Setup knitr and load utility functions

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir="E:/DISC/reproducibility")
utilities_path = "./source/utilities.r"
source(utilities_path)
Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
reldist: Relative Distribution Methods
Version 1.6-6 created on 2016-10-07.
copyright (c) 2003, Mark S. Handcock, University of California-Los Angeles
 For citation information, type citation("reldist").
 Type help(package="reldist") to get started.

Load Data

Here, gene expression matrixes of RNA-seq and FISH experiments will be loaded.
We removed cells as SAVER.
We removed genes as our methods.
Only the overlapped gene in both filtered RNA-seq and FISH matrixes will be used here.

dataset_list = list()
dataset_list[["FISH"]] = readh5_loom("./data/MELANOMA/fish.loom")
[1] "./data/MELANOMA/fish.loom"
[1]    26 88040
raw_data = readh5_loom("./data/MELANOMA/raw.loom")
[1] "./data/MELANOMA/raw.loom"
[1] 32287  8498
gene_filter = gene_selection(raw_data, 10)
raw_input_data = raw_data[gene_filter, ]
use_genes = intersect(rownames(dataset_list[["FISH"]]), rownames(raw_input_data))
use_cell = colnames(dataset_list[["Raw"]])

We use these 19 overlapped genes for analysis below.

print(length(use_genes))
[1] 19
print(use_genes)
 [1] "EGFR"   "SOX10"  "CCNA2"  "GAPDH"  "WNT5A"  "PDGFC"  "FOSL1"  "MITF"   "RUNX2"  "FGFR1"  "JUN"    "VGF"    "BABAM1" "KDM5A" 
[15] "LMNA"   "KDM5B"  "C1S"    "VCL"    "TXNRD1"
dataset_list[["Raw"]] = raw_input_data[use_genes, ]
print(dim(raw_input_data))
[1] 15204  8498
print(dim(dataset_list[["Raw"]]))
[1]   19 8498
use_cell = colnames(dataset_list[["Raw"]])

After Run imputation, we get imputation results.
Now we load them.
Note that SAVER needs to generate gene expression values following gamma distribution (FF, Gini CMD) or poisson–gamma mixture (density distribution). For this reason, we load its rds-formatted file here.

### DISC
dataset_list[["DISC"]] = readh5_loom("./data/MELANOMA/DISC.loom", use_genes)
[1] "./data/MELANOMA/DISC.loom"
[1]   19 8498
### Other methods
#dataset_list[["SAVER"]] = readRDS("./data/MELANOMA/SAVER.rds")
#set.seed(42)
#dataset_list[["SAVER_gamma"]] = gamma_result(dataset_list[["SAVER"]], num_of_obs=1)[use_genes, use_cell]
dataset_list[["scVI"]] = readh5_imputation("./data/MELANOMA/scVI.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/scVI.hdf5"
[1]   19 8498
dataset_list[["MAGIC"]] = readh5_imputation("./data/MELANOMA/MAGIC.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/MAGIC.hdf5"
[1]   19 8498
dataset_list[["DCA"]] = readh5_imputation("./data/MELANOMA/DCA.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/DCA.hdf5"
[1]   19 8498
dataset_list[["scScope"]] = readh5_imputation("./data/MELANOMA/scScope.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/scScope.hdf5"
[1]   19 8498
dataset_list[["DeepImpute"]] = readh5_imputation("./data/MELANOMA/DeepImpute.hdf5", use_genes)
[1] "./data/MELANOMA/DeepImpute.hdf5"
[1]   19 8498
dataset_list[["VIPER"]] = readh5_imputation("./data/MELANOMA/VIPER.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/VIPER.hdf5"
[1]   19 8498
dataset_list[["scImpute"]] = readh5_imputation("./data/MELANOMA/scImpute.hdf5", use_genes, use_cell)
[1] "./data/MELANOMA/scImpute.hdf5"
[1]   19 8498

Output settings

The output color for each method and the output directory will be setup here.

method_name = c("Raw", "DISC", "scVI", "MAGIC", "DCA", "scScope", "DeepImpute", "VIPER", "scImpute")
method_color = c("#A5A5A5", "#E83828", "#278BC4", "#EADE36", "#198B41", "#920783", "#F8B62D", "#8E5E32", "#1E2B68")
names(method_color) = method_name
bar_color = rep("gray50", length(method_name))
names(bar_color) = method_name
bar_color["Raw"] = "gray80"
bar_color["DISC"] = "red"
text_color = rep("black", length(method_name))
names(text_color) = method_name
text_color["DISC"] = "red"
### make output dir
outdir = "./results/MELANOMA/structure_recovery"
dir.create(outdir, showWarnings = F, recursive = T)

