| Next changeset 1:75214276e2b7 (2024-06-14) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/aldex2 commit b99f09cf03f075a6881d192b0f1233233289fa60 |
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added:
aldex2.R aldex2.xml macros.xml test-data/reads.tabular |
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| 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 |
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| b'@@ -0,0 +1,150 @@\n+#!/usr/bin/env Rscript\n+\n+suppressPackageStartupMessages(library("ALDEx2"))\n+suppressPackageStartupMessages(library("data.table"))\n+suppressPackageStartupMessages(library("qgraph"))\n+suppressPackageStartupMessages(library("optparse"))\n+\n+option_list <- list(\n+ make_option(c("--aldex_test"), action = "store", dest = "aldex_test", default = NULL, help = "Indicates which analysis to perform"),\n+ make_option(c("--analysis_type"), action = "store", dest = "analysis_type", help = "Indicates which analysis to perform"),\n+ make_option(c("--cutoff_effect"), action = "store", dest = "cutoff_effect", type = "integer", default = NULL, help = "Effect size cutoff for plotting"),\n+ make_option(c("--cutoff_pval"), action = "store", dest = "cutoff_pval", type = "double", default = NULL, help = "Benjamini-Hochberg fdr cutoff"),\n+ make_option(c("--denom"), action = "store", dest = "denom", help = "Indicates which features to retain as the denominator for the Geometric Mean calculation"),\n+ make_option(c("--effect"), action = "store", dest = "effect", default = "false", help = "Calculate abundances and effect sizes"),\n+ make_option(c("--feature_name"), action = "store", dest = "feature_name", default = NULL, help = "Name of the feature from the input data to be plotted"),\n+ make_option(c("--group_names"), action = "store", dest = "group_names", help = "Group names vector"),\n+ make_option(c("--group_nums"), action = "store", dest = "group_nums", default = NULL, help = "Group number for continuous numeric vector"),\n+ 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"),\n+ make_option(c("--include_sample_summary"), action = "store", dest = "include_sample_summary", default = "false", help = "Include median clr values for each sample"),\n+ make_option(c("--iterate"), action = "store", dest = "iterate", default = "false", help = "Indicates whether to iteratively perform a test"),\n+ make_option(c("--num_cols"), action = "store", dest = "num_cols", help = "Number of columns in group vector"),\n+ 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"),\n+ make_option(c("--num_mc_samples"), action = "store", dest = "num_mc_samples", type = "integer", help = "Number of Monte Carlo samples"),\n+ make_option(c("--output"), action = "store", dest = "output", help = "output file"),\n+ make_option(c("--paired_test"), action = "store", dest = "paired_test", default = "false", help = "Indicates whether to do paired-sample tests"),\n+ make_option(c("--plot_test"), action = "store", dest = "plot_test", default = NULL, help = "The method of calculating significance"),\n+ make_option(c("--plot_type"), action = "store", dest = "plot_type", default = NULL, help = "The type of plot to be produced"),\n+ make_option(c("--reads"), action = "store", dest = "reads", help = "Input reads table"),\n+ make_option(c("--xlab"), action = "store", dest = "xlab", default = NULL, help = "x lable for the plot"),\n+ make_option(c("--ylab"), action = "store", dest = "ylab", default = NULL, help = "y lable for the plot")\n+)\n+\n+parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)\n+args <- parse_args(parser, positional_arguments = TRUE)\n+opt <- args$options\n+\n+get_boolean_value <- function(val) {\n+ if (val == "true") {\n+ return(TRUE)\n+ } else {\n+ return(FALSE)\n+ }\n+}\n+\n+# Read the input reads file into a data frame.\n+reads_df <- read.table(file = opt$reads, header = TRUE, sep = "\\t", row.names = 1, dec = ".", as.is = FALSE)\n+\n+# Split the group_names and num_cols into lists of strings.