01-intro.Rmd

```{r, parameters-and-defaults, include = FALSE}
module <- "scRNAseq"
section <- "intro"
parameters_and_defaults <- list(
  scdata_object = structure(
    "rds/scData.rds",
    type = "character",
    choices = NA,
    several.ok = FALSE
  ),
  tabulate = structure(
    c(),
    type = "character",
    choices = NA,
    several.ok = TRUE
  )
)
```
```{r parameter-merge, include = FALSE}
local_params <- module %>%
  options() %>%
  magrittr::extract2(module) %>%
  magrittr::extract2(section) %>%
  ReporteR.base::validate_params(parameters_and_defaults)
```

```{r scRNAseq-intro-load, include=FALSE}
assertive.files::is_existing_file(local_params$scdata_object)

managed_objects$paths$object <- list(
  path = local_params$scdata_object)

path_filtered_object <- ReporteR.scRNAseq::make_path_filtered_object(local_params$scdata_object)
managed_objects$paths$object_filtered <- list(
  path = path_filtered_object)

object <- readRDS(local_params$scdata_object) %>%
  ReporteR.base::flag_persistent()
```

# Single-cell RNA Sequencing

Single-cell RNA sequencing (*scRNA-seq*) has been a major breakthrough since the late 2000's [@tang_2009] and overcomes limitations from bulk RNA sequencing (*RNA-seq*) by measuring gene expression in individual cells opposed to measuring the average expression across a large population of cells. This enabled the study of new biological questions in which cell-specific changes in the transcriptome are impotant, e.g. cell type identification, heterogeneity of cell responses, stochasticity of gene expression, inference of gene regulatory networks across the cells. 

Computational analysis of data from *scRNA-seq* is very variable and depends on the experimental design as well as specific research questions at hand.
Striving for a robust, fully scriptable and reproducible data exploitation, an in--house developed, `R`--based [@r_core_team_2018], data analytical
pipeline was used to process quantitative data for downstream bioinformatic analysis.

The underlying dataset consists of **`r nrow(object)` features** (genes) measured across **`r ncol(object)` samples** (individual cells).

```{r scRNAseq-intro-table, echo = FALSE, child = system.file(file.path('content', '01-intro-A-table.Rmd'), package = 'ReporteR.scRNAseq', mustWork = TRUE), R.options = params, eval = ifelse(exists('local_params'), assertive.properties::is_non_empty(local_params$tabulate), FALSE)}
```

```{r scRNAseq-intro-terminal-cleanup, include = FALSE}
ReporteR.base::purge_nonpersistent()
```