering out zero-variance and low-abundance peptides + is.okay <- !is.na(vars) & vars > 1e-8 & means >= min.mean + + xt <- t(x) + yt <- t(y) + + # compute PCA + xt_pca <- prcomp(xt[,is.okay], scale. = TRUE, center = TRUE) + yt_pca <- prcomp(yt, scale. = TRUE, center = TRUE) + + C1 <- yt_pca$x[,1:k] + C2 <- xt_pca$x[,1:k] + + + cat("Computing GW distance using k=", k, "Principal Components") reticulate::source_python(system.file("python", "gw.py", package = "msImpute")) - xna <- apply(xna, 2, FUN=function(x) {x[is.na(x)] <- mean(x, na.rm=TRUE); return(x)}) - return(gw(t(xna), t(ximputed), ncol(xna))) + return(gw(C1,C2, ncol(x))) } + + + + diff --git a/R/findVariableFeatures.R b/R/findVariableFeatures.R new file mode 100644 index 0000000..22c769d --- /dev/null +++ b/R/findVariableFeatures.R @@ -0,0 +1,25 @@ +#' Find highly variable peptides +#' +#' For each peptide, the total variance is decomposed into biological and technical variance using package \code{scran} +#' @param y numeric matrix giving log-intensity. Can contain NA values. +#' +#' @return A data frame where rows are peptides and columns contain estimates of biological and technical variances. Peptides are ordered by biological variance. +#' +#' @details A loess trend is fitted to total sample variances and mean intensities. For each peptide, the biological variance is then +#' computed by subtracting the estimated technical variance from the loess fit from the total sample variance. +#' +#' @seealso computeStructuralMetrics +#' +#' @export +#' @importFrom scran trendVar decomposeVar +findVariableFeatures <- function(y){ + fit <- trendVar(y) + results <- decomposeVar(y, fit) + plot(results$mean, results$total) + o <- order(results$mean) + lines(results$mean[o], results$tech[o], col="red", lwd=2) + results <- as.data.frame(results) + top.dec <- results[order(results$bio, decreasing=TRUE), ] + return(top.dec) + +} diff --git a/R/msImpute.R b/R/msImpute.R index 50917e6..7cfaeca 100755 --- a/R/msImpute.R +++ b/R/msImpute.R @@ -8,7 +8,7 @@ #' \code{msImpute} operates on the softImpute-ALS algorithm. #' For more details on the underlying algorithm, please see \code{\link[softImpute]{softImpute}} package. #' -#' @param object Numeric matrix where missing values are denoted by NA. Rows are peptides, columns are samples. +#' @param object Numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples. #' @param rank.max Numeric. This restricts the rank of the solution. is set to min(dim(\code{object})-1) by default. #' @param lambda Numeric. Nuclear-norm regularization parameter. Controls the low-rank property of the solution #' to the matrix completion problem. By default, it is determined at the scaling step. If set to zero @@ -24,16 +24,19 @@ #' #' @examples #' set.seed(101) -#' n=200 -#' p=100 -#' J=50 +#' n=12000 +#' p=10 +#' J=5 #' np=n*p #' missfrac=0.3 -#' x=matrix(rnorm(n*J),n,J)%*%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +#' x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)%*%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ +#' matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 #' ix=seq(np) #' imiss=sample(ix,np*missfrac,replace=FALSE) #' xna=x #' xna[imiss]=NA +#' keep <- (rowSums(!is.na(xna)) >= 4) +#' xna <- xna[keep,] #' xna <- scaleData(xna) #' xcomplete <- msImpute(object=xna) #' @seealso selectFeatures, scaleData @@ -50,12 +53,13 @@ msImpute <- function(object, rank.max = NULL, lambda = NULL, thresh = 1e-05, if(is(object, "matrix")) { x <- object xnas <- x + warning("Input is not scaled. Data scaling is recommended for msImpute optimal performance.") } # MAList object # or \code{MAList} object from \link{limma} # if(is(object,"MAList")) x <- object$E - + if(any(is.nan(x) | is.infinite(x))) stop("Inf or NaN values encountered.") if(any(rowSums(!is.na(x)) <= 3)) stop("Peptides with excessive NAs are detected. Please revisit your fitering step. At least 4 non-missing measurements are required for any peptide.") if(any(x < 0, na.rm = TRUE)){ warning("Negative values encountered in imputed data. Please consider revising filtering and/or normalisation steps.") @@ -63,7 +67,7 @@ msImpute <- function(object, rank.max = NULL, lambda = NULL, thresh = 1e-05, if(is.null(rank.max)) rank.max <- min(dim(x) - 1) cat("maximum rank