Evaluation

Calculate correlation with FISH

### calculate correlation of FISH
cor_mat = matrix(ncol = 3, nrow = 0, dimnames = list(c(), c("Gene x", "Gene y", "FISH Correlation")))
cor_all = list()
for(ii in c("FISH", method_name)){
  cor_all[[ii]] = matrix(nrow = length(use_genes), ncol = length(use_genes), dimnames = list(use_genes, use_genes))
}
for(ii in use_genes){
  for(jj in use_genes){
    #cat(ii, "\t", jj, "\n")
    for(kk in c("FISH", method_name)){
      if(kk == "SAVER"){
        cor_all[["SAVER"]][ii, jj] = cor(dataset_list[["SAVER_gamma"]][ii, ], dataset_list[["SAVER_gamma"]][jj, ], use = "pairwise.complete.obs")
      }else{
        cor_all[[kk]][ii, jj] = cor(dataset_list[[kk]][ii, ], dataset_list[[kk]][jj, ], use = "pairwise.complete.obs")
      }
    }
  }
}

correlation map

fish_mask_mat = !is.na(cor_all[["FISH"]])
for(ii in 1:length(use_genes)){
  fish_mask_mat[ii, seq(ii)] = FALSE
}
fish_mask = as.vector(fish_mask_mat)
fish_gene_mat = apply(fish_mask_mat, 1, names)
outfile = paste0(outdir, "/correlation_map.pdf")
mfrow = make_mfrow(2, length(method_name))
#pdf(outfile, height = mfrow[1] * 3.75, width = mfrow[2] * 3.25)
layout_scatter(cor_all, method_name, fish_mask, this_xlab = "FISH", this_ylab = "scRNA-seq", xlim = c(-0.2, 1), ylim = c(-0.2, 1), point_size = 2.75)
       Raw       DISC       scVI      MAGIC        DCA    scScope DeepImpute      VIPER   scImpute 
 0.2067813  0.1933942  0.3461712  0.7846847  0.7060096  0.2628344  0.2116670  0.2023288  0.2090803 

#dev.off()

Correlation heatmap

#  heatmap & CMD
outfile = paste0(outdir, "/correlation_heatmap.pdf")
use_order = order(colMeans(matrix(apply(cor_all[["FISH"]], 2, function(x){
  x[is.na(x)] = 0
  return(x)
})[t(t(fish_gene_mat) != colnames(fish_gene_mat))], nrow = nrow(cor_all[["FISH"]]) - 1, ncol = ncol(cor_all[["FISH"]]), dimnames = list(c(), colnames(cor_all[["FISH"]])))), decreasing = T)
#pdf(outfile, height = 7, width = 12.5)
cmd_vector = layout_correlogram_plot(cor_all, use_order=use_order)

#dev.off()
names(cmd_vector) = names(cor_all)
cmd_vector = cmd_vector[setdiff(names(cmd_vector), "FISH")]

CMD

barplot_usage(cmd_vector, main = "CMD", bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)

saveRDS(cor_all, paste0(outdir, "/cor_all.rds"))

Normalization

example_gene = c("WNT5A", "SOX10")
rescale_mean_list = list()
for(ii in method_name){
  if(ii != "SAVER"){
    rescale_mean_list[[ii]] = mean_norm_fun(dataset_list[[ii]], dataset_list[["FISH"]])
  }else{
    rescale_mean_list[[ii]] = mean_norm_fun(dataset_list[["SAVER_gamma"]], dataset_list[["FISH"]])
  }
}
max_points = ncol(dataset_list[["FISH"]])
for(ii in rescale_mean_list){
  max_points = max(c(max_points, ncol(ii)))
}
saver_style_filt_norm_list = rescale_mean_list
#saver_style_filt_norm_list[["SAVER"]] = binom_result_mean(dataset_list[["SAVER"]], dataset_list[["FISH"]])
saver_style_filt_norm_list[["FISH"]] = dataset_list[["FISH"]]
norm_for_density = saver_style_filt_norm_list
for(ii in names(norm_for_density)){
  norm_for_density[[ii]] = norm_for_density[[ii]][example_gene,]
}
saveRDS(norm_for_density, paste0(outdir, "/norm_for_density.rds"))

Gini

gini_result_list = list()
for(ii in names(saver_style_filt_norm_list)){
  if(ii != "SAVER"){
    gini_result_list[[ii]] = apply(dataset_list[[ii]], 1, function(x){gini(x[complete.cases(x)])})
  }else{
    gini_result_list[[ii]] = apply(dataset_list[["SAVER_gamma"]], 1, function(x){gini(x[complete.cases(x)])})
  }
}
color_point = c("#31a354", "#a63603")
names(color_point) = example_gene
outfile = paste0(outdir, "/Gini.pdf")
mfrow = make_mfrow(2, length(method_name))
#pdf(outfile, height = mfrow[1] * 3.75, width = mfrow[2] * 3.25)
gini_rmse = layout_scatter(gini_result_list, method_name, use_genes, color_point = color_point, this_xlab = "FISH Gini", this_ylab = "scRNA-seq Gini", xlim = c(0, 1), ylim = c(0, 1))

#dev.off()
saveRDS(gini_result_list, paste0(outdir, "/gini_result_list.rds"))
par(mfrow = c(1, 1))
barplot_usage(gini_rmse, main = "Gini RMSE", bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)

