diff execute_dwt_var_perFeature.R @ 3:6c29c7e347e8 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-devteam/tree/master/tools/dwt_var_perfeature commit f929353ffb0623f2218d7dec459c7da62f3b0d24"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/execute_dwt_var_perFeature.R	Mon Jul 06 20:34:27 2020 -0400
@@ -0,0 +1,147 @@
+#####################################################################
+## plot multiscale wavelet variance
+## create null bands by permuting the original data series
+## generate plots and table of wavelet variance including p-values
+#######################################################################
+options(echo = FALSE)
+library("wavethresh");
+library("waveslim");
+library("bitops");
+
+## to determine if data is properly formatted 2^N observations
+is_power2 <- function(x) {
+    x && !(bitops::bitAnd(x, x - 1));
+}
+
+## dwt : discrete wavelet transform using Haar wavelet filter, simplest wavelet function but later can modify to let user-define the wavelet filter function
+dwt_var_permut_get_max <- function(data, names, alpha, filter = 1, family = "DaubExPhase", bc = "symmetric", method = "kendall", wf = "haar", boundary = "reflection") {
+    title <- NULL;
+    final_pvalue <- NULL;
+    j <- NULL;
+    scale <- NULL;
+    out <- NULL;
+
+    print(class(data));
+    print(names);
+    print(alpha);
+
+    par(mar = c(5, 4, 4, 3), oma = c(4, 4, 3, 2), xaxt = "s", cex = 1, las = 1);
+
+    title <- c("Wavelet", "Variance", "Pvalue", "Test");
+    print(title);
+
+    for (i in seq_len(length(names))) {
+        temp <- NULL;
+        results <- NULL;
+        wave1_dwt <- NULL;
+
+        ## if data fails formatting check, do something
+        print(is.numeric(as.matrix(data)[, i]));
+        if (!is.numeric(as.matrix(data)[, i])) {
+            stop("data must be a numeric vector");
+        }
+        print(length(as.matrix(data)[, i]));
+        print(is_power2(length(as.matrix(data)[, i])));
+        if (!is_power2(length(as.matrix(data)[, i]))) {
+            stop("data length must be a power of two");
+        }
+        j <- wavethresh::wd(as.matrix(data)[, i], filter.number = filter, family = family, bc = bc)$nlevels;
+        print(j);
+        temp <- vector(length = j);
+        wave1_dwt <- waveslim::dwt(as.matrix(data)[, i], wf = wf, j, boundary = boundary);
+
+        temp <- waveslim::wave.variance(wave1_dwt)[- (j + 1), 1];
+        print(temp);
+
+        ##permutations code :
+        feature1 <- NULL;
+        null <- NULL;
+        var_lower <- NULL;
+        limit_lower <- NULL;
+        var_upper <- NULL;
+        limit_upper <- NULL;
+        med <- NULL;
+
+        limit_lower <- alpha / 2 * 1000;
+        print(limit_lower);
+        limit_upper <- (1 - alpha / 2) * 1000;
+        print(limit_upper);
+
+        feature1 <- as.matrix(data)[, i];
+        for (k in 1:1000) {
+            nk_1 <- NULL;
+            null_levels <- NULL;
+            var <- NULL;
+            null_wave1 <- NULL;
+
+            nk_1 <- sample(feature1, length(feature1), replace = FALSE);
+            null_levels <- wavethresh::wd(nk_1, filter.number = filter, family = family, bc = bc)$nlevels;
+            var <- vector(length = length(null_levels));
+            null_wave1 <- waveslim::dwt(nk_1, wf = wf, j, boundary = boundary);
+            var <- waveslim::wave.variance(null_wave1)[- (null_levels + 1), 1];
+            null <- rbind(null, var);
+        }
+        null <- apply(null, 2, sort, na.last = TRUE);
+        var_lower <- null[limit_lower, ];
+        var_upper <- null[limit_upper, ];
+        med <- (apply(null, 2, median, na.rm = TRUE));
+
+        ## plot
+        results <- cbind(temp, var_lower, var_upper);
+        print(results);
+        matplot(results, type = "b", pch = "*", lty = 1, col = c(1, 2, 2), xaxt = "n", xlab = "Wavelet Scale", ylab = "Wavelet variance");
+        mtext(names[i], side = 3, line = 0.5, cex = 1);
+        axis(1, at = 1:j, labels = c(2 ^ (0:(j - 1))), las = 3, cex.axis = 1);
+
+        ## get pvalues by comparison to null distribution
+        for (m in seq_len(length(temp))) {
+            print(paste("scale", m, sep = " "));
+            print(paste("var", temp[m], sep = " "));
+            print(paste("med", med[m], sep = " "));
+            pv <- NULL;
+            tail <- NULL;
+            scale <- NULL;
+            scale <- 2 ^ (m - 1);
+            if (temp[m] >= med[m]) {
+                ## R tail test
+                print("R");
+                tail <- "R";
+                pv <- (length(which(null[, m] >= temp[m]))) / (length(na.exclude(null[, m])));
+            } else {
+                if (temp[m] < med[m]) {
+                    ## L tail test
+                    print("L");
+                    tail <- "L";
+                    pv <- (length(which(null[, m] <= temp[m]))) / (length(na.exclude(null[, m])));
+                }
+            }
+            print(pv);
+            out <- rbind(out, c(paste("Scale", scale, sep = "_"), format(temp[m], digits = 3), pv, tail));
+        }
+        final_pvalue <- rbind(final_pvalue, out);
+    }
+    colnames(final_pvalue) <- title;
+    return(final_pvalue);
+}
+
+## execute
+## read in data
+args <- commandArgs(trailingOnly = TRUE)
+
+data_test <- NULL;
+final <- NULL;
+sub <- NULL;
+sub_names <- NULL;
+data_test <- read.delim(args[1], header = FALSE);
+pdf(file = args[5], width = 11, height = 8)
+for (f in strsplit(args[2], ",")) {
+    f <- as.integer(f)
+    if (f > ncol(data_test))
+        stop(paste("column", f, "doesn't exist"));
+    sub <- data_test[, f];
+    sub_names <- colnames(data_test)[f];
+    final <- rbind(final, dwt_var_permut_get_max(sub, sub_names, as.double(args[3])));
+}
+
+dev.off();
+write.table(final, file = args[4], sep = "\t", quote = FALSE, row.names = FALSE);