is", rank.max, "\n") cat("computing lambda0 ... \n") - if(is.null(lambda)) lambda <- softImpute::lambda0(x) + if(is.null(lambda)) lambda <- softImpute::lambda0(xnas) cat("lambda0 is", lambda, "\n") cat("fit the low-rank model ... \n") fit <- softImpute::softImpute(xnas,rank=rank.max,lambda=lambda, type = "als", thresh = thresh, diff --git a/R/scaleData.R b/R/scaleData.R index 1f01a28..1b68c5c 100755 --- a/R/scaleData.R +++ b/R/scaleData.R @@ -1,7 +1,7 @@ #' Standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one. #' #' -#' @param object numeric matrix where missing values are denoted by NA. Rows are peptides, columns are samples. +#' @param object numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples. #' @param maxit numeric. maximum iteration for the algorithm to converge (default to 20). When both row and column centering/scaling is requested, iteration may be necessary. #' @param thresh numeric. Convergence threshold (default to 1e-09). #' @param row.center logical. if row.center==TRUE (the default), row centering will be performed resulting in a matrix with row means zero. If row.center is a vector, it will be used to center the rows. If row.center=FALSE nothing is done. @@ -12,6 +12,7 @@ #' #' @details #' Standardizes rows and/or columns of a matrix with missing values, according to the \code{biScale} algorithm in Hastie et al. 2015. +#' Data is assumed to be normalised and log-transformed. #' #' @return #' A list of two components: E and E.scaled. E contains the input matrix, E.scaled contains the scaled data @@ -19,16 +20,19 @@ #' #' @examples #' set.seed(101) -#' n=200 -#' p=100 -#' J=50 +#' n=12000 +#' p=10 +#' J=5 #' np=n*p #' missfrac=0.3 -#' x=matrix(rnorm(n*J),n,J)%*%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +#' x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)%*%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ +#' matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 #' ix=seq(np) #' imiss=sample(ix,np*missfrac,replace=FALSE) #' xna=x #' xna[imiss]=NA +#' keep <- (rowSums(!is.na(xna)) >= 4) +#' xna <- xna[keep,] #' xna <- scaleData(xna) #' @seealso selectFeatures, msImpute #' @export @@ -39,7 +43,7 @@ scaleData <- function(object, maxit = 20, thresh = 1e-09, row.center = TRUE, row }else{ x <- object } - + if(any(is.nan(x) | is.infinite(x))) stop("Inf or NaN values encountered.") if(any(rowSums(!is.na(x)) <= 3)) stop("Peptides with excessive NAs are detected. Please revisit your fitering step. At least 4 non-missing measurements are required for any peptide.") if(any(x < 0, na.rm = TRUE)){ warning("Negative values encountered in imputed data. Please consider revisting the filtering and/or normalisation steps, if appropriate.") diff --git a/R/selectFeatures.R b/R/selectFeatures.R index 76a03d9..18f09eb 100755 --- a/R/selectFeatures.R +++ b/R/selectFeatures.R @@ -4,7 +4,7 @@ #' used to determine if data is Missing Not At Random (MNAR). Users should note that \code{msImpute} assumes peptides #' are Missing At Random (MAR). #' -#' @param object Numeric matrix where missing values are denoted by NA. +#' @param object Numeric matrix giving log-intensity where missing values are denoted by NA. #' Rows are peptides, columns are samples. #' @param n_features Numeric, number of features with high dropout rate. 500 by default. #' @param suppress_plot Logical show plot of dropouts vs abundances. @@ -13,16 +13,19 @@ #' #' @examples #' set.seed(101) -#' n=800 -#' p=100 -#' J=50 +#' n=12000 +#' p=10 +#' J=5 #' np=n*p #' missfrac=0.3 -#' x=matrix(rnorm(n*J),n,J)%*%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +#' x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)%*%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ +#' matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 #' ix=seq(np) #' imiss=sample(ix,np*missfrac,replace=FALSE) #' xna=x #' xna[imiss]=NA +#' keep <- (rowSums(!is.na(xna)) >= 4) +#' xna <- xna[keep,] #' rownames(xna) <- 1:nrow(xna) #' hdp <- selectFeatures(xna, n_features=500, suppress_plot=FALSE) #' # construct matrix M to capture missing entries @@ -59,6 +62,8 @@ selectFeatures <- function(object, n_features=500, suppress_plot = FALSE) { } if(is.null(rownames(x))) stop("No row names in input. Please provide input with named