Density

plot_genes = use_genes
ks_matrix = matrix(nrow = length(plot_genes), ncol = length(method_name), dimnames = list(plot_genes, method_name))
mfrow = c(3, 6)
outfile = paste0(outdir, "/density.pdf")
#pdf(outfile, height = mfrow[1] * 3, width = mfrow[2] * 2.75)
par(mfrow = mfrow)
for(ii in plot_genes){
  this_fish = saver_style_filt_norm_list[["FISH"]][ii, ]
  this_fish = this_fish[!is.na(this_fish)]
  fish_density = density(this_fish)
  zero_proportion = round(100 * (1 - sum(dataset_list[["Raw"]][ii, ] > 0) / length(use_cell)), digits = 4)
  xlim_max = as.numeric(quantile(this_fish, 0.90)) * 2
  dens.bw = fish_density$bw
  ylim_max = max(fish_density$y)
  use_density = list()
  for(method_index in 1:length(method_name)){
    this_method_expression = saver_style_filt_norm_list[[method_name[method_index]]][ii, ]
    if(length(unique(this_method_expression)) == 1){
      this_method_expression[1] = this_method_expression[1] * 1.0001
    }
    #if(method_index <= 2){
    if(T){
      this_density = density(this_method_expression, bw = dens.bw)
      use_density[[method_name[method_index]]] = this_density
      ylim_max = max(c(ylim_max, this_density$y))
    }
    ks_matrix[ii, method_index] = ks.test(delete_lt0.5(this_method_expression), delete_lt0.5(this_fish))$statistic
  }
  plot(fish_density, lwd = 2, col = "black", lty = 1,
       xlim = c(min(fish_density$x, 0), xlim_max + 5),
       ylim = c(0, ylim_max), yaxt = "s", bty="n",
       main = paste0(toupper(ii), " (", zero_proportion, "%)"),
       sub = "", ylab = "Density", xlab = "mRNA Counts")
  par(las = 0)
  for(this_density_name in names(use_density)){
    lines(use_density[[this_density_name]], lwd = 3, col=method_color[this_density_name])
  }
  if(ii %in% plot_genes[mfrow[2] + (mfrow[1] * mfrow[2] * seq(0, floor(length(plot_genes) / mfrow[1] * mfrow[2])))]){
    legend("topright", c("FISH", names(use_density)), lty = rep(1, 1 + length(names(use_density))),
           lwd = rep(3, 1 + length(names(use_density))), col = c("black", method_color[names(use_density)]), box.lty = 0, xjust = 1, yjust = 1)
  }
}

#dev.off()
outfile = paste0(outdir, "/density_summary.pdf")
#pdf(outfile, height = 6, width = ncol(ks_matrix) * 0.6)
ks_mean = Matrix::colMeans(ks_matrix)
standard_error_ks = apply(ks_matrix, 2, function(x) sqrt(var(x)/length(x)))
par(mfrow = c(1, 1))
barplot_usage(ks_mean, main = "K-S Statistic", bar_color = method_color, text_color = text_color, use_data_order = T, standard_error = standard_error_ks, use_border = F)

#dev.off()

2D distribution

# Make the plots
dist_outdir = paste0(outdir, "/2d_distribution")
dir.create(dist_outdir, showWarnings = F)
mfrow = make_mfrow(2, length(c("FISH", method_name)))
pairs_2d_distribution = cor_mat[order(abs(cor_mat[, 3]), decreasing = TRUE), ]
library(parallel)
no_cores <- max(c(detectCores() - 1, 1))
cl <- makeCluster(no_cores)
clusterExport(cl, varlist = c("dist_outdir", "rescale_mean_list", "method_name", "use_cell", "mfrow", "fish_gene_mat", "fish_mask_mat", "dataset_list", "utilities_path"))
return_list = parLapply(cl, 1:sum(fish_mask), function(ii){
  source(utilities_path)
  gene_x = fish_gene_mat[t(fish_mask_mat)][ii]
  gene_y = t(fish_gene_mat)[t(fish_mask_mat)][ii]
  x_dropout_rate = round(100 * (1 - sum(dataset_list[["Raw"]][gene_x, ] > 0) / length(use_cell)), digits = 4)
  y_dropout_rate = round(100 * (1 - sum(dataset_list[["Raw"]][gene_y, ] > 0) / length(use_cell)), digits = 4)
  x_fish_raw = dataset_list[["FISH"]][gene_x, ]
  y_fish_raw = dataset_list[["FISH"]][gene_y, ]
  select_cell = !is.na(x_fish_raw) & !is.na(y_fish_raw)
  x_fish = x_fish_raw[select_cell]
  y_fish = y_fish_raw[select_cell]
  fish_pair_mat = matrix(c(x_fish, y_fish), ncol = 2)
  ks_stat = c()
  corr_score = cor(x_fish, y_fish)
  x_i = list(FISH = x_fish)
  y_i = list(FISH = y_fish)
  for(jj in 1:length(method_name)){
    this_method_name = method_name[jj]
    x_i[[this_method_name]] = rescale_mean_list[[this_method_name]][gene_x, ]
    y_i[[this_method_name]] = rescale_mean_list[[this_method_name]][gene_y, ]
    ks_stat = c(ks_stat, ks2d2s(round(x_fish), round(y_fish), round(x_i[[this_method_name]]), round(y_i[[this_method_name]])))
    corr_score = c(corr_score, cor(x_i[[this_method_name]], y_i[[this_method_name]]))
  }
  names(corr_score) = c("FISH", method_name)
  names(ks_stat) = method_name
  this_name = c(paste(gene_x, gene_y, sep = "_"))
  pdf(paste0(dist_outdir, "/", this_name, ".pdf"),
      height = mfrow[1] * 4,
      width = mfrow[2] * 3.75)
  par(mfrow = mfrow)
  nbin = 128
  x_fish_95 = quantile(x_fish, 0.95) + 1### R is from 1 to max + 1
  y_fish_95 = quantile(y_fish, 0.95) + 1
  for(jj in c("FISH", method_name)){
    if(jj == "DISC"){
      col.main = "red"
    }else{
      col.main = "black"
    }
    if(jj == "Raw"){
      x_use = dataset_list[[jj]][gene_x, ]
      y_use = dataset_list[[jj]][gene_y, ]
      xlim = c(0, max(x_use))
      ylim = c(0, max(y_use))
      bandwidth = c(xlim[2] / nbin, ylim[2] / nbin)
    }else{
      x_use = x_i[[jj]]
      y_use = y_i[[jj]]
      xlim = c(0, x_fish_95)
      ylim = c(0, y_fish_95)
      bandwidth = c(max(x_fish) / nbin, max(y_fish) / nbin)
    }
    smoothScatter1(x = x_use, y = y_use,
                   xlab = paste0(gene_x, " (", x_dropout_rate, "%)"),
                   ylab = paste0(gene_y, " (", y_dropout_rate, "%)"),
                   cex = 1.5, xlim = xlim, ylim = ylim,
                   lwd = 2, main = paste0(jj, " - FF = ", round(ks_stat[jj], 4)),
                   nrpoints = 0, col.main = col.main, nbin = nbin, bandwidth = bandwidth)
  }
  dev.off()
  return(list("ks_stat" = matrix(ks_stat, nrow = 1, dimnames = list(paste(gene_x, gene_y, sep = " - "), c())),
              "corr_score" = matrix(corr_score, nrow = 1, dimnames = list(paste(gene_x, gene_y, sep = " - "), c()))))
})
stopCluster(cl)
ks_stat_mat = matrix(nrow = 0, ncol = length(method_name), dimnames = list(c(), method_name))
corr_mat = matrix(nrow = 0, ncol = length(method_name) + 1, dimnames = list(c(), c("FISH", method_name)))

