diff scripts/estimateprops.R @ 0:22232092be53 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit d007ae51743e621dc47524f681501e72ef3a2910"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/estimateprops.R	Mon May 02 09:59:18 2022 +0000
@@ -0,0 +1,250 @@
+suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
+suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
+suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
+suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
+## We use this script to estimate the effectiveness of proportion methods
+
+## Load Conf
+args <- commandArgs(trailingOnly = TRUE)
+source(args[1])
+
+## Estimate cell type proportions
+est_prop <- music_prop(
+    bulk.eset = bulk_eset, sc.eset = scrna_eset,
+    clusters = celltypes_label,
+    samples = samples_label, select.ct = celltypes, verbose = T)
+
+
+estimated_music_props <- est_prop$Est.prop.weighted
+estimated_nnls_props <- est_prop$Est.prop.allgene
+##
+estimated_music_props_flat <- melt(estimated_music_props)
+estimated_nnls_props_flat <- melt(estimated_nnls_props)
+
+scale_yaxes <- function(gplot, value) {
+    if (is.na(value)) {
+        gplot
+    } else {
+        gplot + scale_y_continuous(lim = c(0, value))
+    }
+}
+
+sieve_data <- function(func, music_data, nnls_data) {
+    if (func == "list") {
+        res <- list(if ("MuSiC" %in% methods) music_data else NULL,
+                    if ("NNLS" %in% methods) nnls_data else NULL)
+        res[lengths(res) > 0] ## filter out NULL elements
+    } else if (func == "rbind") {
+        rbind(if ("MuSiC" %in% methods) music_data else NULL,
+              if ("NNLS" %in% methods) nnls_data else NULL)
+    } else if (func == "c") {
+        c(if ("MuSiC" %in% methods) music_data else NULL,
+          if ("NNLS" %in% methods) nnls_data else NULL)
+    }
+}
+
+
+## Show different in estimation methods
+## Jitter plot of estimated cell type proportions
+jitter_fig <- scale_yaxes(Jitter_Est(
+    sieve_data("list",
+               data.matrix(estimated_music_props),
+               data.matrix(estimated_nnls_props)),
+    method.name = methods, title = "Jitter plot of Est Proportions",
+    size = 2, alpha = 0.7) + theme_minimal(), maxyscale)
+
+## Make a Plot
+## A more sophisticated jitter plot is provided as below. We separated
+## the T2D subjects and normal subjects by their disease factor levels.
+m_prop <- sieve_data("rbind",
+                     estimated_music_props_flat,
+                     estimated_nnls_props_flat)
+colnames(m_prop) <- c("Sub", "CellType", "Prop")
+
+if (is.null(celltypes)) {
+    celltypes <- levels(m_prop$CellType)
+    message("No celltypes declared, using:")
+    message(celltypes)
+}
+
+if (is.null(phenotype_factors)) {
+    phenotype_factors <- colnames(pData(bulk_eset))
+}
+## filter out unwanted factors like "sampleID" and "subjectName"
+phenotype_factors <- phenotype_factors[
+    !(phenotype_factors %in% phenotype_factors_always_exclude)]
+message("Phenotype Factors to use:")
+message(paste0(phenotype_factors, collapse = ", "))
+
+m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
+m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
+                        levels = methods)
+
+if (use_disease_factor) {
+
+    if (phenotype_target_threshold == -99) {
+        phenotype_target_threshold <- -Inf
+        message("phenotype target threshold set to -Inf")
+    }
+    ## the "2" here is to do with the sample groups, not number of methods
+    m_prop$Disease_factor <- rep(bulk_eset[[phenotype_target]], 2 * length(celltypes)) # nolint
+    m_prop <- m_prop[!is.na(m_prop$Disease_factor), ]
+    ## Generate a TRUE/FALSE table of Normal == 1 and Disease == 2
+    sample_groups <- c("Normal", sample_disease_group)
+    m_prop$Disease <- factor(sample_groups[(m_prop$Disease_factor > phenotype_target_threshold) + 1], # nolint
+                             levels = sample_groups)
+
+    ## Binary to scale: e.g. TRUE / 5 = 0.2
+    m_prop$D <- (m_prop$Disease ==   # nolint
+                 sample_disease_group) / sample_disease_group_scale
+    ## NA's are not included in the comparison below
+    m_prop <- rbind(subset(m_prop, Disease != sample_disease_group),
+                    subset(m_prop, Disease == sample_disease_group))
+
+    jitter_new <- scale_yaxes(
+        ggplot(m_prop, aes(Method, Prop)) +
+        geom_point(aes(fill = Method, color = Disease,
+                       stroke = D, shape = Disease),
+                   size = 2, alpha = 0.7,
+                   position = position_jitter(width = 0.25, height = 0)) +
+        facet_wrap(~ CellType, scales = "free") +
+        scale_colour_manual(values = c("white", "gray20")) +
+        scale_shape_manual(values = c(21, 24)) + theme_minimal(), maxyscale)
+
+}
+
+if (use_disease_factor) {
+
+    ## Plot to compare method effectiveness
+    ## Create dataframe for beta cell proportions and Disease_factor levels
+    ## - Ugly code. Essentially, doubles the cell type proportions for each
+    ##   set of MuSiC and NNLS methods
+    m_prop_ana <- data.frame(