rows.") + if(any(is.nan(x) | is.infinite(x))) stop("Inf or NaN values encountered.") + AveExpr <- rowMeans(x, na.rm = TRUE) dropout <- rowMeans(is.na(x)) diff --git a/README.md b/README.md index 90fd08e..9f4d4cd 100755 --- a/README.md +++ b/README.md @@ -19,10 +19,20 @@ selectFeatures(xna) # xna is a numeric matrix with NAs (for MAR/MNAR diagnosis xna <- scaleData(xna) msImpute(xna, rank.max = 2) # rank 2 approximaiton xcomplete <- msImpute(xna) # optimal rank determined by msImpute -computeStructuralMetrics(xcomplete, group, xna$E) # "group" denotes experimental condition (e.g. control, treatment etc). Requires python. See Manual for more information. + + +# Requires python. See Manual for more information. +top.hvp <- findVariableFeatures(xna$E) +computeStructuralMetrics(xcomplete, + # "group" denotes experimental condition (e.g. control, treatment etc). + group, + xna$E[rownames(top.hvp)[1:50],], + k = 2) + + ``` -See [user manual](https://github.com/DavisLaboratory/msImpute/blob/master/msImpute_1.2.0.pdf) for help. +See [user manual](https://github.com/DavisLaboratory/msImpute/blob/master/msImpute_1.3.0.pdf) for help. **Reference** diff --git a/man/CPD.Rd b/man/CPD.Rd index dce6b8c..0ab15be 100755 --- a/man/CPD.Rd +++ b/man/CPD.Rd @@ -7,9 +7,9 @@ CPD(xorigin, ximputed) } \arguments{ -\item{xorigin}{numeric matrix. The original data. Can not contain missing values.} +\item{xorigin}{numeric matrix. The original log-intensity data. Can not contain missing values.} -\item{ximputed}{numeric matrix. The imputed data. Can not contain missing values.} +\item{ximputed}{numeric matrix. The imputed log-intensity data. Can not contain missing values.} } \value{ numeric diff --git a/man/KNC.Rd b/man/KNC.Rd index eb26957..8163e2d 100755 --- a/man/KNC.Rd +++ b/man/KNC.Rd @@ -7,9 +7,9 @@ KNC(xorigin, ximputed, class, k = 3) } \arguments{ -\item{xorigin}{numeric matrix. The original data. Can contain missing values.} +\item{xorigin}{numeric matrix. The original log-intensity data. Can contain missing values.} -\item{ximputed}{numeric matrix. The imputed data.} +\item{ximputed}{numeric matrix. The imputed log-intensity data.} \item{class}{factor. A vector of length number of columns (samples) in the data specifying the class/label (i.e. experimental group) of each sample.} diff --git a/man/KNN.Rd b/man/KNN.Rd index df36b7a..0477998 100755 --- a/man/KNN.Rd +++ b/man/KNN.Rd @@ -7,9 +7,9 @@ KNN(xorigin, ximputed, k = 3) } \arguments{ -\item{xorigin}{numeric matrix. The original data. Can not contain missing values.} +\item{xorigin}{numeric matrix. The original log-intensity data. Can not contain missing values.} -\item{ximputed}{numeric matrix. The imputed data. Can not contain missing values.} +\item{ximputed}{numeric matrix. The imputed log-intensity data. Can not contain missing values.} \item{k}{number of nearest neighbours. default to k=3.} } diff --git a/man/computeStructuralMetrics.Rd b/man/computeStructuralMetrics.Rd index 7f3316b..f1b0690 100644 --- a/man/computeStructuralMetrics.Rd +++ b/man/computeStructuralMetrics.Rd @@ -4,23 +4,25 @@ \alias{computeStructuralMetrics} \title{Metrics for the assessment of post-imputation structural preservation} \usage{ -computeStructuralMetrics(x, group, xna = NULL) +computeStructuralMetrics(x, group = NULL, y = NULL, k = 2) } \arguments{ -\item{x}{numeric matrix. An imputed data matrix.} +\item{x}{numeric matrix. An imputed data matrix of log-intensity.} \item{group}{factor. A vector of biological groups, experimental conditions or phenotypes (e.g. control, treatment).} -\item{xna}{numeric matrix. Data matrix with missing values (i.e. the original intensity matrix with NAs)} +\item{y}{numeric matrix. The source data (i.e. the original log-intensity matrix), preferably subsetted on highly variable peptides (see \code{findVariableFeatures}).} + +\item{k}{numeric. Number of Principal Components used to compute the GW distance. default to 2.