for(ii in return_list){
  ks_stat_mat = rbind(ks_stat_mat, ii$ks_stat)
  corr_mat = rbind(corr_mat, ii$corr_score)
}
saveRDS(ks_stat_mat, paste(outdir, "/ks_stat_mat.rds", sep = ""))
print(paste0("Please see ", dist_outdir, " for all results."))
[1] "Please see ./results/MELANOMA/structure_recovery/2d_distribution for all results."
###all_compare
mean_ks_stat = Matrix::colMeans(ks_stat_mat)
standard_error_ks_stat = apply(ks_stat_mat, 2, function(x) sqrt(var(x)/length(x)))
outfile = paste(outdir, "/score_compare.pdf", sep = "")
#pdf(outfile, height = 5, width = 11)
par(mfrow = c(1, 2))
barplot_usage(mean_ks_stat, main = "Fasano and Franceschini's Test", cex.main = 1.5,bar_color = method_color, text_color = text_color, use_data_order = T, standard_error = standard_error_ks_stat, use_border = F)
corr_rmse = sapply(method_name, function(x) rmse(corr_mat[, "FISH"], corr_mat[, x]))
barplot_usage(corr_rmse, main = "FISH - Impute Correlation RMSE", cex.main = 1.5, bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)

#dev.off()

Summary

outfile = paste(outdir, "/Bar_plot.pdf", sep = "")
#pdf(outfile, height = 3, width = 9.5)
plot_height = 4
plot_width = 3.5
plot_region = matrix(seq(3), nrow = 1)
this_height = rep(plot_height, nrow(plot_region))
this_width = rep(plot_width, ncol(plot_region))
this_index = max(plot_region) + 1
layout_mat = plot_region
xlab_region = matrix(rep(this_index, ncol(plot_region)), nrow = 1)
layout_mat = rbind(layout_mat, xlab_region)
this_height = c(this_height, 0.5)
layout(mat = layout_mat, heights = this_height, widths = this_width)
par(mar = c(1, 4.1, 4.1, 2.1))
barplot_usage(cmd_vector, main = "CMD", bar_color = method_color, use_data_order = T, use_border = F)
barplot_usage(gini_rmse, main = "Gini RMSE", bar_color = method_color, use_data_order = T, use_border = F)
barplot_usage(mean_ks_stat, main = "Fasano and\nFranceschini's Test", cex.main = 1.5, bar_color = method_color, use_data_order = T, standard_error = standard_error_ks_stat, use_border = F)
par(mar = rep(0, 4))
plot(1, type = "n", axes = FALSE, xlab="", ylab="")
legend(x = "top",inset = 0, legend = names(method_color), fill = method_color, horiz = TRUE, border = NA, bty = "n")

#dev.off()
output_list = list()
output_list[["cmd"]] = cmd_vector
output_list[["gini_rmse"]] = gini_rmse
output_list[["mean_ks_stat"]] = mean_ks_stat
output_list[["standard_error_ks_stat"]] = standard_error_ks_stat
saveRDS(output_list, paste(outdir, "/Bar_stat.rds", sep = ""))

Notes:

Single plots of FF are saved in here.
ALL data we used in this script can be found here.

