amplify automates routine pcr-based tasks - including plate planning, dilution making, visualizing, and analyzing.
You can install this package from GitHub with:
# install.packages("devtools")
devtools::install_github("KaiAragaki/amplify")library(amplify)
library(readxl)
library(knitr)
library(dplyr)Data exported from QuantStudio is fairly non-standard:
untidy_file_path <- system.file("extdata", "untidy-pcr-example.xls", package = "amplify")
untidy_file_path |>
read_excel() |>
select(1:10) |>
head()
#> New names:
#> • `` -> `...3`
#> • `` -> `...4`
#> • `` -> `...5`
#> • `` -> `...6`
#> • `` -> `...7`
#> • `` -> `...8`
#> • `` -> `...9`
#> • `` -> `...10`
#> • `` -> `...11`
#> • `` -> `...12`
#> • `` -> `...13`
#> # A tibble: 6 × 10
#> `Block Type` `384-Well Block` ...3 ...4 ...5 ...6 ...7 ...8 ...9 ...10
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Calibration … Yes <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 Calibration … 01-13-2020 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 Calibration … Yes <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 Calibration … 01-13-2020 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 Calibration … Yes <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 Calibration … 01-13-2020 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>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.frames
tidy_pcr <- untidy_file_path |>
read_pcr() |>
tidy_lab()
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double(),
#> `Well Position` = col_character(),
#> Omit = col_logical(),
#> `Sample Name` = col_character(),
#> `Target Name` = col_character(),
#> Task = col_character(),
#> Reporter = col_character(),
#> Quencher = col_character(),
#> Quantity = col_logical(),
#> `Quantity Mean` = col_logical(),
#> `Quantity SD` = col_logical(),
#> CT = col_character(),
#> `Delta Ct` = col_logical(),
#> `Automatic Ct Threshold` = col_logical(),
#> `Automatic Baseline` = col_logical(),
#> Comments = col_logical()
#> )
#> ℹ Use `spec()` for the full column specifications.
tidy_pcr |>
scrub() |>
select(1:10) |>
head()
#> # A tibble: 6 × 10
#> .row .col well well_position omit sample_name target_name task reporter
#> <dbl> <dbl> <dbl> <chr> <lgl> <chr> <chr> <chr> <chr>
#> 1 1 1 NA <NA> NA <NA> <NA> <NA> <NA>
#> 2 1 2 NA <NA> NA <NA> <NA> <NA> <NA>
#> 3 1 3 NA <NA> NA <NA> <NA> <NA> <NA>
#> 4 1 4 NA <NA> NA <NA> <NA> <NA> <NA>
#> 5 1 5 NA <NA> NA <NA> <NA> <NA> <NA>
#> 6 1 6 NA <NA> NA <NA> <NA> <NA> <NA>
#> # ℹ 1 more variable: quencher <chr>This works with both ddCt or standard curve result files.
Tidied results can be plotted using pcr_plot
tidy_pcr |>
pcr_rq("RD1") |>
pcr_plot()
Additionally, overviews of plate features can be done using pcr_plate
tidy_pcr |>
pcr_plate_view("target_name")
More details can be found in the Analyzing ddCt qPCR with amplify vignette.
RNA library preparation results output from Quantstudio can be tidied
using pcr_tidy:
untidy_lib_path <- system.file("extdata", "untidy-standard-curve.xlsx", package = "amplify")
tidy_lib <- read_pcr(untidy_lib_path) |>
tidy_lab(pad_zero = TRUE)
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double(),
#> `Well Position` = col_character(),
#> Omit = col_logical(),
#> `Sample Name` = col_character(),
#> `Target Name` = col_character(),
#> Task = col_character(),
#> Reporter = col_character(),
#> Quencher = col_character(),
#> CT = col_character(),
#> `Automatic Ct Threshold` = col_logical(),
#> `Automatic Baseline` = col_logical(),
#> Comments = col_logical()
#> )
#> ℹ Use `spec()` for the full column specifications.
