--- a/scripts/estimateprops.R.orig	Tue Oct 29 13:40:06 2024 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,281 +0,0 @@
-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")
-}
-
-
-<<<<<<< HEAD
-=======
-write.table(est_prop$Est.prop.weighted,
-            file = paste0("report_data/prop_",
-                          "Music Estimated Proportions of Cell Types",
-                          ".tabular"),
-            quote = F, sep = "\t", col.names = NA)
-write.table(est_prop$Est.prop.allgene,
-            file = paste0("report_data/prop_",
-                          "NNLS Estimated Proportions of Cell Types",
-                          ".tabular"),
-            quote = F, sep = "\t", col.names = NA)
-write.table(est_prop$Weight.gene,
-            file = paste0("report_data/weightgene_",
-                          "Music Estimated Proportions of Cell Types (by Gene)",
-                          ".tabular"),
-            quote = F, sep = "\t", col.names = NA)
-write.table(est_prop$r.squared.full,
-            file = paste0("report_data/rsquared_",
-                          "Music R-sqr Estimated Proportions of Each Subject",
-                          ".tabular"),
-            quote = F, sep = "\t", col.names = NA)
-write.table(est_prop$Var.prop,
-            file = paste0("report_data/varprop_",
-                          "Matrix of Variance of MuSiC Estimates",
-                          ".tabular"),
-            quote = F, sep = "\t", col.names = NA)
-
-
->>>>>>> 7a416140 (fitting summaries only apply when disease factor is used)
-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"))
-    }
-}
-