---
title: "Gene expression structures recovery validated by FISH"
output: html_notebook
---
### Setup knitr and load utility functions
```{r setup}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir="E:/DISC/reproducibility")
```
```{r}
utilities_path = "./source/utilities.r"
source(utilities_path)
```
### Load Data
Here, gene expression matrixes of RNA-seq and FISH experiments will be loaded.</br>
We removed cells as <a href="https://www.nature.com/articles/s41592-018-0033-z">SAVER</a>.</br>
We removed genes as our methods.</br>
Only the overlapped gene in both filtered RNA-seq and FISH matrixes will be used here.
```{r}
dataset_list = list()
dataset_list[["FISH"]] = readh5_loom("./data/MELANOMA/fish.loom")
raw_data = readh5_loom("./data/MELANOMA/raw.loom")
gene_filter = gene_selection(raw_data, 10)
raw_input_data = raw_data[gene_filter, ]
use_genes = intersect(rownames(dataset_list[["FISH"]]), rownames(raw_input_data))
use_cell = colnames(dataset_list[["Raw"]])
```
We use these 19 overlapped genes for analysis below.
```{r}
print(length(use_genes))
print(use_genes)
dataset_list[["Raw"]] = raw_input_data[use_genes, ]
print(dim(raw_input_data))
print(dim(dataset_list[["Raw"]]))
use_cell = colnames(dataset_list[["Raw"]])
```
After <a href="https://github.com/iyhaoo/DISC/blob/master/reproducibility/tutorials/run_imputation.md">Run imputation</a>, we get imputation results.</br>
Now we load them.</br>
Note that SAVER needs to generate gene expression values following gamma distribution (FF, Gini CMD) or poisson–gamma mixture (density distribution). For this reason, we load its rds-formatted file here.
```{r}
### DISC
dataset_list[["DISC"]] = readh5_loom("./data/MELANOMA/DISC.loom", use_genes)
### Other methods
#dataset_list[["SAVER"]] = readRDS("./data/MELANOMA/SAVER.rds")
#set.seed(42)
#dataset_list[["SAVER_gamma"]] = gamma_result(dataset_list[["SAVER"]], num_of_obs=1)[use_genes, use_cell]
dataset_list[["scVI"]] = readh5_imputation("./data/MELANOMA/scVI.hdf5", use_genes, use_cell)
dataset_list[["MAGIC"]] = readh5_imputation("./data/MELANOMA/MAGIC.hdf5", use_genes, use_cell)
dataset_list[["DCA"]] = readh5_imputation("./data/MELANOMA/DCA.hdf5", use_genes, use_cell)
dataset_list[["scScope"]] = readh5_imputation("./data/MELANOMA/scScope.hdf5", use_genes, use_cell)
dataset_list[["DeepImpute"]] = readh5_imputation("./data/MELANOMA/DeepImpute.hdf5", use_genes)
dataset_list[["VIPER"]] = readh5_imputation("./data/MELANOMA/VIPER.hdf5", use_genes, use_cell)
dataset_list[["scImpute"]] = readh5_imputation("./data/MELANOMA/scImpute.hdf5", use_genes, use_cell)
```
### Output settings
The output color for each method and the output directory will be setup here.
```{r}
method_name = c("Raw", "DISC", "scVI", "MAGIC", "DCA", "scScope", "DeepImpute", "VIPER", "scImpute")
method_color = c("#A5A5A5", "#E83828", "#278BC4", "#EADE36", "#198B41", "#920783", "#F8B62D", "#8E5E32", "#1E2B68")
names(method_color) = method_name
bar_color = rep("gray50", length(method_name))
names(bar_color) = method_name
bar_color["Raw"] = "gray80"
bar_color["DISC"] = "red"
text_color = rep("black", length(method_name))
names(text_color) = method_name
text_color["DISC"] = "red"
### make output dir
outdir = "./results/MELANOMA/structure_recovery"
dir.create(outdir, showWarnings = F, recursive = T)
```
### Evaluation
#### Calculate correlation with FISH
```{r}
### calculate correlation of FISH
cor_mat = matrix(ncol = 3, nrow = 0, dimnames = list(c(), c("Gene x", "Gene y", "FISH Correlation")))
cor_all = list()
for(ii in c("FISH", method_name)){
  cor_all[[ii]] = matrix(nrow = length(use_genes), ncol = length(use_genes), dimnames = list(use_genes, use_genes))
}
for(ii in use_genes){
  for(jj in use_genes){
    #cat(ii, "\t", jj, "\n")
    for(kk in c("FISH", method_name)){
      if(kk == "SAVER"){
        cor_all[["SAVER"]][ii, jj] = cor(dataset_list[["SAVER_gamma"]][ii, ], dataset_list[["SAVER_gamma"]][jj, ], use = "pairwise.complete.obs")
      }else{
        cor_all[[kk]][ii, jj] = cor(dataset_list[[kk]][ii, ], dataset_list[[kk]][jj, ], use = "pairwise.complete.obs")
      }