} } \value{ list of three metrics: withinness (sum of squared distances within a phenotype group), betweenness (sum of squared distances between the phenotypes), and gromov-wasserstein distance (if \code{xna} is not NULL). -All metrics are on log scale. +if \code{group} is NULL only the GW distance is returned. All metrics are on log scale. } \description{ For an imputed dataset, it computes within phenotype/experimental condition similarity (i.e. preservation of local structures), -between phenotype distances (preservation of global structures), and the Gromov-Wasserstein (GW) distance between original and +between phenotype distances (preservation of global structures), and the Gromov-Wasserstein (GW) distance between original (source) and imputed data. } \details{ @@ -32,10 +34,16 @@ group distances, hence smaller withinness, and maximizes between group distances The GW metric considers preservation of both local and global structures simultaneously. A small GW distance suggests that imputation has introduced small distortions to global and local structures overall, whereas a large distance implies significant distortions. When comparing two or more imputation methods, the optimal method is the method with smallest GW distance. -To compute the GW distance, the missing values in each column of \code{xna} are replaced by mean of observed values in that column. -This is equivalent to imputation by KNN, where k is set to the total number of identified peptides (i.e. number of rows in the input matrix). -GW distance estimation requires \code{python}. See example. -All metrics are on log scale. +The GW distance is computed on Principal Components (PCs) of the source and imputed data, instead of peptides. Principal components capture the +geometry of the data, hence GW computed on PCs is a better measure of preservation of local and global structures. The PCs in the source data are +recommended to be computed on peptides with high biological variance. Hence, users are recommended to subset the source data only on highly variable peptides (hvp) +(see \code{findVariableFeatures}). Since the hvp peptides have high biological variance, they are likely to have enough information to discriminate samples +from different experimental groups. Hence, PCs computed on those peptides should be representative of the original source data with missing values. +If the samples cluster by experimental group in the first couple of PCs, then a choice of k=2 is reasonable. If the desired separation/clustering of samples +occurs in later PCs (i.e. the first few PCs are dominated by batches or unwanted variability), then it is recommended to use a larger number of PCs to compute the +GW metric. If you are interested in how well the imputed data represent the original data in all possible dimensions, then set k to the number of samples +in the data (i.e. the number of columns in the intensity matrix). +GW distance estimation requires \code{python}. See example. All metrics are on log scale. } \examples{ # To compute the GW distance you need to have python installed @@ -59,19 +67,24 @@ use_virtualenv("msImpute-reticulate") # you can then run the computeStructuralMetrics() function. # Note that the reticulate package should be loaded before loading msImpute. set.seed(101) -n=200 -p=100 -J=50 +n=12000 +p=10 +J=5 np=n*p missfrac=0.3 -x=matrix(rnorm(n*J),n,J)\%*\%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)\%*\%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ + matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 ix=seq(np) imiss=sample(ix,np*missfrac,replace=FALSE) xna=x xna[imiss]=NA +keep <- (rowSums(!is.na(xna)) >= 4) +xna <- xna[keep,] +rownames(xna) <- 1:nrow(xna) y <- xna xna <- scaleData(xna) xcomplete <- msImpute(object=xna) -G <- as.factor(sample(1:5, 100, replace = TRUE)) -computeStructuralMetrics(xcomplete, G, y) +G <- as.factor(sample(1:3, p, replace = TRUE)) +top.hvp <- findVariableFeatures(y) +computeStructuralMetrics(xcomplete, G, y[rownames(top.hvp)[1:50],], k = 2) } diff --git a/man/findVariableFeatures.Rd b/man/findVariableFeatures.Rd new file mode 100644 index 0000000..90a7bd6 --- /dev/null +++ b/man/findVariableFeatures.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/findVariableFeatures.R +\name{findVariableFeatures} +\alias{findVariableFeatures} +\title{Find highly variable peptides} +\usage{ +findVariableFeatures(y) +} +\arguments{ +\item{y}{numeric matrix giving log-intensity. Can contain NA values.