# HG changeset patch
# User iuc
# Date 1656488205 0
# Node ID f4d0bd4b4d6d62901804a7e07d19dff52c7f5247
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/aldex2 commit b99f09cf03f075a6881d192b0f1233233289fa60
diff -r 000000000000 -r f4d0bd4b4d6d aldex2.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/aldex2.R Wed Jun 29 07:36:45 2022 +0000
@@ -0,0 +1,150 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages(library("ALDEx2"))
+suppressPackageStartupMessages(library("data.table"))
+suppressPackageStartupMessages(library("qgraph"))
+suppressPackageStartupMessages(library("optparse"))
+
+option_list <- list(
+ make_option(c("--aldex_test"), action = "store", dest = "aldex_test", default = NULL, help = "Indicates which analysis to perform"),
+ make_option(c("--analysis_type"), action = "store", dest = "analysis_type", help = "Indicates which analysis to perform"),
+ make_option(c("--cutoff_effect"), action = "store", dest = "cutoff_effect", type = "integer", default = NULL, help = "Effect size cutoff for plotting"),
+ make_option(c("--cutoff_pval"), action = "store", dest = "cutoff_pval", type = "double", default = NULL, help = "Benjamini-Hochberg fdr cutoff"),
+ make_option(c("--denom"), action = "store", dest = "denom", help = "Indicates which features to retain as the denominator for the Geometric Mean calculation"),
+ make_option(c("--effect"), action = "store", dest = "effect", default = "false", help = "Calculate abundances and effect sizes"),
+ make_option(c("--feature_name"), action = "store", dest = "feature_name", default = NULL, help = "Name of the feature from the input data to be plotted"),
+ make_option(c("--group_names"), action = "store", dest = "group_names", help = "Group names vector"),
+ make_option(c("--group_nums"), action = "store", dest = "group_nums", default = NULL, help = "Group number for continuous numeric vector"),
+ make_option(c("--hist_plot"), action = "store", dest = "hist_plot", default = "false", help = "Indicates whether to plot a histogram of p-values for the first Dirichlet Monte Carlo instance"),
+ make_option(c("--include_sample_summary"), action = "store", dest = "include_sample_summary", default = "false", help = "Include median clr values for each sample"),
+ make_option(c("--iterate"), action = "store", dest = "iterate", default = "false", help = "Indicates whether to iteratively perform a test"),
+ make_option(c("--num_cols"), action = "store", dest = "num_cols", help = "Number of columns in group vector"),
+ make_option(c("--num_cols_in_groups"), action = "store", dest = "num_cols_in_groups", default = NULL, help = "Number of columns in each group dewfining the continuous numeric vector"),
+ make_option(c("--num_mc_samples"), action = "store", dest = "num_mc_samples", type = "integer", help = "Number of Monte Carlo samples"),
+ make_option(c("--output"), action = "store", dest = "output", help = "output file"),
+ make_option(c("--paired_test"), action = "store", dest = "paired_test", default = "false", help = "Indicates whether to do paired-sample tests"),
+ make_option(c("--plot_test"), action = "store", dest = "plot_test", default = NULL, help = "The method of calculating significance"),
+ make_option(c("--plot_type"), action = "store", dest = "plot_type", default = NULL, help = "The type of plot to be produced"),
+ make_option(c("--reads"), action = "store", dest = "reads", help = "Input reads table"),
+ make_option(c("--xlab"), action = "store", dest = "xlab", default = NULL, help = "x lable for the plot"),
+ make_option(c("--ylab"), action = "store", dest = "ylab", default = NULL, help = "y lable for the plot")
+)
+
+parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
+args <- parse_args(parser, positional_arguments = TRUE)
+opt <- args$options
+
+get_boolean_value <- function(val) {
+ if (val == "true") {
+ return(TRUE)
+ } else {
+ return(FALSE)
+ }
+}
+
+# Read the input reads file into a data frame.
