annotate execute_dwt_cor_aVb_all.R @ 2:e01e8a9a82f4 draft default tip

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

#################################################################################
## code to do all correlation tests of form: motif(a) vs. motif(b)
## add code to create null bands by permuting the original data series
## generate plots and table matrix of correlation coefficients including p-values
#################################################################################
library("wavethresh");
library("waveslim");

options(echo = FALSE)

## normalize data
norm <- function(data) {
    v <- (data - mean(data)) / sd(data);
    if (sum(is.na(v)) >= 1) {
        v <- data;
    }
    return(v);
}

dwt_cor <- function(data_short, names_short, data_long, names_long, test, pdf, table, filter = 4, bc = "symmetric", method = "kendall", wf = "haar", boundary = "reflection") {
    print(test);
    print(pdf);
    print(table);

    pdf(file = pdf);
    final_pvalue <- NULL;
    title <- NULL;

    short_levels <- wavethresh::wd(data_short[, 1], filter.number = filter, bc = bc)$nlevels;
    title <- c("motif1", "motif2");
    for (i in 1:short_levels) {
        title <- c(title, paste(i, "cor", sep = "_"), paste(i, "pval", sep = "_"));
    }
    print(title);

    ## normalize the raw data
    data_short <- apply(data_short, 2, norm);
    data_long <- apply(data_long, 2, norm);

    ## loop to compare a vs b
    for (i in seq_len(length(names_short))) {
        for (j in seq_len(i - 1)) {
            ## Kendall Tau
            ## DWT wavelet correlation function
            ## include significance to compare
            wave1_dwt <- NULL;
            wave2_dwt <- NULL;
            tau_dwt <- NULL;
            out <- NULL;

            print(names_short[i]);
            print(names_long[j]);

            ## need exit if not comparing motif(a) vs motif(a)
            if (names_short[i] == names_long[j]) {
                stop(paste("motif", names_short[i], "is the same as", names_long[j], sep = " "));
            }
            else {
                wave1_dwt <- waveslim::dwt(data_short[, i], wf = wf, short_levels, boundary = boundary);
                wave2_dwt <- waveslim::dwt(data_long[, j], wf = wf, short_levels, boundary = boundary);
                tau_dwt <- vector(length = short_levels)

                ## perform cor test on wavelet coefficients per scale
                for (level in 1:short_levels) {
                    w1_level <- NULL;
                    w2_level <- NULL;
                    w1_level <- (wave1_dwt[[level]]);
                    w2_level <- (wave2_dwt[[level]]);
                    tau_dwt[level] <- cor.test(w1_level, w2_level, method = method)$estimate;
                }

                ## CI bands by permutation of time series
                feature1 <- NULL;
                feature2 <- NULL;
                feature1 <- data_short[, i];
                feature2 <- data_long[, j];
                null <- NULL;
                results <- NULL;
                med <- NULL;
                cor_25 <- NULL;
                cor_975 <- NULL;

                for (k in 1:1000) {
                    nk_1 <- NULL;
                    nk_2 <- NULL;
                    null_levels <- NULL;
                    cor <- NULL;
                    null_wave1 <- NULL;
                    null_wave2 <- NULL;

                    nk_1 <- sample(feature1, length(feature1), replace = FALSE);
                    nk_2 <- sample(feature2, length(feature2), replace = FALSE);
                    null_levels <- wavethresh::wd(nk_1, filter.number = filter, bc = bc)$nlevels;
                    cor <- vector(length = null_levels);
                    null_wave1 <- waveslim::dwt(nk_1, wf = wf, short_levels, boundary = boundary);
                    null_wave2 <- waveslim::dwt(nk_2, wf = wf, short_levels, boundary = boundary);

                    for (level in 1:null_levels) {
                        null_level1 <- NULL;
                        null_level2 <- NULL;
                        null_level1 <- (null_wave1[[level]]);
                        null_level2 <- (null_wave2[[level]]);
                        cor[level] <- cor.test(null_level1, null_level2, method = method)$estimate;
                    }
                    null <- rbind(null, cor);
                }

                null <- apply(null, 2, sort, na.last = TRUE);
                cor_25 <- null[25, ];
                cor_975 <- null[975, ];
                med <- (apply(null, 2, median, na.rm = TRUE));

                ## plot
                results <- cbind(tau_dwt, cor_25, cor_975);
                matplot(results, type = "b", pch = "*", lty = 1, col = c(1, 2, 2), ylim = c(-1, 1), xlab = "Wavelet Scale", ylab = "Wavelet Correlation Kendall's Tau", main = (paste(test, names_short[i], "vs.", names_long[j], sep = " ")), cex.main = 0.75);
                abline(h = 0);

                ## get pvalues by comparison to null distribution
                ### modify pval calculation for error type II of T test ####
                out <- c(names_short[i], names_long[j]);
                for (m in seq_len(length(tau_dwt))) {
                    print(m);
                    print(tau_dwt[m]);
                    out <- c(out, format(tau_dwt[m], digits = 3));
                    pv <- NULL;
                    if (is.na(tau_dwt[m])) {
                        pv <- "NA";
                    }
                    else{
                        if (tau_dwt[m] >= med[m]) {
                            ## R tail test
                            pv <- (length(which(null[, m] >= tau_dwt[m]))) / (length(na.exclude(null[, m])));
                        }
                        else {
                            if (tau_dwt[m] < med[m]) {
                                ## L tail test
                                pv <- (length(which(null[, m] <= tau_dwt[m]))) / (length(na.exclude(null[, m])));
                            }
                        }
                    }
                    out <- c(out, pv);
                    print(pv);
                }
                final_pvalue <- rbind(final_pvalue, out);
                print(out);
            }
        }
    }
    colnames(final_pvalue) <- title;
    write.table(final_pvalue, file = table, sep = "\t", quote = FALSE, row.names = FALSE)
    dev.off();
}

## execute
## read in data
args <- commandArgs(trailingOnly = TRUE)

input_data1 <- NULL;
input_data2 <- NULL;
input_data_short1 <- NULL;
input_data_short2 <- NULL;
input_data_names_short1 <- NULL;
input_data_names_short2 <- NULL;

input_data1 <- read.delim(args[1]);
input_data_short1 <- input_data1[, +c(seq_len(ncol(input_data1)))];
input_data_names_short1 <- colnames(input_data_short1);

input_data2 <- read.delim(args[2]);
input_data_short2 <- input_data2[, +c(seq_len(ncol(input_data2)))];
input_data_names_short2 <- colnames(input_data_short2);

# cor test for motif(a) in input_data1 vs motif(b) in input_data2
dwt_cor(input_data_short1, input_data_names_short1, input_data_short2, input_data_names_short2, test = "cor_aVb_all", pdf = args[3], table = args[4]);
print("done with the correlation test");