Adds .assignClass() to reduce repeated code.
Reorders if statements for clarity. Removes
unnecessary EM function args.
Max McGrath authored on 23/07/2021 17:21:57 | ... | ... |
@@ -1,6 +1,6 @@ |
| 1 | 1 |
# Applied from Lai et al 2007 Bioinformatics. |
| 2 | 2 |
# |
| 3 |
-# Code written by Charlotte Siska |
|
| 3 |
+# Code written by Charlotte Siska and Max McGrath |
|
| 4 | 4 |
# R functions fishersTrans, createVectors, discordantRun and makeTable |
| 5 | 5 |
# Email: charlotte.siska@ucdenver.edu |
| 6 | 6 |
# |
| ... | ... |
@@ -12,163 +12,124 @@ |
| 12 | 12 |
# R function for SparCC, adapted from https://bitbucket.org/yonatanf/sparcc |
| 13 | 13 |
# Email: hyfang@pku.edu.cn |
| 14 | 14 |
|
| 15 |
+#' @export |
|
| 15 | 16 |
discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE, |
| 16 | 17 |
subsampling = FALSE, subSize = dim(x)[1], iter = 100, |
| 17 | 18 |
components = 3) {
|
| 18 | 19 |
|
| 19 |
- .checkDiscordantInputs(v1, v2, x, y, transform, subsampling, subSize, iter, |
|
| 20 |
- components) |
|
| 21 |
- |
|
| 22 |
- if (transform) {
|
|
| 23 |
- v1 <- fishersTrans(v1) |
|
| 24 |
- v2 <- fishersTrans(v2) |
|
| 25 |
- } |
|
| 26 |
- |
|
| 27 |
- x <- exprs(x) |
|
| 28 |
- if(is.null(y) == FALSE) { y <- exprs(y) }
|
|
| 29 |
- featureSize = dim(x)[1] |
|
| 30 |
- |
|
| 31 |
- pdata <- cbind(v1, v2) |
|
| 32 |
- |
|
| 33 |
- param1 <- sd(v1) |
|
| 34 |
- param2 <- sd(v2) |
|
| 35 |
- |
|
| 36 |
- if(components == 3) {
|
|
| 37 |
- class <- cbind(rep(0, dim(pdata)[1]), rep(0, dim(pdata)[1])) |
|
| 38 |
- class[pdata[,1]>0+param1,1] <- 2 |
|
| 39 |
- class[pdata[,1]<0-param1,1] <- 1 |
|
| 40 |
- class[pdata[,2]>0+param2,2] <- 2 |
|
| 41 |
- class[pdata[,2]<0-param2,2] <- 1 |
|
| 42 |
- discordClass <- c(2,3,4,6,7,8) |
|
| 43 |
- } |
|
| 44 |
- |
|
| 45 |
- if(components == 5) {
|
|
| 46 |
- class <- cbind(rep(0, dim(pdata)[1]), rep(0, dim(pdata)[1])) |
|
| 47 |
- class[pdata[,1]>0+param1,1] <- 2 |
|
| 48 |
- class[pdata[,1]<0-param1,1] <- 1 |
|
| 49 |
- class[pdata[,1]>0+(2*param1),1] <- 4 |
|
| 50 |
- class[pdata[,1]<0-(2*param1),1] <- 3 |
|
| 51 |
- class[pdata[,2]>0+param2,2] <- 2 |
|
| 52 |
- class[pdata[,2]<0-param2,2] <- 1 |
|
| 53 |
- class[pdata[,2]>0+(2*param2),2] <- 4 |
|
| 54 |
- class[pdata[,2]<0-(2*param2),2] <- 3 |
|
| 55 |
- concordClass <- c(1,7,13,19,25) |
|
| 56 |
- discordClass <- setdiff(1:25,concordClass) |
|
| 57 |
- } |
|
| 20 |
+ .checkDiscordantInputs(v1, v2, x, y, transform, subsampling, subSize, iter, |
|
| 21 |
+ components) |
|
| 22 |
+ |
|
| 23 |
+ if (transform) {
|
|
| 24 |
+ v1 <- fishersTrans(v1) |
|
| 25 |
+ v2 <- fishersTrans(v2) |
|
| 26 |
+ } |
|
| 58 | 27 |
|
| 59 |
- if(subsampling == TRUE) {
|
|
| 60 |
- subSize = nrow(x) |
|
| 61 |