+reads_df <- read.table(file = opt$reads, header = TRUE, sep = "\t", row.names = 1, dec = ".", as.is = FALSE)
+
+# Split the group_names and num_cols into lists of strings.
+group_names_str <- as.character(opt$group_names)
+group_names_list <- strsplit(group_names_str, ",")[[1]]
+num_cols_str <- as.character(opt$num_cols)
+num_cols_list <- strsplit(num_cols_str, ",")[[1]]
+# Construct conditions vector.
+conditions_vector <- c()
+for (i in seq_along(num_cols_list)) {
+ num_cols <- as.integer(num_cols_list[i])
+ group_name <- group_names_list[i]
+ for (j in 1:num_cols) {
+ conditions_vector <- cbind(conditions_vector, group_name)
+ }
+}
+# The conditions_vector is now a matrix,
+# so coerce it back to a vector.
+conditions_vector <- as.vector(conditions_vector)
+
+# Convert boolean values to boolean.
+effect <- get_boolean_value(opt$effect)
+include_sample_summary <- get_boolean_value(opt$include_sample_summary)
+iterate <- get_boolean_value(opt$iterate)
+
+if (opt$analysis_type == "aldex") {
+ aldex_obj <- aldex(reads = reads_df,
+ conditions_vector,
+ mc.samples = opt$num_mc_samples,
+ test = opt$aldex_test,
+ effect = effect,
+ include.sample.summary = include_sample_summary,
+ denom = opt$denom,
+ iterate = iterate)
+} else {
+ # Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each
+ # instance using a log-ratio transform. This is the input for all further analyses.
+ aldex_clr_obj <- aldex.clr(reads_df, conditions_vector, mc.samples = opt$num_mc_samples, denom = opt$denom)
+
+ if (opt$analysis_type == "aldex_corr") {
+ if (!is.null(opt$cont_var)) {
+ # Read the input cont_var vector.
+ cont_var <- as.numeric(read.table(file = opt$cont_var, header = FALSE, sep = "\t"))
+ }
+
+ # Split the group_names and num_cols into lists of strings.
+ group_nums_str <- as.character(opt$group_nums)
+ group_nums_list <- strsplit(group_nums_str, ",")[[1]]
+ num_cols_in_groups_str <- as.character(opt$num_cols_in_groups)
+ num_cols_in_groups_list <- strsplit(num_cols_in_groups_str, ",")[[1]]
+ # Construct continuous numeric vector.
+ cont_var_vector <- c()
+ for (i in seq_along(num_cols_in_groups_list)) {
+ num_cols_in_group <- as.integer(num_cols_in_groups_list[i])
+ group_num <- group_nums_list[i]
+ for (j in 1:num_cols_in_group) {
+ cont_var_vector <- cbind(cont_var_vector, group_num)
+ }
+ }
+ # The cont_var_vector is now a matrix,
+ # so coerce it back to a vector.