\n+group_names_str <- as.character(opt$group_names)\n+group_names_list <- strsplit(group_names_str, ",")[[1]]\n+num_cols_st'..b'pt$iterate)\n+\n+if (opt$analysis_type == "aldex") {\n+ aldex_obj <- aldex(reads = reads_df,\n+ conditions_vector,\n+ mc.samples = opt$num_mc_samples,\n+ test = opt$aldex_test,\n+ effect = effect,\n+ include.sample.summary = include_sample_summary,\n+ denom = opt$denom,\n+ iterate = iterate)\n+} else {\n+ # Generate Monte Carlo samples of the Dirichlet distribution for each sample. Convert each\n+ # instance using a log-ratio transform. This is the input for all further analyses.\n+ aldex_clr_obj <- aldex.clr(reads_df, conditions_vector, mc.samples = opt$num_mc_samples, denom = opt$denom)\n+\n+ if (opt$analysis_type == "aldex_corr") {\n+ if (!is.null(opt$cont_var)) {\n+ # Read the input cont_var vector.\n+ cont_var <- as.numeric(read.table(file = opt$cont_var, header = FALSE, sep = "\\t"))\n+ }\n+\n+ # Split the group_names and num_cols into lists of strings.\n+ group_nums_str <- as.character(opt$group_nums)\n+ group_nums_list <- strsplit(group_nums_str, ",")[[1]]\n+ num_cols_in_groups_str <- as.character(opt$num_cols_in_groups)\n+ num_cols_in_groups_list <- strsplit(num_cols_in_groups_str, ",")[[1]]\n+ # Construct continuous numeric vector.\n+ cont_var_vector <- c()\n+ for (i in seq_along(num_cols_in_groups_list)) {\n+ num_cols_in_group <- as.integer(num_cols_in_groups_list[i])\n+ group_num <- group_nums_list[i]\n+ for (j in 1:num_cols_in_group) {\n+ cont_var_vector <- cbind(cont_var_vector, group_num)\n+ }\n+ }\n+ # The cont_var_vector is now a matrix,\n+ # so coerce it back to a vector.\n+ cont_var_vector <- as.numeric(as.vector(cont_var_vector))\n+\n+ aldex_obj <- aldex.corr(aldex_clr_obj, cont.var = cont_var_vector)\n+ } else if (opt$analysis_type == "aldex_effect") {\n+ aldex_obj <- aldex.effect(aldex_clr_obj, include_sample_summary)\n+ } else if (opt$analysis_type == "aldex_expected_distance") {\n+ dist <- aldex.expectedDistance(aldex_clr_obj)\n+ png(filename = opt$output)\n+ qgraph(dist, layout = "spring", vsize = 1)\n+ dev.off()\n+ } else if (opt$analysis_type == "aldex_kw") {\n+ aldex_obj <- aldex.kw(aldex_clr_obj)\n+ } else if (opt$analysis_type == "aldex_plot") {\n+ aldex_obj <- aldex(reads = reads_df,\n+ conditions_vector,\n+ mc.samples = opt$num_mc_samples,\n+ test = opt$aldex_test,\n+ effect = effect,\n+ include.sample.summary = include_sample_summary,\n+ denom = opt$denom,\n+ iterate = iterate)\n+ png(filename = opt$output)\n+ aldex.plot(x = aldex_obj,\n+ type = opt$plot_type,\n+ test = opt$plot_test,\n+ cutoff.pval = opt$cutoff_pval,\n+ cutoff.effect = opt$cutoff_effect,\n+ xlab = opt$xlab,\n+ ylab = opt$ylab)\n+ dev.off()\n+ } else if (opt$analysis_type == "aldex_plot_feature") {\n+ png(filename = opt$output)\n+ aldex.plotFeature(aldex_clr_obj, opt$feature_name)\n+ dev.off()\n+ } else if (opt$analysis_type == "aldex_ttest") {\n+ paired_test <- get_boolean_value(opt$paired_test)\n+ hist_plot <- get_boolean_value(opt$hist_plot)\n+ aldex_obj <- aldex.ttest(aldex_clr_obj, paired.test = paired_test, hist.plot = hist_plot)\n+ }\n+}\n+if ((opt$analysis_type != "aldex_expected_distance") && (opt$analysis_type != "aldex_plot") && (opt$analysis_type != "aldex_plot_feature")) {\n+ # Output the ALDEx object.\n+ write.table(aldex_obj, file = opt$output, append = FALSE, sep = "\\t", dec = ".", row.names = FALSE, col.names = TRUE)\n+}\n' |
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| 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 |
