report.Rmd

---
output: html_document
params:
    file:
        value: x
    set_title:
        value: x
title: '`r params$set_title`'
date: '`r strftime(Sys.time(), format = "%B %d, %Y")`'
---

```{r, include=FALSE}
library(knitr)
opts_chunk$set(fig.height = 6, fig.width = 8, fig.align = 'center', echo = FALSE)
library(scater)
library(SingleCellExperiment)
d <- readRDS(paste0("scater-objects/", params$file))
cell_types <- sort(colnames(colData(d))[grepl("cell_type", colnames(colData(d)))])
```

## QC of library size and detected genes

```{r}
if(!is.null(d$total_counts) & !is.null(d$total_features)) {
    for(ct in cell_types) {
        print(plotPhenoData(d, aes_string(x = "total_counts", y = "total_features", colour = ct)))
    }
}
```

```{r}
if(!is.null(d$total_counts) & !is.null(d$total_features)) {
    for(ct in cell_types) {
        print(plotPhenoData(d, aes_string(x = ct, y = "total_features", colour = "log10_total_counts")) + theme(axis.text.x = element_text(angle = 90, hjust = 1)))
    }
}
```

## QC of expression

```{r, message=FALSE, warning=FALSE}
if(!is.null(d$total_features)) {
    plotQC(d, type = "highest-expression")
}
```

```{r}
if(!is.null(rowData(d)$mean_counts) & !is.null(rowData(d)$n_cells_counts)) {
    plotQC(d, type = "exprs-freq-vs-mean")
}
```

## PCA plot
```{r}
for(ct in cell_types) {
    print(plotPCA(d, colour_by = ct))
}
```