+ cont_var_vector <- as.numeric(as.vector(cont_var_vector))
+
+ aldex_obj <- aldex.corr(aldex_clr_obj, cont.var = cont_var_vector)
+ } else if (opt$analysis_type == "aldex_effect") {
+ aldex_obj <- aldex.effect(aldex_clr_obj, include_sample_summary)
+ } else if (opt$analysis_type == "aldex_expected_distance") {
+ dist <- aldex.expectedDistance(aldex_clr_obj)
+ png(filename = opt$output)
+ qgraph(dist, layout = "spring", vsize = 1)
+ dev.off()
+ } else if (opt$analysis_type == "aldex_kw") {
+ aldex_obj <- aldex.kw(aldex_clr_obj)
+ } else if (opt$analysis_type == "aldex_plot") {
+ aldex_obj <- aldex(reads = reads_df,
+ conditions_vector,
+ mc.samples = opt$num_mc_samples,
+ test = opt$aldex_test,
+ effect = effect,
+ include.sample.summary = include_sample_summary,
+ denom = opt$denom,
+ iterate = iterate)
+ png(filename = opt$output)
+ aldex.plot(x = aldex_obj,
+ type = opt$plot_type,
+ test = opt$plot_test,
+ cutoff.pval = opt$cutoff_pval,
+ cutoff.effect = opt$cutoff_effect,
+ xlab = opt$xlab,
+ ylab = opt$ylab)
+ dev.off()
+ } else if (opt$analysis_type == "aldex_plot_feature") {
+ png(filename = opt$output)
+ aldex.plotFeature(aldex_clr_obj, opt$feature_name)
+ dev.off()
+ } else if (opt$analysis_type == "aldex_ttest") {
+ paired_test <- get_boolean_value(opt$paired_test)
+ hist_plot <- get_boolean_value(opt$hist_plot)
+ aldex_obj <- aldex.ttest(aldex_clr_obj, paired.test = paired_test, hist.plot = hist_plot)
+ }
+}
+if ((opt$analysis_type != "aldex_expected_distance") && (opt$analysis_type != "aldex_plot") && (opt$analysis_type != "aldex_plot_feature")) {
+ # Output the ALDEx object.
+ write.table(aldex_obj, file = opt$output, append = FALSE, sep = "\t", dec = ".", row.names = FALSE, col.names = TRUE)
+}
diff -r 000000000000 -r f4d0bd4b4d6d aldex2.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/aldex2.xml Wed Jun 29 07:36:45 2022 +0000
@@ -0,0 +1,471 @@
+
+ differential abundance analysis
+
+ macros.xml
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+ analysis_type_cond['analysis_type'] == 'aldex'
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+ analysis_type_cond['analysis_type'] == 'aldex_corr'
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+ analysis_type_cond['analysis_type'] == 'aldex_effect'
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+ analysis_type_cond['analysis_type'] == 'aldex_expected_distance'
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+ analysis_type_cond['analysis_type'] == 'aldex_kw'
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+ analysis_type_cond['analysis_type'] == 'aldex_plot'
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+ analysis_type_cond['analysis_type'] == 'aldex_plot_feature'
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+ analysis_type_cond['analysis_type'] == 'aldex_ttest'
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+ analysis_type_cond['analysis_type'] == 'aldex_ttest' and analysis_type_cond['hist_plot'] == 'true'
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+**What it does**
+
+Performs a differential abundance analysis for the comparison of two or more conditions (e.g., single-organism
+and meta-RNA-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence
+selections). This tool generates Monte Carlo samples of the Dirichlet distribution for each sample, converts
+each instance using a log-ratio transform, then returns test results for two sample (Welch’s t, Wilcoxon) or
+multi-sample (glm, Kruskal-Wallace) tests. The tool also estimates effect size for two sample analyses.
+
+**Options**
+
+ * **Reads table file** - a tabular file with unique names for all rows and columns. Rows should contain genes and columns should contain sequencing read counts (i.e., sample vectors). Rows with 0 reads in each sample are deleted prior to analysis.
+ * **Define the comparison groups describing the reads data structure** - Specify a group label and the number of sequencing read count columns associated with it. The total number of columns must be equal to the number of numeric columns in the selected Reads table file.
+ * **Number of Monte Carlo samples to use** - the number of Monte Carlo samples to use when estimating the underlying distributions. Since we are estimating central tendencies, 128 is usually sufficient.
+ * **Select features to retain as the denominator for the Geometric Mean calculation** - indicates which features to retain as the denominator for the Geometric Mean calculation.
