diff scripts/estimateprops.R @ 1:be91cb6f48e7 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/music/ commit 683bb72ae92b5759a239b7e3bf4c5a229ed35b54"

--- a/scripts/estimateprops.R	Sun Sep 12 19:49:12 2021 +0000
+++ b/scripts/estimateprops.R	Fri Nov 26 15:55:11 2021 +0000
@@ -14,36 +14,67 @@
     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
 
 ## Show different in estimation methods
 ## Jitter plot of estimated cell type proportions
-jitter.fig <- Jitter_Est(
-    list(data.matrix(est_prop$Est.prop.weighted),
-         data.matrix(est_prop$Est.prop.allgene)),
-    method.name = methods, title = "Jitter plot of Est Proportions")
+jitter_fig <- Jitter_Est(
+    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()
 
 
 ## Make a Plot
 ## A more sophisticated jitter plot is provided as below. We separated
-## the T2D subjects and normal subjects by their HbA1c levels.
-m_prop <- rbind(melt(est_prop$Est.prop.weighted),
-               melt(est_prop$Est.prop.allgene))
+## the T2D subjects and normal subjects by their disease factor levels.
+estimated_music_props_flat <- melt(estimated_music_props)
+estimated_nnls_props_flat <- melt(estimated_nnls_props)
+
+m_prop <- 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)
+}
 
-colnames(m_prop) <- c("Sub", "CellType", "Prop")
+if (phenotype_target_threshold == -99) {
+    phenotype_target_threshold <- -Inf
+    message("phenotype target threshold set to -Inf")
+}
+
+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(phenotype_factors)
+
 
 m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
-m_prop$Method <- factor(rep(methods, each = 89 * 6), levels = methods) # nolint
-m_prop$HbA1c <- rep(bulk_eset$hba1c, 2 * 6) # nolint
-m_prop <- m_prop[!is.na(m_prop$HbA1c), ]
-m_prop$Disease <- factor(sample_groups[(m_prop$HbA1c > 6.5) + 1], # nolint
+m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
+                        levels = methods)
+m_prop$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
-m_prop <- rbind(subset(m_prop, Disease == healthy_phenotype),
-               subset(m_prop, Disease != healthy_phenotype))
+## 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 <- ggplot(m_prop, aes(Method, Prop)) +
+jitter_new <- 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)) +
@@ -52,20 +83,23 @@
     scale_shape_manual(values = c(21, 24)) + theme_minimal()
 
 ## Plot to compare method effectiveness
-## Create dataframe for beta cell proportions and HbA1c levels
-m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:89, 2), phenotype_factors],
-                        ct.prop = c(est_prop$Est.prop.weighted[, 2],
-                                    est_prop$Est.prop.allgene[, 2]),
-                        Method = factor(rep(methods, each = 89),
+## Create dataframe for beta cell proportions and Disease_factor levels
+m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:nrow(estimated_music_props), 2), #nolint
+                                          phenotype_factors],
+                        ct.prop = c(estimated_music_props[, 2],
+                                    estimated_nnls_props[, 2]),
+                        Method = factor(rep(methods,
+                                            each = nrow(estimated_music_props)),
                                         levels = methods))
-colnames(m_prop_ana)[1:4] <- phenotype_factors
-m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_gene]))
+colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
+m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
 m_prop_ana$Disease <- factor(sample_groups[(  # nolint
-    m_prop_ana[phenotype_gene] > 6.5) + 1], sample_groups)
+    m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1],
+    sample_groups)
 m_prop_ana$D <- (m_prop_ana$Disease ==        # nolint
                  sample_disease_group) / sample_disease_group_scale
 
-jitt_compare <- ggplot(m_prop_ana, aes_string(phenotype_gene, "ct.prop")) +
+jitt_compare <- 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) +
@@ -73,21 +107,81 @@
     scale_colour_manual(values = c("white", "gray20")) +
     scale_shape_manual(values = c(21, 24))
 
+## BoxPlot
+plot_box <- Boxplot_Est(list(
+    data.matrix(estimated_music_props),
+    data.matrix(estimated_nnls_props)),
+    method.name = c("MuSiC", "NNLS")) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 8)) +
+    ggtitle(element_blank()) + theme_minimal()
+
+## Heatmap
+plot_hmap <- Prop_heat_Est(list(
+    data.matrix(estimated_music_props),
+    data.matrix(estimated_nnls_props)),
+    method.name = c("MuSiC", "NNLS")) +
+    theme(axis.text.x = element_text(angle = -90),
+          axis.text.y = element_text(size = 6))
 
 pdf(file = outfile_pdf, width = 8, height = 8)
-plot_grid(jitter.fig, jitter.new, labels = "auto", ncol = 1, nrow = 2)
-jitt_compare
-dev.off()
+plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
+plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
+plot_hmap
+message(dev.off())
+
+## Output Proportions
+
+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)
+
 
 ## Summary table
 for (meth in methods) {
     ##lm_beta_meth = lm(ct.prop ~ age + bmi + hba1c + gender, data =
-    ##subset(m_prop_ana, Method == meth))
+    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(phenotype_factors, collapse = " + "),
-              sep = " ~ ")),
-        data = subset(m_prop_ana, Method == meth))
-    print(paste0("Summary: ", meth))
+        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_", meth, ".txt"))
+                   file = paste0("report_data/summ_Log of ",
+                                 meth,
+                                 " fitting.txt"))
 }