} +} +\value{ +A data frame where rows are peptides and columns contain estimates of biological and technical variances. Peptides are ordered by biological variance. +} +\description{ +For each peptide, the total variance is decomposed into biological and technical variance using package \code{scran} +} +\details{ +A loess trend is fitted to total sample variances and mean intensities. For each peptide, the biological variance is then +computed by subtracting the estimated technical variance from the loess fit from the total sample variance. +} +\seealso{ +computeStructuralMetrics +} diff --git a/man/msImpute.Rd b/man/msImpute.Rd index f10287d..cdbf928 100755 --- a/man/msImpute.Rd +++ b/man/msImpute.Rd @@ -9,7 +9,7 @@ msImpute(object, rank.max = NULL, lambda = NULL, thresh = 1e-05, final.svd = TRUE) } \arguments{ -\item{object}{Numeric matrix where missing values are denoted by NA. Rows are peptides, columns are samples.} +\item{object}{Numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples.} \item{rank.max}{Numeric. This restricts the rank of the solution. is set to min(dim(\code{object})-1) by default.} @@ -44,16 +44,19 @@ For more details on the underlying algorithm, please see \code{\link[softImpute] } \examples{ set.seed(101) -n=200 -p=100 -J=50 +n=12000 +p=10 +J=5 np=n*p missfrac=0.3 -x=matrix(rnorm(n*J),n,J)\%*\%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)\%*\%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ + matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 ix=seq(np) imiss=sample(ix,np*missfrac,replace=FALSE) xna=x xna[imiss]=NA +keep <- (rowSums(!is.na(xna)) >= 4) +xna <- xna[keep,] xna <- scaleData(xna) xcomplete <- msImpute(object=xna) } diff --git a/man/scaleData.Rd b/man/scaleData.Rd index f3132bd..c9c30ff 100755 --- a/man/scaleData.Rd +++ b/man/scaleData.Rd @@ -9,7 +9,7 @@ scaleData(object, maxit = 20, thresh = 1e-09, row.center = TRUE, trace = FALSE) } \arguments{ -\item{object}{numeric matrix where missing values are denoted by NA. Rows are peptides, columns are samples.} +\item{object}{numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples.} \item{maxit}{numeric. maximum iteration for the algorithm to converge (default to 20). When both row and column centering/scaling is requested, iteration may be necessary.} @@ -33,19 +33,23 @@ Standardize a matrix to have optionally row means zero and variances one, and/or } \details{ Standardizes rows and/or columns of a matrix with missing values, according to the \code{biScale} algorithm in Hastie et al. 2015. +Data is assumed to be normalised and log-transformed. } \examples{ set.seed(101) -n=200 -p=100 -J=50 +n=12000 +p=10 +J=5 np=n*p missfrac=0.3 -x=matrix(rnorm(n*J),n,J)\%*\%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)\%*\%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ + matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 ix=seq(np) imiss=sample(ix,np*missfrac,replace=FALSE) xna=x xna[imiss]=NA +keep <- (rowSums(!is.na(xna)) >= 4) +xna <- xna[keep,] xna <- scaleData(xna) } \seealso{ diff --git a/man/selectFeatures.Rd b/man/selectFeatures.Rd index 91d83e7..9250d72 100755 --- a/man/selectFeatures.Rd +++ b/man/selectFeatures.Rd @@ -7,7 +7,7 @@ selectFeatures(object, n_features = 500, suppress_plot = FALSE) } \arguments{ -\item{object}{Numeric matrix where missing values are denoted by NA. +\item{object}{Numeric matrix giving log-intensity where missing values are denoted by NA. Rows are peptides, columns are samples.} \item{n_features}{Numeric, number of features with high dropout rate. 500 by default.} @@ -24,16 +24,19 @@ are Missing At Random (MAR). } \examples{ set.seed(101) -n=800 -p=100 -J=50 +n=12000 +p=10 +J=5 np=n*p missfrac=0.3 -x=matrix(rnorm(n*J),n,J)\%*\%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5 +x=matrix(rnorm(n*J,mean = 5,sd = 0.2),n,J)\%*\%matrix(rnorm(J*p, mean = 5,sd = 0.2),J,p)+ + matrix(rnorm(np,mean = 5,sd = 0.2),n,p)/5 ix=seq(np) imiss=sample(ix,np*missfrac,replace=FALSE) xna=x xna[imiss]=NA +keep <- (rowSums(!is.na(xna)) >= 4) +xna <- xna[keep,] rownames(xna) <- 1:nrow(xna) hdp <- selectFeatures(xna, n_features=500, suppress_plot=FALSE) # construct matrix M to capture missing entries diff --git a/msImpute_1.2.0.pdf b/msImpute_1.2.0.pdf deleted file mode 100644 index e7e35fb1bfdaff7973653925cfdd65b88eb04d00..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 89001 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