+ * **Select the analysis to be performed**
+
+ * **Compute an ALDEx2 object (aldex)** - performs log-ratio transformation and statistical testing. Specifically, this function: (a) generates Monte Carlo samples of the Dirichlet distribution for each sample, (b) converts each instance using a log-ratio transform, then (c) retcalculates the expected values for the correlation between each feature and a contin- uous variable, using data returned returned by aldex.clr and a vector of the continuous variable. Returns results of Pearson, Spearman and Kendall tests.urns test results for two sample (Welch’s t, Wilcoxon) or multi-sample (glm, Kruskal-Wallace) tests. This function also estimates effect size for two sample analyses.
+
+ * **Select the tests to be performed** - select the tests to be performed when crearing the ALDEx2 object.
+ * **Calculate abundances and effect sizes** - applies only if the selected test is Welch’s t and Wilcox tests, or if tests are performed iteratively.
+ * **Include median clr values for each sample** - specify whether to include median clr values for each sample (applies only if abundances and effect sizes are calculated).
+ * **Perform tests iteratively** - Specify whether to iteratively perform a test. For example, this will use the results from an initial Welch’s t and Wilcox test routine to seed the reference (i.e., denominator of Geometric Mean calculation) for a second Welch’s t and Wilcox test routine.
+
+ * **Calculate correlation with a continuous variable (aldex.corr)** - calculates the expected values for the correlation between each feature and a continuous variable using data returned by aldex.clr and a vector of the continuous variable. Returns results of Pearson, Spearman and Kendall tests.
+ * **Calculate effect sizes and differences between conditions (aldex.effect)** - calculates (1) the median clr abundances per sample, per condition, (2) the median differences in abundance between 2 conditions and (3) the median effect size and proportion of effect that overlaps 0.
+ * **Calculate the expected values of distances between samples (aldex.expectedDistance)** - calculates the expected value of distances between samples using the median value of distances derived from N Monte-Carlo replicates.
+ * **Calculate the Kruskal-Wallis test and glm ANOVA statistics (aldex.kw)** - calculates the expected values of the Kruskal-Wallis test and a glm ANOVA.
+ * **Plot an ALDEx2 object (aldex.plot)** - generate an MW-plot or an MA-plot of the given ALDEx2 object.
+ * **Show dispersion of the expected values returned by aldex.effect (aldex.plotFeature)** - generates density plots showing the dispersion of the expected values given in the output from aldex.effect. The expected values are shown in the plots. This is a diagnostic visualization to help determine if the expected values are trustworthy.
+ * **Calculate Welch’s t-test and Wilcoxon test statistics (aldex.ttest)** - calculate the expected values of the Wilcoxon Rank Sum test and Welch's t-test on the data returned by aldex.clr
+
+ * **Calculate paired tests** - specify whether to calculate effect size for paired samples (applies only if abundances and effect sizes are calculated and the selected test is Welch’s t and Wilcox tests).
+ * **Plot a histogram of p-values for the first Dirichlet Monte Carlo instance** - specify whether to plot a histogram of p-values for the first Dirichlet Monte Carlo instance.
