cor_report.Rmd

---
title: "Correlation Plots"
output: html_document

params:
  rp_upload_data: NA
  rp_combined_meta: NA
  rp_choice_b0: NA
  rp_choice_b1: NA
---

```{r eval = FALSE, echo = FALSE}
# For PDF output, change the header to have "output: pdf_document".
#
# Note that due to an issue in rmarkdown, the default value of a parameter in
# the header cannot be `NULL`, so I used a default of `NA` for the default value
# of `n`.
library(dplyr)
library(DT)
library(ggpubr)
library(patchwork)
```


## Summary of plate level information.


```{r, echo=F, message=F, warning=F}
#common_sample <- intersect(params$rp_upload_data[[params$rp_choice_b0]]$Assay, params$rp_upload_data[[params$rp_choice_b1]]$Assay)
common_sample <- intersect(params$rp_combined_meta$Sample[params$rp_combined_meta$File == params$rp_choice_b0], 
                               params$rp_combined_meta$Sample[params$rp_combined_meta$File == params$rp_choice_b1])

common_analyte <- intersect(rownames(params$rp_upload_data[[params$rp_choice_b0]]), rownames(params$rp_upload_data[[params$rp_choice_b1]]))

data_a <- params$rp_upload_data[[params$rp_choice_b0]][match(common_analyte, rownames(params$rp_upload_data[[params$rp_choice_b0]])),
                                                       match(common_sample, params$rp_combined_meta$Sample[params$rp_combined_meta$File == params$rp_choice_b0])]
data_b <- params$rp_upload_data[[params$rp_choice_b1]][match(common_analyte, rownames(params$rp_upload_data[[params$rp_choice_b1]])),
                                                       match(common_sample, params$rp_combined_meta$Sample[params$rp_combined_meta$File == params$rp_choice_b1])]

```


```{r, echo=F, message=F, warning=F}
DT::datatable(
      data.frame(filename = c(data_a$File[1]%>% unlist(), data_b$File[1]%>% unlist()),
                 total_common_samples = c(ncol(data_a), ncol(data_b)),
                 total_common_analyte = c(nrow(data_a), nrow(data_b))),
      rownames = NULL,
      options = list(scrollX = TRUE))

```


```{r, echo=F, message=F, warning=F, eval=F}
paste("Total of", nrow(data_a), "Common analytes :")

paste(common_analyte, collapse = ",")
```

## Correlation of common analytes 

**Dataset A: `r paste(params$rp_choice_b0)`**     
**Dataset B: `r paste(params$rp_choice_b1)`**     


```{r, echo=F, message=F, warning=F, out.width= "100%", results="hide"}
data_a <- data_a@assays@data$data_imputed
data_b <- data_b@assays@data$data_imputed

data_a[is.na(data_a)] < 0
data_b[is.na(data_b)] < 0

rownames(data_a) <- rownames(data_b) <- common_analyte

plot.ls <- lapply(common_analyte, function(x){
  data.frame(A = data_a[rownames(data_a) == x, ],
             B = data_b[rownames(data_b) == x, ],
             Sample = common_sample)%>%
  ggscatter(x = "A", y = "B", size = 1,
  add = "reg.line", conf.int = F)+
  stat_cor(color = "red", size = 2)+
  labs(title = x)+
  theme_bw(base_size = 8)
})

per_panel <- 9
n_panel <- ceiling(length(plot.ls)/per_panel)
for (i in 1 : n_panel) {
  print(
    wrap_plots(plot.ls[(1 + (i-1) * per_panel) : min(length(plot.ls), (per_panel + (i-1) * per_panel))],
               ncol = 3, nrow = 3, byrow = T)
  )
}
```