    }
  }
}
```
#### correlation map
```{r fig.height=7.5, fig.width=16}
fish_mask_mat = !is.na(cor_all[["FISH"]])
for(ii in 1:length(use_genes)){
  fish_mask_mat[ii, seq(ii)] = FALSE
}
fish_mask = as.vector(fish_mask_mat)
fish_gene_mat = apply(fish_mask_mat, 1, names)
outfile = paste0(outdir, "/correlation_map.pdf")
mfrow = make_mfrow(2, length(method_name))
#pdf(outfile, height = mfrow[1] * 3.75, width = mfrow[2] * 3.25)
layout_scatter(cor_all, method_name, fish_mask, this_xlab = "FISH", this_ylab = "scRNA-seq", xlim = c(-0.2, 1), ylim = c(-0.2, 1), point_size = 2.75)
#dev.off()
```
#### Correlation heatmap
```{r fig.height=7, fig.width=16}
#  heatmap & CMD
outfile = paste0(outdir, "/correlation_heatmap.pdf")
use_order = order(colMeans(matrix(apply(cor_all[["FISH"]], 2, function(x){
  x[is.na(x)] = 0
  return(x)
})[t(t(fish_gene_mat) != colnames(fish_gene_mat))], nrow = nrow(cor_all[["FISH"]]) - 1, ncol = ncol(cor_all[["FISH"]]), dimnames = list(c(), colnames(cor_all[["FISH"]])))), decreasing = T)
#pdf(outfile, height = 7, width = 12.5)
cmd_vector = layout_correlogram_plot(cor_all, use_order=use_order)
#dev.off()
names(cmd_vector) = names(cor_all)
cmd_vector = cmd_vector[setdiff(names(cmd_vector), "FISH")]
```
#### CMD
```{r fig.height=6, fig.width=4.5}
barplot_usage(cmd_vector, main = "CMD", bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)
saveRDS(cor_all, paste0(outdir, "/cor_all.rds"))
```
#### Normalization
```{r}
example_gene = c("WNT5A", "SOX10")
rescale_mean_list = list()
for(ii in method_name){
  if(ii != "SAVER"){
    rescale_mean_list[[ii]] = mean_norm_fun(dataset_list[[ii]], dataset_list[["FISH"]])
  }else{
    rescale_mean_list[[ii]] = mean_norm_fun(dataset_list[["SAVER_gamma"]], dataset_list[["FISH"]])
  }
}
max_points = ncol(dataset_list[["FISH"]])
for(ii in rescale_mean_list){
  max_points = max(c(max_points, ncol(ii)))
}
saver_style_filt_norm_list = rescale_mean_list
#saver_style_filt_norm_list[["SAVER"]] = binom_result_mean(dataset_list[["SAVER"]], dataset_list[["FISH"]])
saver_style_filt_norm_list[["FISH"]] = dataset_list[["FISH"]]
norm_for_density = saver_style_filt_norm_list
for(ii in names(norm_for_density)){
  norm_for_density[[ii]] = norm_for_density[[ii]][example_gene,]
}
saveRDS(norm_for_density, paste0(outdir, "/norm_for_density.rds"))
```
#### Gini
```{r fig.height=7.5, fig.width=16.5}
gini_result_list = list()
for(ii in names(saver_style_filt_norm_list)){
  if(ii != "SAVER"){
    gini_result_list[[ii]] = apply(dataset_list[[ii]], 1, function(x){gini(x[complete.cases(x)])})
  }else{
    gini_result_list[[ii]] = apply(dataset_list[["SAVER_gamma"]], 1, function(x){gini(x[complete.cases(x)])})
  }
}
color_point = c("#31a354", "#a63603")
names(color_point) = example_gene
outfile = paste0(outdir, "/Gini.pdf")
mfrow = make_mfrow(2, length(method_name))
#pdf(outfile, height = mfrow[1] * 3.75, width = mfrow[2] * 3.25)
gini_rmse = layout_scatter(gini_result_list, method_name, use_genes, color_point = color_point, this_xlab = "FISH Gini", this_ylab = "scRNA-seq Gini", xlim = c(0, 1), ylim = c(0, 1))
#dev.off()
saveRDS(gini_result_list, paste0(outdir, "/gini_result_list.rds"))
```
```{r fig.height=6, fig.width=4.5}
par(mfrow = c(1, 1))
barplot_usage(gini_rmse, main = "Gini RMSE", bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)
```
#### Density
```{r fig.height=9, fig.width=16.5, warning=FALSE}
plot_genes = use_genes
ks_matrix = matrix(nrow = length(plot_genes), ncol = length(method_name), dimnames = list(plot_genes, method_name))
mfrow = c(3, 6)
outfile = paste0(outdir, "/density.pdf")
#pdf(outfile, height = mfrow[1] * 3, width = mfrow[2] * 2.75)
par(mfrow = mfrow)
for(ii in plot_genes){
  this_fish = saver_style_filt_norm_list[["FISH"]][ii, ]
  this_fish = this_fish[!is.na(this_fish)]
  fish_density = density(this_fish)
  zero_proportion = round(100 * (1 - sum(dataset_list[["Raw"]][ii, ] > 0) / length(use_cell)), digits = 4)
  xlim_max = as.numeric(quantile(this_fish, 0.90)) * 2
  dens.bw = fish_density$bw
  ylim_max = max(fish_density$y)
  use_density = list()
  for(method_index in 1:length(method_name)){