- if(is.null(y) == FALSE & nrow(y) < subSize) {
|
|
| 62 |
- subSize = nrow(y) |
|
| 28 |
+ x <- exprs(x) |
|
| 29 |
+ if (is.null(y) == FALSE) { y <- exprs(y) }
|
|
| 30 |
+ featureSize = dim(x)[1] |
|
| 31 |
+ |
|
| 32 |
+ pdata <- cbind(v1, v2) |
|
| 33 |
+ param1 <- sd(v1) |
|
| 34 |
+ param2 <- sd(v2) |
|
| 35 |
+ class <- cbind(.assignClass(v1, param1, components), |
|
| 36 |
+ .assignClass(v2, param2, components)) |
|
| 37 |
+ |
|
| 38 |
+ if (components == 3) {
|
|
| 39 |
+ discordClass <- c(2,3,4,6,7,8) |
|
| 40 |
+ } else {
|
|
| 41 |
+ discordClass <- setdiff(1:25, c(1, 7, 13, 19, 25)) |
|
| 63 | 42 |
} |
| 64 |
- total_mu <- rep(0,3) |
|
| 65 |
- total_sigma <- rep(0,3) |
|
| 66 |
- total_nu <- rep(0,3) |
|
| 67 |
- total_tau <- rep(0,3) |
|
| 68 |
- total_pi <- rep(0,3) |
|
| 69 |
- for(i in 1:iter) {
|
|
| 70 |
- # make sure pairs are independent |
|
| 71 |
- rowIndex <- sample(nrow(x), subSize) |
|
| 72 |
- colIndex <- sample(nrow(y), subSize) |
|
| 73 |
- mat1 <- matrix(v1, nrow = nrow(x), byrow = FALSE) |
|
| 74 |
- mat2 <- matrix(v2, nrow = nrow(x), byrow = FALSE) |
|
| 43 |
+ |
|
| 44 |
+ if (subsampling) {
|
|
| 45 |
+ subSize <- min(nrow(x), nrow(y)) |
|
| 46 |
+ total_mu <- total_sigma <- total_nu <- |
|
| 47 |
+ total_tau <- total_pi <- rep(0, components) |
|
| 48 |
+ # NOTE: changed this from default 3 to components... need to figure out |
|
| 49 |
+ # if that's right |
|
| 50 |
+ |
|
| 51 |
+ for(i in 1:iter) {
|
|
| 52 |
+ # make sure pairs are independent |
|
| 53 |
+ rowIndex <- sample(nrow(x), subSize) |
|
| 54 |
+ colIndex <- sample(nrow(y), subSize) |
|
| 55 |
+ mat1 <- matrix(v1, nrow = nrow(x), byrow = FALSE) |
|
| 56 |
+ mat2 <- matrix(v2, nrow = nrow(x), byrow = FALSE) |
|
| 57 |
+ |
|
| 58 |
+ subSampV1 <- sapply(1:subSize, function(x) mat1[rowIndex[x], |
|
| 59 |
+ colIndex[x]]) |
|
| 60 |
+ subSampV2 <- sapply(1:subSize, function(x) mat2[rowIndex[x], |
|
| 61 |
+ colIndex[x]]) |
|
| 62 |
+ |
|
| 63 |
+ sub.pdata <- cbind(subSampV1, subSampV2) |
|
| 64 |
+ sub.class <- cbind(.assignClass(subSampV1, param1, components), |
|
| 65 |
+ .assignClass(subSampV2, param2, components)) |
|
| 66 |
+ |
|
| 67 |
+ pd <- em.normal.partial.concordant(sub.pdata, sub.class, components) |
|
| 68 |
+ total_mu <- total_mu + pd$mu_sigma[1,] |
|
| 69 |
+ total_sigma <- total_sigma + pd$mu_sigma[2,] |
|
| 70 |
+ total_nu <- total_nu + pd$nu_tau[1,] |
|
| 71 |
+ total_tau <- total_tau + pd$nu_tau[2,] |
|
| 72 |
+ total_pi <- total_pi + pd$pi |
|
| 73 |
+ } |
|
| 75 | 74 |
|
| 76 |
- subSampV1 <- sapply(1:subSize, function(x) mat1[rowIndex[x], |
|
| 77 |
- colIndex[x]]) |
|
| 78 |
- subSampV2 <- sapply(1:subSize, function(x) mat2[rowIndex[x], |
|
| 79 |
- colIndex[x]]) |
