02-quality-control-Y-summary.Rmd

```{r parameters-and-defaults, include = FALSE}
module <- "scRNAseq"
section <- "quality_control"
```

```{r parameter-merge, include = FALSE}
local_params <- module %>%
  options() %>%
  magrittr::extract2(module) %>%
  magrittr::extract2(section) %>%
  ReporteR.base::validate_params(parameters_and_defaults)
```

### Summary

```{r scRNAseq-quality-control-G-summary-checks, incldue = FALSE, echo = FALSE}
assertive.sets::assert_is_subset(local_params$summarize_features, colnames(SummarizedExperiment::rowData(object)))
assertive.sets::assert_is_subset(local_params$summarize_samples, colnames(SummarizedExperiment::colData(object)))
```

```{r scRNAseq-quality-control-G-summary-processing, echo=FALSE}
sample_summary <- lapply(local_params$summarize_samples, function(c) {
  x <- SummarizedExperiment::colData(object_filtered)[, c]
  list(
    mean = round(mean(x, na.rm = T)),
    sd = round(sd(x, na.rm = T), digits = 2),
    median = round(stats::median(x, na.rm = T)),
    min = round(min(x, na.rm = T)),
    max = round(max(x, na.rm = T))
  )
})
names(sample_summary) <- local_params$summarize_samples
feature_summary <- lapply(local_params$summarize_features, function(c) {
  x <- SummarizedExperiment::rowData(object_filtered)[, c]
  list(
    mean = round(mean(x, na.rm = T)),
    sd = round(sd(x, na.rm = T), digits = 2),
    median = round(stats::median(x, na.rm = T)),
    min = round(min(x, na.rm = T)),
    max = round(max(x, na.rm = T))
  )
})
names(feature_summary) <- local_params$summarize_features
```

In total, and after filtering, we obtained high quality transcriptomes from `r ncol(object_filtered)` samples across `r nrow(object_filtered)` features.

```{r scRNAseq-quality-control-G-summary-samples, results="asis"}
verbalized <- sapply(names(sample_summary), function(x) {
  stats <- sample_summary[[x]]
  paste0(x, " with a mean of ", stats$mean, " (+/- ", stats$sd, "), ranging from [", stats$min, ", ", stats$max, "] and a median of ", stats$median)
})
if(assertive.properties::is_non_empty(local_params$summarize_samples)) {
  cat("Samples can be further characterized by:  \n  \n")
  cat(paste(paste("* ", verbalized), collapse = "  \n"))
}
```

```{r scRNAseq-quality-control-G-summary-features, results="asis"}
verbalized <- sapply(names(feature_summary), function(x) {
  stats <- feature_summary[[x]]
  paste0(x, " with a mean of ", stats$mean, " (+/- ", stats$sd, "), ranging from [", stats$min, ", ", stats$max, "] and a median of ", stats$median)
})
if(assertive.properties::is_non_empty(local_params$summarize_features)) {
  cat("Features can be further characterized by:  \n  \n")
  cat(paste(paste("* ", verbalized), collapse = "  \n"))
}
```