README.Rmd

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output: github_document
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knitr::opts_chunk$set(
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# amplify <img src='man/figures/logo.png' align="right" height="138" />
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**amplify** automates routine pcr-based tasks - including plate planning, dilution making, visualizing, and analyzing.

## Installation

You can install this package from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("KaiAragaki/amplify")
```
```{r message=FALSE, warning=FALSE}
library(amplify)
library(readxl)
library(knitr)
library(dplyr)
```


## Tidying qPCR data

Data exported from QuantStudio is fairly non-standard:

```{r untidy_pcr}
untidy_file_path <- system.file("extdata", "untidy-pcr-example.xls", package = "amplify")

untidy_file_path |> 
  read_excel() |>
  select(1:10) |> 
  head()
```

amplify provides `read_pcr` to read in and `tidy_lab` (from {mop}) to automatically tidy these files. `scrub` (also from {mop}) can convert `tidy_lab` objects to `data.frame`s

```{r tidy_pcr}
tidy_pcr <- untidy_file_path |> 
  read_pcr() |>
  tidy_lab()

tidy_pcr |>
  scrub() |>
  select(1:10) |> 
  head()
```

This works with both ddCt or standard curve result files.

## Plotting qPCR results

Tidied results can be plotted using `pcr_plot`

```{r pcr_plot}
tidy_pcr |> 
  pcr_rq("RD1") |> 
  pcr_plot()
```

Additionally, overviews of plate features can be done using `pcr_plate`

```{r}
tidy_pcr |> 
  pcr_plate_view("target_name")
```

More details can be found in the **Analyzing ddCt qPCR with amplify** vignette.

## Library Preparation Quantification

### Library Preparation Quantification Calculation

RNA library preparation results output from Quantstudio can be tidied using `pcr_tidy`:

```{r}
untidy_lib_path <- system.file("extdata", "untidy-standard-curve.xlsx", package = "amplify")
tidy_lib <- read_pcr(untidy_lib_path) |>
  tidy_lab(pad_zero = TRUE) 
tidy_lib |>
  scrub() |>
  select(1:10) |> 
  head()
```

Calculating the concentration of library (before dilution) can be performed using `pcr_lib_calc`:

```{r}
calc_lib <- pcr_lib_calc(tidy_lib) 

calc_lib |>
  scrub() |>
  filter(task == "UNKNOWN") |>
  select(sample_name, concentration) |>
  head()
```
### Library preparation quantification quality control

We can generate useful plots to determine the quality of the quantification run by first using `pcr_lib_qc`:

```{r}
qc <- calc_lib |> 
  pcr_lib_qc()
lapply(qc, head, n = 3)
```
These data, by themselves, are not particularly useful. However, a suite of QC plotting functions can be used upon these data to give insight, such as:

```{r}
qc |> pcr_lib_qc_plot_conc()
```

All QC plotting functions can be run and generate a report using `pcr_lib_qc_report`.


```{r eval = FALSE}
qc |> pcr_lib_qc_report("path/to/my/report.html")
```

More information about the plots available, as well as their interpretations, can be found in **Performing Library Quantification QC**