|
| 75 |
+ mu <- total_mu / iter |
|
| 76 |
+ sigma <- total_sigma / iter |
|
| 77 |
+ nu <- total_nu / iter |
|
| 78 |
+ tau <- total_tau / iter |
|
| 79 |
+ pi <- total_pi / iter |
|
| 80 | 80 |
|
| 81 |
- sub.pdata <- cbind(subSampV1, subSampV2) |
|
| 82 |
- sub.class <- cbind(rep(0, subSize), rep(0, subSize)) |
|
| 83 |
- if(components == 3) {
|
|
| 84 |
- sub.class[sub.pdata[,1]>0+param1,1] <- 2 |
|
| 85 |
- sub.class[sub.pdata[,1]<0-param1,1] <- 1 |
|
| 86 |
- sub.class[sub.pdata[,2]>0+param2,2] <- 2 |
|
| 87 |
- sub.class[sub.pdata[,2]<0-param2,2] <- 1 |
|
| 88 |
- } |
|
| 89 |
- if(components == 5) {
|
|
| 90 |
- sub.class[sub.pdata[,1]>0+param1,1] <- 2 |
|
| 91 |
- sub.class[sub.pdata[,1]<0-param1,1] <- 1 |
|
| 92 |
- sub.class[sub.pdata[,1]>0+(2*param1),1] <- 4 |
|
| 93 |
- sub.class[sub.pdata[,1]<0-(2*param1),1] <- 3 |
|
| 94 |
- sub.class[pdata[,2]>0+param2,2] <- 2 |
|
| 95 |
- sub.class[pdata[,2]<0-param2,2] <- 1 |
|
| 96 |
- sub.class[pdata[,2]>0+(2*param2),2] <- 4 |
|
| 97 |
- sub.class[pdata[,2]<0-(2*param2),2] <- 3 |
|
| 98 |
- } |
|
| 99 |
- pd <- em.normal.partial.concordant(sub.pdata, sub.class, tol = 0.001, |
|
| 100 |
- restriction = 0, |
|
| 101 |
- constrain = c(0,-sd(pdata),sd(pdata)), |
|
| 102 |
- iteration = 1000, |
|
| 103 |
- components = components) |
|
| 104 |
- total_mu <- total_mu + pd$mu_sigma[1,] |
|
| 105 |
- total_sigma <- total_sigma + pd$mu_sigma[2,] |
|
| 106 |
- total_nu <- total_nu + pd$nu_tau[1,] |
|
| 107 |
- total_tau <- total_tau + pd$nu_tau[2,] |
|
| 108 |
- total_pi <- total_pi + pd$pi |
|
| 81 |
+ finalResult <- subSampleData(pdata, class, mu, sigma, nu, tau, pi, |
|
| 82 |
+ components) |
|
| 83 |
+ zTable <- finalResult$z |
|
| 84 |
+ classVector <- finalResult$class |
|
| 85 |
+ } else {
|
|
| 86 |
+ pd <- em.normal.partial.concordant(pdata, class, components) |
|
| 87 |
+ zTable <- pd$z |
|
| 88 |
+ classVector <- pd$class |
|
| 109 | 89 |
} |
| 110 | 90 |
|
| 111 |
- mu <- total_mu / iter |
|
| 112 |
- sigma <- total_sigma / iter |
|
| 113 |
- nu <- total_nu / iter |
|
| 114 |
- tau <- total_tau / iter |
|
| 115 |
- pi <- total_pi / iter |
|
| 91 |
+ discordPPV <- apply(zTable, 1, function(x) sum(x[discordClass])/sum(x)) |
|
| 116 | 92 |
|
| 117 |
- finalResult <- subSampleData(pdata, class, mu, sigma, nu, tau, pi, |
|
| 118 |
- components) |
|
| 119 |
- zTable <- finalResult$z |
|
| 120 |
- classVector <- finalResult$class |
|
| 121 |
- } else {
|
|
| 122 |
- pd <- em.normal.partial.concordant(pdata, class, tol = 0.001, |
|
| 123 |
- restriction = 0, |
|
| 124 |
- constrain = c(0, -sd(pdata), sd(pdata)), |
|
| 125 |
- iteration = 1000, |
|
| 126 |
- components = components) |
|
| 127 |
- zTable <- pd$z |
|
| 128 |
