---
title: "<img src=\"./anabel-logo.png\" style=\"float: right;width: 180px\"/>Single-curve Analysis" 
params:
  rprt_title: "Single-curve Analysis"
  prev_plot: ""
  kinetics: ""
  method_params: ""
  ids: NULL
  fit_data: ""
output: 
  html_document:
    df_print: paged
    toc: true
    toc_float: true
    toc_depth: 3
    number_sections: false
---

```{r, include = FALSE}
library(dplyr)
library(ggplot2)

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", width = 68)
```

# Summary report of SCA

## Experiment input

### Preview of randomly selected sensograms

```{r Preview_Plot, warning=FALSE, error=FALSE, echo=FALSE, fig.align='center', fig.cap = "Sensogram of randomly chosen samples"}
knitr::opts_chunk$set(echo = FALSE)
rslts = nrow(params$kinetics) > 0
params$prev_plot
```

### Input parameters 

```{r InParams, comment=NA}
str = params$method_params %>% strsplit("\n") %>% unlist
for (i in 1:length(str)){
  if(length(grep("^Fitting", str[i])) ==1) {
    break
  }else{cat(str[i])}
  cat("\n")
}
```


## Results

```{r rslt_kntks_tbl, eval = rslts, warning=FALSE}
df = params$kinetics
ids = params$ids
  if(!is.null(ids)){
    for(i in ids){
      df <- rbind(df, NA)
      df$ID[nrow(df)] = i
      df$FittingQ[nrow(df)] = "Failed"
    }
  }
```

### Fitting summary

```{r rslt_kntks_plot, eval = rslts, fig.show = 'hold', out.width= '50%', warning=FALSE}
par(mar = c(4, 4, .2, .1))
df <- df %>% mutate(
  Samples = case_when(
    FittingQ == "Failed" ~ "Unsuccessful fitting",
    FittingQ == "Warning" ~ "Successful fitting with warnings",
    TRUE ~ "Successful fitting"
  )
)
Type <- df %>% group_by(Samples) %>% summarize(Counts = n()) 
df = merge(df, Type, by ="Samples", all.x = TRUE)

Type = c("Successful fitting", "Successful fitting with warnings", "Unsuccessful fitting")
if(any(!Type %in% unique(df$Samples))){
  Type = Type[!Type %in% unique(df$Samples)]
  df = bind_rows(df, data.frame(Samples = Type, Counts = 0))
}

 ggplot(data = df, aes(Samples, fill = Samples)) + geom_bar(col = "#64686A") + theme_minimal() + 
  scale_fill_manual(values = c("Successful fitting" = "#D1E288", 
                               "Successful fitting with warnings" = "#E8A5A3", 
                               "Unsuccessful fitting" = "#F0F0F0")) + 
  geom_text(data = df, aes(Samples, Counts, label = Counts), nudge_y = 31) + 
  theme(legend.position = "none") + ggtitle("Fitting quality")

 df = df[!is.na(df$ID),]
 
df = df %>%  
        mutate(
                binder = case_when(
                  log10(KD) <= -15 ~ "below fM",                            
                  log10(KD) <= -12 ~ "fM",                           
                  log10(KD) <= -9 ~ "pM",                            
                  log10(KD) <= -6 ~ "nM",                            
                  log10(KD) <= -3 ~ "uM",                            
                  is.na(KD) | KD <= 0 ~ "no KD",                            
                  TRUE ~ "other") 
                )

Counts_df <- df %>% 
  group_by(binder) %>% count() %>%
  mutate(ypos = cumsum(n)- 0.5*n )

df = merge(df, Counts_df, by = "binder", all.x = TRUE)
df$label <- paste0(df$binder, ": ", df$n)

ggplot(df, aes(binder,fill=label)) + ggtitle("Value of binding affinity") + 
  geom_bar(width=1, col = "white") + theme_void() + 
  theme(legend.title = element_blank()) + labs(x = NULL, y = NULL) + coord_polar("y", start=0) +
  scale_fill_manual(values = c("#A2C510", "#C61E19", "#99CFE9", 
                               "#FBB800", "#958BB2","#F08000", "#6696B9"))

```

### Fitting results of randomly selected sensograms

```{r rslt_fit_plot, eval = rslts, fig.show = 'hold', fig.align='center', fig.height=2, fig.width=9, warning=FALSE}
params$fit_data %>%
  ggplot(aes(x = Time)) + geom_point(aes(y = Response, col = Name)) +
  geom_path(aes(y = fit)) + theme_light() +
  facet_wrap(~Name, ncol = length(unique(params$fit_data$Name))) +
  scale_color_manual(values = c(
      "#A2C510", "#C61E19", "#99CFE9", "#FBB800", "#958BB2",
      "#F08000", "#6696B9", "#B2B4B5", "#6B7B88", "#5C9B5B"
    ))
```

> Check the pdf file for more compehensive output

```{r rslt_kntks_empty, eval = !rslts}
print("!!! Sample(s) could not be fitted !!!")
```

#### Glossary

```{r gloss, out.width= '50%'}
df = openxlsx::read.xlsx("./tbls/glossary_sca.xlsx")
df
```