+        pData(bulk_eset)[rep(1:nrow(estimated_music_props), length(methods)), #nolint
+                         phenotype_factors],
+        ## get proportions of target cell type
+        ct.prop = sieve_data("c",
+                             estimated_music_props[, phenotype_scrna_target],
+                             estimated_nnls_props[, phenotype_scrna_target]),
+        ##
+        Method = factor(rep(methods,
+                            each = nrow(estimated_music_props)),
+                        levels = methods))
+    ## - fix headers
+    colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
+    ## - drop NA for target phenotype (e.g. hba1c)
+    m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
+    m_prop_ana$Disease <- factor(   # nolint
+        ## - Here we set Normal/Disease assignments across the methods
+        sample_groups[(
+            m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1
+            ],
+        sample_groups)
+    ## - Then we scale this binary assignment to a plotable factor
+    m_prop_ana$D <- (m_prop_ana$Disease ==        # nolint
+                     sample_disease_group) / sample_disease_group_scale
+
+    jitt_compare <- scale_yaxes(
+        ggplot(m_prop_ana, aes_string(phenotype_target, "ct.prop")) +
+        geom_smooth(method = "lm",  se = FALSE, col = "black", lwd = 0.25) +
+        geom_point(aes(fill = Method, color = Disease,
+                       stroke = D, shape = Disease),
+                   size = 2, alpha = 0.7) +  facet_wrap(~ Method) +
+        ggtitle(paste0(toupper(phenotype_target), " vs. ",
+                       toupper(phenotype_scrna_target),
+                       " Cell Type Proportion")) +
+        theme_minimal() +
+        ylab(paste0("Proportion of ",
+                    phenotype_scrna_target, " cells")) +
+        xlab(paste0("Level of bulk factor (", phenotype_target, ")")) +
+        scale_colour_manual(values = c("white", "gray20")) +
+        scale_shape_manual(values = c(21, 24)), maxyscale)
+}
+
+## BoxPlot
+plot_box <- scale_yaxes(Boxplot_Est(
+    sieve_data("list",
+               data.matrix(estimated_music_props),
+               data.matrix(estimated_nnls_props)),
+    method.name = methods) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 8)) +
+    ggtitle(element_blank()) + theme_minimal(), maxyscale)
+
+## Heatmap
+plot_hmap <- Prop_heat_Est(
+    sieve_data(
+        "list",
+        data.matrix(estimated_music_props),
+        data.matrix(estimated_nnls_props)),
+    method.name = methods) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 6))
+
+pdf(file = outfile_pdf, width = 8, height = 8)
+if (length(celltypes) <= 8) {
+    plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
+} else {
+    print(jitter_fig)
+    plot_box
+}
+if (use_disease_factor) {
+    plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
+}
+plot_hmap
+message(dev.off())
+
+writable <- function(obj, prefix, title) {
+    write.table(obj,
+                file = paste0("report_data/", prefix, "_",
+                              title, ".tabular"),
+                quote = F, sep = "\t", col.names = NA)
+}
+
+## Output Proportions
+if ("NNLS" %in% methods) {
+    writable(est_prop$Est.prop.allgene, "prop",
+             "NNLS Estimated Proportions of Cell Types")
+}
+
+if ("MuSiC" %in% methods) {
+    writable(est_prop$Est.prop.weighted, "prop",
+             "Music Estimated Proportions of Cell Types")
+    writable(est_prop$Weight.gene, "weightgene",
+             "Music Estimated Proportions of Cell Types (by Gene)")
+    writable(est_prop$r.squared.full, "rsquared",
+             "Music R-sqr Estimated Proportions of Each Subject")
+    writable(est_prop$Var.prop, "varprop",
+             "Matrix of Variance of MuSiC Estimates")
+}
+
+
+if (use_disease_factor) {
+    ## Summary table of linear regressions of disease factors
+    for (meth in methods) {
+        ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
+        sub_data <- subset(m_prop_ana, Method == meth)
+
+        ## We can only do regression where there are more than 1 factors
+        ## so we must find and exclude the ones which are not
+        gt1_facts <- sapply(phenotype_factors, function(facname) {
+            return(length(unique(sort(sub_data[[facname]]))) == 1)
+        })
+        form_factors <- phenotype_factors
+        exclude_facts <- names(gt1_facts)[gt1_facts]
+        if (length(exclude_facts) > 0) {
+            message("Factors with only one level will be excluded:")
+            message(exclude_facts)
+            form_factors <- phenotype_factors[
+                !(phenotype_factors %in% exclude_facts)]
+        }
+        lm_beta_meth <- lm(as.formula(
+            paste("ct.prop", paste(form_factors, collapse = " + "),
+                  sep = " ~ ")), data = sub_data)
+        message(paste0("Summary: ", meth))
+        capture.output(summary(lm_beta_meth),
+                       file = paste0("report_data/summ_Log of ",
+                                     meth,
+                                     " fitting.txt"))
+    }
+}