    this_method_expression = saver_style_filt_norm_list[[method_name[method_index]]][ii, ]
    if(length(unique(this_method_expression)) == 1){
      this_method_expression[1] = this_method_expression[1] * 1.0001
    }
    #if(method_index <= 2){
    if(T){
      this_density = density(this_method_expression, bw = dens.bw)
      use_density[[method_name[method_index]]] = this_density
      ylim_max = max(c(ylim_max, this_density$y))
    }
    ks_matrix[ii, method_index] = ks.test(delete_lt0.5(this_method_expression), delete_lt0.5(this_fish))$statistic
  }
  plot(fish_density, lwd = 2, col = "black", lty = 1,
       xlim = c(min(fish_density$x, 0), xlim_max + 5),
       ylim = c(0, ylim_max), yaxt = "s", bty="n",
       main = paste0(toupper(ii), " (", zero_proportion, "%)"),
       sub = "", ylab = "Density", xlab = "mRNA Counts")
  par(las = 0)
  for(this_density_name in names(use_density)){
    lines(use_density[[this_density_name]], lwd = 3, col=method_color[this_density_name])
  }
  if(ii %in% plot_genes[mfrow[2] + (mfrow[1] * mfrow[2] * seq(0, floor(length(plot_genes) / mfrow[1] * mfrow[2])))]){
    legend("topright", c("FISH", names(use_density)), lty = rep(1, 1 + length(names(use_density))),
           lwd = rep(3, 1 + length(names(use_density))), col = c("black", method_color[names(use_density)]), box.lty = 0, xjust = 1, yjust = 1)
  }
}
#dev.off()
```
```{r fig.height=6, fig.width=4.5}
outfile = paste0(outdir, "/density_summary.pdf")
#pdf(outfile, height = 6, width = ncol(ks_matrix) * 0.6)
ks_mean = Matrix::colMeans(ks_matrix)
standard_error_ks = apply(ks_matrix, 2, function(x) sqrt(var(x)/length(x)))
par(mfrow = c(1, 1))
barplot_usage(ks_mean, main = "K-S Statistic", bar_color = method_color, text_color = text_color, use_data_order = T, standard_error = standard_error_ks, use_border = F)
#dev.off()
```
#### 2D distribution
```{r}
# Make the plots
dist_outdir = paste0(outdir, "/2d_distribution")
dir.create(dist_outdir, showWarnings = F)
mfrow = make_mfrow(2, length(c("FISH", method_name)))
pairs_2d_distribution = cor_mat[order(abs(cor_mat[, 3]), decreasing = TRUE), ]
library(parallel)
no_cores <- max(c(detectCores() - 1, 1))
cl <- makeCluster(no_cores)
clusterExport(cl, varlist = c("dist_outdir", "rescale_mean_list", "method_name", "use_cell", "mfrow", "fish_gene_mat", "fish_mask_mat", "dataset_list", "utilities_path"))
return_list = parLapply(cl, 1:sum(fish_mask), function(ii){
  source(utilities_path)
  gene_x = fish_gene_mat[t(fish_mask_mat)][ii]
  gene_y = t(fish_gene_mat)[t(fish_mask_mat)][ii]
  x_dropout_rate = round(100 * (1 - sum(dataset_list[["Raw"]][gene_x, ] > 0) / length(use_cell)), digits = 4)
  y_dropout_rate = round(100 * (1 - sum(dataset_list[["Raw"]][gene_y, ] > 0) / length(use_cell)), digits = 4)
  x_fish_raw = dataset_list[["FISH"]][gene_x, ]
  y_fish_raw = dataset_list[["FISH"]][gene_y, ]
  select_cell = !is.na(x_fish_raw) & !is.na(y_fish_raw)
  x_fish = x_fish_raw[select_cell]
  y_fish = y_fish_raw[select_cell]
  fish_pair_mat = matrix(c(x_fish, y_fish), ncol = 2)
  ks_stat = c()
  corr_score = cor(x_fish, y_fish)
  x_i = list(FISH = x_fish)
  y_i = list(FISH = y_fish)
  for(jj in 1:length(method_name)){
    this_method_name = method_name[jj]
    x_i[[this_method_name]] = rescale_mean_list[[this_method_name]][gene_x, ]
    y_i[[this_method_name]] = rescale_mean_list[[this_method_name]][gene_y, ]
    ks_stat = c(ks_stat, ks2d2s(round(x_fish), round(y_fish), round(x_i[[this_method_name]]), round(y_i[[this_method_name]])))
    corr_score = c(corr_score, cor(x_i[[this_method_name]], y_i[[this_method_name]]))
  }
  names(corr_score) = c("FISH", method_name)
  names(ks_stat) = method_name
  this_name = c(paste(gene_x, gene_y, sep = "_"))
  pdf(paste0(dist_outdir, "/", this_name, ".pdf"),
      height = mfrow[1] * 4,
      width = mfrow[2] * 3.75)
  par(mfrow = mfrow)
  nbin = 128
  x_fish_95 = quantile(x_fish, 0.95) + 1### R is from 1 to max + 1
  y_fish_95 = quantile(y_fish, 0.95) + 1
  for(jj in c("FISH", method_name)){
    if(jj == "DISC"){
      col.main = "red"
    }else{
      col.main = "black"