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| b'@@ -0,0 +1,471 @@\n+<tool id="aldex2" name="ALDEx2" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">\n+ <description>differential abundance analysis</description>\n+ <macros>\n+ <import>macros.xml</import>\n+ </macros>\n+ <expand macro="biotools"/>\n+ <expand macro="requirements"/>\n+ <command detect_errors="exit_code"><![CDATA[\n+## Enable building the conditions_vector.\n+#set group_names = []\n+#set num_cols = []\n+#for $condition_items in $conditions:\n+ $group_names.append(str($condition_items.group_name))\n+ $num_cols.append(str($condition_items.num_cols))\n+#end for\n+#set group_names = \',\'.join($group_names)\n+#set num_cols = \',\'.join($num_cols)\n+\n+#if str($analysis_type_cond.analysis_type) == \'aldex_corr\':\n+ ## Enable building the cond_var (a continuous numeric vector).\n+ #set group_nums = []\n+ #set num_cols_in_groups = []\n+ #for $cont_var_items in $analysis_type_cond.cont_var:\n+ $group_nums.append(str($cont_var_items.group_num))\n+ $num_cols_in_groups.append(str($cont_var_items.num_cols_in_group))\n+ #end for\n+ #set group_nums = \',\'.join($group_nums)\n+ #set num_cols_in_groups = \',\'.join($num_cols_in_groups)\n+#end if\n+\n+Rscript \'${__tool_directory__}/aldex2.R\' \n+--reads \'$reads\' \n+--group_names \'$group_names\'\n+--num_cols \'$num_cols\'\n+--num_mc_samples $num_mc_samples\n+--denom \'$denom\'\n+--analysis_type \'$analysis_type_cond.analysis_type\'\n+#if str($analysis_type_cond.analysis_type) == \'aldex\':\n+ --aldex_test \'$analysis_type_cond.aldex_test\'\n+ --effect \'$analysis_type_cond.effect\'\n+ --include_sample_summary \'$analysis_type_cond.include_sample_summary\'\n+ --iterate \'$analysis_type_cond.iterate\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_corr\':\n+ --group_nums \'$group_nums\'\n+ --num_cols_in_groups \'$num_cols_in_groups\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_effect\':\n+ --include_sample_summary \'$analysis_type_cond.include_sample_summary\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_plot\':\n+ --aldex_test \'$analysis_type_cond.aldex_test\'\n+ --effect \'$analysis_type_cond.effect\'\n+ --include_sample_summary \'$analysis_type_cond.include_sample_summary\'\n+ --iterate \'$analysis_type_cond.iterate\'\n+ --plot_type \'$analysis_type_cond.plot_type\'\n+ --plot_test \'$analysis_type_cond.plot_test\'\n+ --cutoff_pval $analysis_type_cond.cutoff_pval\n+ --cutoff_effect $analysis_type_cond.cutoff_effect\n+ #if str($analysis_type_cond.xlab) != \'\':\n+ --xlab \'$analysis_type_cond.xlab\'\n+ #end if\n+ #if str($analysis_type_cond.ylab) != \'\':\n+ --ylab \'$analysis_type_cond.ylab\'\n+ #end if\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_plot_feature\':\n+ --feature_name \'$analysis_type_cond.feature_name\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_ttest\':\n+ --paired_test \'$analysis_type_cond.paired_test\'\n+ --hist_plot \'$analysis_type_cond.hist_plot\'\n+#end if\n+#if str($analysis_type_cond.analysis_type) == \'aldex\':\n+ --output \'$output_aldex\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_corr\':\n+ --output \'$output_aldex_corr\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_effect\':\n+ --output \'$output_aldex_effect\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_expected_distance\':\n+ --output \'$output_aldex_expected_distance\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_kw\':\n+ --output \'$output_aldex_kw\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_plot\':\n+ --output \'$output_aldex_plot\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_plot_feature\':\n+ --output \'$output_aldex_plot_feature\'\n+#else if str($analysis_type_cond.analysis_type) == \'aldex_ttest\':\n+ --output \'$output_aldex_ttest\'\n+ #if str($analysis_type_cond.hist_plot) == \'true\':\n+ && mv \'Rplots.pdf\' \'$output_aldex_ttest_plot\'\n+ #end if\n+#end if\n+ ]]></command>\n+ <inputs>\n+ <p'..b'e selected Reads table file.\n+ * **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.\n+ * **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. \n+ * **Select the analysis to be performed**\n+\n+ * **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\xe2\x80\x99s t, Wilcoxon) or multi-sample (glm, Kruskal-Wallace) tests. This function also estimates effect size for two sample analyses.