--- a/scripts/estimateprops.R	Tue Nov 30 13:07:36 2021 +0000
+++ b/scripts/estimateprops.R	Sat Jan 29 12:51:49 2022 +0000
@@ -17,13 +17,21 @@
 estimated_music_props <- est_prop$Est.prop.weighted
 estimated_nnls_props <- est_prop$Est.prop.allgene
 
+scale_yaxes <- function(gplot, value) {
+    if (is.na(value)) {
+        gplot
+    } else {
+        gplot + scale_y_continuous(lim = c(0, value))
+    }
+}
+
 ## Show different in estimation methods
 ## Jitter plot of estimated cell type proportions
-jitter_fig <- Jitter_Est(
+jitter_fig <- scale_yaxes(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()
+    size = 2, alpha = 0.7) + theme_minimal(), maxyscale)
 
 
 ## Make a Plot
@@ -42,11 +50,6 @@
     message(celltypes)
 }
 
-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))
 }
@@ -54,67 +57,94 @@
 phenotype_factors <- phenotype_factors[
     !(phenotype_factors %in% phenotype_factors_always_exclude)]
 message("Phenotype Factors to use:")
-message(phenotype_factors)
-
+message(paste0(phenotype_factors, collapse = ", "))
 
 m_prop$CellType <- factor(m_prop$CellType, levels = celltypes) # nolint
 m_prop$Method <- factor(rep(methods, each = nrow(estimated_music_props_flat)), # nolint
                         levels = methods)
-m_prop$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$Disease_factor > phenotype_target_threshold) + 1], # nolint
-                         levels = sample_groups)
+
+if (use_disease_factor) {
+
+    if (phenotype_target_threshold == -99) {
+        phenotype_target_threshold <- -Inf
+        message("phenotype target threshold set to -Inf")
+    }
+
+    m_prop$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))
+    ## 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 <- 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()
+    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
-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:length(phenotype_factors)] <- phenotype_factors #nolint
-m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
-m_prop_ana$Disease <- factor(sample_groups[(  # nolint
-    m_prop_ana[phenotype_target] > phenotype_target_threshold) + 1],
-    sample_groups)
-m_prop_ana$D <- (m_prop_ana$Disease ==        # nolint
-                 sample_disease_group) / sample_disease_group_scale
+    ## Plot to compare method effectiveness
+    ## Create dataframe for beta cell proportions and Disease_factor levels
+    ## - Ugly code. Essentially, doubles the cell type proportions for each
+    ##   set of MuSiC and NNLS methods
+    m_prop_ana <- data.frame(pData(bulk_eset)[rep(1:nrow(estimated_music_props), 2), #nolint
+                                              phenotype_factors],
+                             ## get proportions of target cell type
+                             ct.prop = c(estimated_music_props[, phenotype_scrna_target],
+                                         estimated_nnls_props[, phenotype_scrna_target]),
+                             ##
+                             Method = factor(rep(methods,
+                                                 each = nrow(estimated_music_props)),
+                                             levels = methods))
+    ## - fix headers
+    colnames(m_prop_ana)[1:length(phenotype_factors)] <- phenotype_factors #nolint
+    ## - drop NA for target phenotype (e.g. hba1c)
+    m_prop_ana <- subset(m_prop_ana, !is.na(m_prop_ana[phenotype_target]))
+    m_prop_ana$Disease <- factor(   # nolint
+        ## - Here we set Normal/Disease assignments across the two MuSiC and NNLS 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 <- 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(compare_title) + theme_minimal() +
-    scale_colour_manual(values = c("white", "gray20")) +
-    scale_shape_manual(values = c(21, 24))
+    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 <- Boxplot_Est(list(
+plot_box <- scale_yaxes(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()
+    ggtitle(element_blank()) + theme_minimal(), maxyscale)
 
 ## Heatmap
 plot_hmap <- Prop_heat_Est(list(
@@ -125,8 +155,15 @@
           axis.text.y = element_text(size = 6))
 
 pdf(file = outfile_pdf, width = 8, height = 8)
-plot_grid(jitter_fig, plot_box, labels = "auto", ncol = 1, nrow = 2)
-plot_grid(jitter_new, jitt_compare, labels = "auto", ncol = 1, nrow = 2)
+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())
 
@@ -159,29 +196,32 @@
             quote = F, sep = "\t", col.names = NA)
 
 
-## Summary table
-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)]
+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"))
     }
-    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"))
 }