    }
    if(jj == "Raw"){
      x_use = dataset_list[[jj]][gene_x, ]
      y_use = dataset_list[[jj]][gene_y, ]
      xlim = c(0, max(x_use))
      ylim = c(0, max(y_use))
      bandwidth = c(xlim[2] / nbin, ylim[2] / nbin)
    }else{
      x_use = x_i[[jj]]
      y_use = y_i[[jj]]
      xlim = c(0, x_fish_95)
      ylim = c(0, y_fish_95)
      bandwidth = c(max(x_fish) / nbin, max(y_fish) / nbin)
    }
    smoothScatter1(x = x_use, y = y_use,
                   xlab = paste0(gene_x, " (", x_dropout_rate, "%)"),
                   ylab = paste0(gene_y, " (", y_dropout_rate, "%)"),
                   cex = 1.5, xlim = xlim, ylim = ylim,
                   lwd = 2, main = paste0(jj, " - FF = ", round(ks_stat[jj], 4)),
                   nrpoints = 0, col.main = col.main, nbin = nbin, bandwidth = bandwidth)
  }
  dev.off()
  return(list("ks_stat" = matrix(ks_stat, nrow = 1, dimnames = list(paste(gene_x, gene_y, sep = " - "), c())),
              "corr_score" = matrix(corr_score, nrow = 1, dimnames = list(paste(gene_x, gene_y, sep = " - "), c()))))
})
stopCluster(cl)
ks_stat_mat = matrix(nrow = 0, ncol = length(method_name), dimnames = list(c(), method_name))
corr_mat = matrix(nrow = 0, ncol = length(method_name) + 1, dimnames = list(c(), c("FISH", method_name)))

for(ii in return_list){
  ks_stat_mat = rbind(ks_stat_mat, ii$ks_stat)
  corr_mat = rbind(corr_mat, ii$corr_score)
}
saveRDS(ks_stat_mat, paste(outdir, "/ks_stat_mat.rds", sep = ""))
print(paste0("Please see ", dist_outdir, " for all results."))
###all_compare
mean_ks_stat = Matrix::colMeans(ks_stat_mat)
standard_error_ks_stat = apply(ks_stat_mat, 2, function(x) sqrt(var(x)/length(x)))
```
```{r fig.height=8, fig.width=12}
outfile = paste(outdir, "/score_compare.pdf", sep = "")
#pdf(outfile, height = 5, width = 11)
par(mfrow = c(1, 2))
barplot_usage(mean_ks_stat, main = "Fasano and Franceschini's Test", cex.main = 1.5,bar_color = method_color, text_color = text_color, use_data_order = T, standard_error = standard_error_ks_stat, use_border = F)
corr_rmse = sapply(method_name, function(x) rmse(corr_mat[, "FISH"], corr_mat[, x]))
barplot_usage(corr_rmse, main = "FISH - Impute Correlation RMSE", cex.main = 1.5, bar_color = method_color, text_color = text_color, use_data_order = T, use_border = F)
#dev.off()
```
### Summary
```{r fig.height=3, fig.width=9.5}
outfile = paste(outdir, "/Bar_plot.pdf", sep = "")
#pdf(outfile, height = 3, width = 9.5)
plot_height = 4
plot_width = 3.5
plot_region = matrix(seq(3), nrow = 1)
this_height = rep(plot_height, nrow(plot_region))
this_width = rep(plot_width, ncol(plot_region))
this_index = max(plot_region) + 1
layout_mat = plot_region
xlab_region = matrix(rep(this_index, ncol(plot_region)), nrow = 1)
layout_mat = rbind(layout_mat, xlab_region)
this_height = c(this_height, 0.5)
layout(mat = layout_mat, heights = this_height, widths = this_width)
par(mar = c(1, 4.1, 4.1, 2.1))
barplot_usage(cmd_vector, main = "CMD", bar_color = method_color, use_data_order = T, use_border = F)
barplot_usage(gini_rmse, main = "Gini RMSE", bar_color = method_color, use_data_order = T, use_border = F)
barplot_usage(mean_ks_stat, main = "Fasano and\nFranceschini's Test", cex.main = 1.5, bar_color = method_color, use_data_order = T, standard_error = standard_error_ks_stat, use_border = F)
par(mar = rep(0, 4))
plot(1, type = "n", axes = FALSE, xlab="", ylab="")
legend(x = "top",inset = 0, legend = names(method_color), fill = method_color, horiz = TRUE, border = NA, bty = "n")
#dev.off()
output_list = list()
output_list[["cmd"]] = cmd_vector
output_list[["gini_rmse"]] = gini_rmse
output_list[["mean_ks_stat"]] = mean_ks_stat
output_list[["standard_error_ks_stat"]] = standard_error_ks_stat
saveRDS(output_list, paste(outdir, "/Bar_stat.rds", sep = ""))
```
### Notes:
Single plots of FF are saved in <a href="https://github.com/iyhaoo/DISC/tree/master/reproducibility/results/MELANOMA/structure_recovery/2d_distribution">here</a>.</br>
ALL data we used in this script can be found <a href="https://github.com/iyhaoo/DISC_data_availability/tree/master/MELANOMA">here</a>.