\n+\n+ * **Select the tests to be performed** - select the tests to be performed when crearing the ALDEx2 object.\n+ * **Calculate abundances and effect sizes** - applies only if the selected test is Welch\xe2\x80\x99s t and Wilcox tests, or if tests are performed iteratively.\n+ * **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).\n+ * **Perform tests iteratively** - Specify whether to iteratively perform a test. For example, this will use the results from an initial Welch\xe2\x80\x99s t and Wilcox test routine to seed the reference (i.e., denominator of Geometric Mean calculation) for a second Welch\xe2\x80\x99s t and Wilcox test routine.\n+\n+ * **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.\n+ * **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.\n+ * **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.\n+ * **Calculate the Kruskal-Wallis test and glm ANOVA statistics (aldex.kw)** - calculates the expected values of the Kruskal-Wallis test and a glm ANOVA.\n+ * **Plot an ALDEx2 object (aldex.plot)** - generate an MW-plot or an MA-plot of the given ALDEx2 object.\n+ * **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.\n+ * **Calculate Welch\xe2\x80\x99s 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\n+\n+ * **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\xe2\x80\x99s t and Wilcox tests).\n+ * **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.\n+ </help>\n+ <expand macro="citations"/>\n+</tool>\n+\n' |
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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 |
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| @@ -0,0 +1,52 @@ +<macros> + <token name="@TOOL_VERSION@">1.26.0</token> + <token name="@VERSION_SUFFIX@">0</token> + <token name="@PROFILE@">21.01</token> + <xml name="biotools"> + <xrefs> + <xref type="bio.tools">aldex2</xref> + </xrefs> + </xml> + <xml name="requirements"> + <requirements> + <requirement type="package" version="@TOOL_VERSION@">bioconductor-aldex2</requirement> + <requirement type="package" version="1.14.2">r-data.table</requirement> + <requirement type="package" version="1.7.1">r-optparse</requirement> + <requirement type="package" version="1.9.2">r-qgraph</requirement> + </requirements> + </xml> + <xml name="aldex_test_param"> + <param name="aldex_test" type="select" label="Select the tests to be performed"> + <option value="t" selected="true">Welch's t and Wilcoxon tests</option> + <option value="kw">Kruskal-Wallace and glm tests</option> + <option value="corr">Correlation test</option> + </param> + </xml> + <xml name="effect_param"> + <param argument="--effect" type="boolean" truevalue="true" falsevalue="false" checked="true" label="Calculate abundances and effect sizes?" help="Used only for Welch's t and Wilcoxon tests or if performing tests iteratively"/> + </xml> + <xml name="include_sample_summary_param"> + <param argument="--include_sample_summary" type="boolean" truevalue="true" falsevalue="false" checked="false" label="Include median clr values for each sample?"/> + </xml> + <xml name="iterate_param"> + <param argument="--iterate" type="boolean" truevalue="true" falsevalue="false" checked="false" label="Perform tests iteratively?"/> + </xml> + <xml name="sanitize_query" token_validinitial="string.printable"> + <sanitizer> + <valid initial="@VALIDINITIAL@"> + <remove value="'"/> + </valid> + <mapping initial="none"> + <add source="'" target="'"'"'"/> + </mapping> + </sanitizer> + </xml> + <xml name="citations"> + <citations> + <citation type="doi">10.1371/journal.pone.0067019</citation> + <citation type="doi">10.1186/2049-2618-2-15</citation> + <citation type="doi">10.1080/10618600.2015.1131161</citation> + </citations> + </xml> +</macros> + |
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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 |
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| b'@@ -0,0 +1,201 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