- classVector <- pd$class |
|
| 129 |
- } |
|
| 130 |
- |
|
| 131 |
- discordPPV <- apply(zTable, 1, function(x) sum(x[discordClass])/sum(x)) |
|
| 132 |
- |
|
| 133 |
- if(is.null(y) == FALSE) {
|
|
| 134 |
- discordPPMatrix <- matrix(discordPPV, nrow = featureSize, |
|
| 135 |
- byrow = FALSE) |
|
| 136 |
- classMatrix <- matrix(classVector, nrow = featureSize, byrow = FALSE) |
|
| 137 |
- rownames(discordPPMatrix) <- rownames(x) |
|
| 138 |
- colnames(discordPPMatrix) <- rownames(y) |
|
| 139 |
- rownames(classMatrix) <- rownames(x) |
|
| 140 |
- colnames(classMatrix) <- rownames(y) |
|
| 93 |
+ if(is.null(y)) {
|
|
| 94 |
+ discordPPMatrix <- matrix(NA,nrow = featureSize, ncol = featureSize) |
|
| 95 |
+ classMatrix <- discordPPMatrix |
|
| 96 |
+ diag <- lower.tri(discordPPMatrix, diag = FALSE) |
|
| 97 |
+ discordPPMatrix[diag] <- discordPPV |
|
| 98 |
+ classMatrix[diag] <- classVector |
|
| 99 |
+ rownames(discordPPMatrix) <- rownames(x) |
|
| 100 |
+ colnames(discordPPMatrix) <- rownames(x) |
|
| 101 |
+ rownames(classMatrix) <- rownames(x) |
|
| 102 |
+ colnames(classMatrix) <- rownames(x) |
|
| 103 |
+ vector_names <- getNames(x) |
|
| 104 |
+ names(discordPPV) <- vector_names |
|
| 105 |
+ names(classVector) <- vector_names |
|
| 106 |
+ } else {
|
|
| 107 |
+ discordPPMatrix <- matrix(discordPPV, nrow = featureSize, |
|
| 108 |
+ byrow = FALSE) |
|
| 109 |
+ classMatrix <- matrix(classVector, nrow = featureSize, byrow = FALSE) |
|
| 110 |
+ rownames(discordPPMatrix) <- rownames(x) |
|
| 111 |
+ colnames(discordPPMatrix) <- rownames(y) |
|
| 112 |
+ rownames(classMatrix) <- rownames(x) |
|
| 113 |
+ colnames(classMatrix) <- rownames(y) |
|
| 114 |
+ |
|
| 115 |
+ vector_names <- getNames(x,y) |
|
| 116 |
+ names(discordPPV) <- vector_names |
|
| 117 |
+ names(classVector) <- vector_names |
|
| 118 |
+ } |
|
| 141 | 119 |
|
| 142 |
- vector_names <- getNames(x,y) |
|
| 143 |
- names(discordPPV) <- vector_names |
|
| 144 |
- names(classVector) <- vector_names |
|
| 145 |
- } |
|
| 146 |
- |
|
| 147 |
- if(is.null(y)) {
|
|
| 148 |
- discordPPMatrix <- matrix(NA,nrow = featureSize, ncol = featureSize) |
|
| 149 |
- classMatrix <- discordPPMatrix |
|
| 150 |
- diag <- lower.tri(discordPPMatrix, diag = FALSE) |
|
| 151 |
- discordPPMatrix[diag] <- discordPPV |
|
| 152 |
- classMatrix[diag] <- classVector |
|
| 153 |
- rownames(discordPPMatrix) <- rownames(x) |
|
| 154 |
- colnames(discordPPMatrix) <- rownames(x) |
|
| 155 |
- rownames(classMatrix) <- rownames(x) |
|
| 156 |
- colnames(classMatrix) <- rownames(x) |
|
| 157 |
- vector_names <- getNames(x) |
|
| 158 |
- names(discordPPV) <- vector_names |
|
| 159 |
- names(classVector) <- vector_names |
|
| 160 |
- } |
|
| 161 |
- |
|