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diff -r 000000000000 -r f4d0bd4b4d6d macros.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/macros.xml Wed Jun 29 07:36:45 2022 +0000
@@ -0,0 +1,52 @@
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+ 1.26.0
+ 0
+ 21.01
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+ aldex2
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+ bioconductor-aldex2
+ r-data.table
+ r-optparse
+ r-qgraph
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+ 10.1371/journal.pone.0067019
+ 10.1186/2049-2618-2-15
+ 10.1080/10618600.2015.1131161
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diff -r 000000000000 -r f4d0bd4b4d6d test-data/reads.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/reads.tabular Wed Jun 29 07:36:45 2022 +0000
@@ -0,0 +1,201 @@
+ 1_ANS 1_BNS 1_CNS 1_DNS 2_ANS 2_CNS 2_DNS 1_AS 1_BS 1_CS 1_DS 2_AS 2_CS 2_DS
+A:D:A:D 347 271 396 317 391 260 620 8 1 1 2 0 1 3
+A:D:A:E 436 361 461 241 410 387 788 83 6 8 12 0 3 2
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+A:E:A:E 513 307 424 381 499 447 709 10547 12033 5394 15998 293 1816 33
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+A:E:C:E 238 177 204 163 242 255 561 25 48 145 46 81 30 7
+A:D:D:D 192 140 236 207 181 182 720 0 1 0 1 0 0 1
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+A:D:M:D 73 125 117 55 91 97 167 1 0 0 0 1 0 0
+A:D:M:E 88 68 111 51 101 99 128 1 0 0 18 0 0 0
+A:E:M:D 78 94 93 39 143 114 213 9 0 46 2 1 0 0
+A:E:M:E 103 75 106 65 104 114 116 9 8 10 6719 0 0 2
+A:D:N:D 87 83 115 46 83 84 174 0 0 0 0 0 0 0
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+A:E:N:D 112 118 187 140 150 129 227 0 0 0 1 0 1 1
+A:E:N:E 101 85 108 65 157 132 254 0 1 0 1 1 0 2
+A:D:P:D 324 261 200 239 295 293 982 0 0 0 0 12 1 1
+A:D:P:E 291 192 290 176 355 300 701 269 0 0 0 2 0 0
+A:E:P:D 415 267 343 211 376 341 461 0 4 2 14 2 0 2
+A:E:P:E 393 257 471 215 352 352 935 142 2 2 3 0 0 2
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+A:E:Q:E 210 157 267 143 191 194 400 0 1 1 0 0 2 1
+A:D:R:D 587 404 532 511 606 539 1088 0 1 0 1 1 0 2
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+A:E:R:D 688 502 588 444 581 605 1316 82 16 294 14 31 27 16
+A:E:R:E 743 501 559 510 636 737 1916 57 44 50 52 47 83 6
+A:D:S:D 425 303 405 369 507 420 953 1 1 0 0 0 0 1
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+A:D:V:E 378 320 366 325 526 360 1143 0 0 1 2 2 0 1
+A:E:V:D 501 333 482 493 485 434 1205 36 42 94 18 49 15 65
+A:E:V:E 573 343 552 426 537 484 914 63 51 93 72 61 54 2835
+A:D:W:D 108 85 111 113 90 79 186 0 0 0 0 0 0 1
+A:D:W:E 88 85 87 51 110 92 260 3 0 0 0 0 0 0
+A:E:W:D 129 122 101 140 142 133 332 49 66 139 9 73 50 15
+A:E:W:E 149 144 111 99 115 191 134 254 65 83 52 106 119 0
+A:D:Y:D 136 91 159 93 93 102 385 0 0 0 0 0 0 1
+A:D:Y:E 113 79 142 83 146 123 671 0 0 0 0 0 0 2
+A:E:Y:D 165 108 171 99 153 124 533 0 0 0 0 5 0 1
+A:E:Y:E 222 148 246 139 164 137 449 0 2 2 2 3 2 0
+C:D:A:D 217 117 164 91 189 126 170 1 1 0 0 2 0 1
+C:D:A:E 164 138 169 80 165 184 293 0 0 8 0 0 0 1
+C:E:A:D 212 146 210 86 200 187 307 0 1 0 0 0 0 11
+C:E:A:E 180 124 152 89 252 191 397 2 5 5 11 4 1 0
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+C:D:D:D 89 54 83 48 128 107 211 0 0 0 0 0 0 0
+C:D:D:E 83 46 64 32 67 56 92 0 0 0 0 0 0 0
+C:E:D:D 76 71 120 104 81 90 114 0 0 7 0 0 0 0
+C:E:D:E 72 79 86 63 152 99 148 0 0 0 0 0 0 0
+C:D:E:D 110 40 93 39 79 75 96 0 0 0 0 0 0 1
+C:D:E:E 98 64 66 26 89 99 62 0 0 0 0 0 0 0
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+C:E:F:D 80 44 61 37 68 80 87 0 0 0 0 1 0 0
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