| 162 |
- zTable <- t(apply(zTable, 1, function(x) x/sum(x))) |
|
| 163 |
- rownames(zTable) <- vector_names |
|
| 164 |
- |
|
| 165 |
- return(list(discordPPMatrix = discordPPMatrix, discordPPVector = |
|
| 166 |
- discordPPV, classMatrix = classMatrix, classVector = classVector, |
|
| 167 |
- probMatrix = zTable, loglik = pd$loglik)) |
|
| 120 |
+ zTable <- t(apply(zTable, 1, function(x) x/sum(x))) |
|
| 121 |
+ rownames(zTable) <- vector_names |
|
| 122 |
+ |
|
| 123 |
+ return(list(discordPPMatrix = discordPPMatrix, discordPPVector = discordPPV, |
|
| 124 |
+ classMatrix = classMatrix, classVector = classVector, |
|
| 125 |
+ probMatrix = zTable, loglik = pd$loglik)) |
|
| 168 | 126 |
} |
| 169 | 127 |
|
| 170 |
-em.normal.partial.concordant <- function(data, class, tol=0.001, |
|
| 171 |
- restriction=0, constrain=0, iteration=1000, components) {
|
|
| 128 |
+em.normal.partial.concordant <- function(data, class, components) {
|
|
| 129 |
+ tol <- 0.001 |
|
| 130 |
+ restriction <- 0 |
|
| 131 |
+ constrain <- 0 |
|
| 132 |
+ iteration <- 1000 |
|
| 172 | 133 |
n <- as.integer(dim(data)[1]) |
| 173 | 134 |
g <- as.integer(nlevels(as.factor(class))) |
| 174 | 135 |
|
| ... | ... |
@@ -209,6 +170,21 @@ em.normal.partial.concordant <- function(data, class, tol=0.001, |
| 209 | 170 |
z = array(results[[3]], dim = c(n, g*g)))) |
| 210 | 171 |
} |
| 211 | 172 |
|
| 173 |
+ |
|
| 174 |
+# Internal function to assign class to vector based on number of components and |
|
| 175 |
+# each elements distance from zero |
|
| 176 |
+#' @importFrom dplyr case_when |
|
| 177 |
+.assignClass <- function(x, param, components) {
|
|
| 178 |
+ if (components == 3) {
|
|
| 179 |
+ rtn <- case_when(x < -param ~ 1, x > param ~ 2, TRUE ~ 0) |
|
| 180 |
+ } else {
|
|
| 181 |
+ rtn <- case_when(x < -2*param ~ 3, x > 2*param ~ 4, x < -param ~ 1, |
|
| 182 |
+ x > param ~ 2, TRUE ~ 0) |
|
| 183 |
+ } |
|
| 184 |
+ return(rtn) |
|
| 185 |
+} |
|
| 186 |
+ |
|
| 187 |
+# Internal function to validate user inputs for discordantRun() |
|
| 212 | 188 |
.checkDiscordantInputs <- function(v1, v2, x, y, transform, |
| 213 | 189 |
subsampling, subSize, iter, |
| 214 | 190 |
components) {
|
| ... | ... |
@@ -217,6 +193,11 @@ em.normal.partial.concordant <- function(data, class, tol=0.001, |
| 217 | 193 |
stop("x and y (if present) must be type ExpressionSet")
|
| 218 | 194 |
} |
| 219 | 195 |
|
| 196 |
+ # Need to double check if this is true w/ Katerina |
|
| 197 |
+ if (is.null(y) && subsampling) {
|
|
| 198 |
+ stop("y cannot be NULL if subsampling is TRUE")
|
|
| 199 |
+ } |
|
| 200 |
+ |
|
| 220 | 201 |
if (!(components %in% c(3, 5))) {
|
| 221 | 202 |
stop ("components must be equal to 3 or 5")
|
| 222 | 203 |
} |