#> ! Multiple files in zip: reading ''[Content_Types].xml''
tidy_lib |>
scrub() |>
select(1:10) |>
head()
#> # A tibble: 6 × 10
#> .row .col well well_position omit sample_name target_name task reporter
#> <dbl> <dbl> <dbl> <chr> <lgl> <chr> <chr> <chr> <chr>
#> 1 1 1 1 A1 FALSE Standard 01 Target 1 STANDA… FAM
#> 2 1 2 2 A2 FALSE Standard 01 Target 1 STANDA… FAM
#> 3 1 3 3 A3 FALSE Standard 01 Target 1 STANDA… FAM
#> 4 1 4 NA <NA> NA <NA> <NA> <NA> <NA>
#> 5 1 5 5 A5 FALSE Sample 01 Target 1 UNKNOWN FAM
#> 6 1 6 6 A6 FALSE Sample 01 Target 1 UNKNOWN FAM
#> # ℹ 1 more variable: quencher <chr>Calculating the concentration of library (before dilution) can be
performed using pcr_lib_calc:
calc_lib <- pcr_lib_calc(tidy_lib)
calc_lib |>
scrub() |>
filter(task == "UNKNOWN") |>
select(sample_name, concentration) |>
head()
#> Adding missing grouping variables: `task`
#> # A tibble: 6 × 3
#> # Groups: task [1]
#> task sample_name concentration
#> <chr> <chr> <dbl>
#> 1 UNKNOWN Sample 06 2039.
#> 2 UNKNOWN Sample 06 2039.
#> 3 UNKNOWN Sample 06 2039.
#> 4 UNKNOWN Sample 12 1893.
#> 5 UNKNOWN Sample 12 1893.
#> 6 UNKNOWN Sample 12 1893.We can generate useful plots to determine the quality of the
quantification run by first using pcr_lib_qc:
qc <- calc_lib |>
pcr_lib_qc()
lapply(qc, head, n = 3)
#> $standards
#> # A tibble: 3 × 19
#> # Groups: sample_name [1]
#> sample_name task quantity_mean concentration quantity quant_actual dil
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Standard 01 STANDARD NA NA 6.80 6.80 0
#> 2 Standard 01 STANDARD NA NA 6.80 6.80 0
#> 3 Standard 01 STANDARD NA NA 6.80 6.80 0
#> # ℹ 12 more variables: slope <dbl>, efficiency <dbl>, r_superscript_2 <dbl>,
#> # ct <dbl>, no_po_mean <dbl>, no_po_sd <dbl>, keep <lgl>, keep_temp <lgl>,
#> # mean_adj <dbl>, sd_adj <dbl>, quant_adj <dbl>, z <dbl>
#>
#> $samples
#> # A tibble: 3 × 19
#> # Groups: sample_name [1]
#> sample_name task quantity_mean concentration quantity quant_actual dil
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Sample 06 UNKNOWN 2.04 2039. 2.06 2.06 NA
#> 2 Sample 06 UNKNOWN 2.04 2039. 2.12 2.12 NA
#> 3 Sample 06 UNKNOWN 2.04 2039. 1.94 1.94 NA
#> # ℹ 12 more variables: slope <dbl>, efficiency <dbl>, r_superscript_2 <dbl>,
#> # ct <dbl>, no_po_mean <dbl>, no_po_sd <dbl>, keep <lgl>, keep_temp <lgl>,
#> # mean_adj <dbl>, sd_adj <dbl>, quant_adj <dbl>, z <dbl>
#>
#> $sample_summary
#> # A tibble: 3 × 3
#> sample_name quantity_mean quant_adj
#> <chr> <dbl> <dbl>
#> 1 Sample 01 0.599 0.599
#> 2 Sample 02 1.05 1.05
#> 3 Sample 03 1.37 1.39
#>
#> $standard_summary
#> # A tibble: 3 × 4
#> sample_name dil quantity_mean quant_actual
#> <chr> <dbl> <dbl> <dbl>
#> 1 Standard 01 0 6.80 6.80
#> 2 Standard 02 9.27 0.680 0.734
#> 3 Standard 03 12.2 0.0680 0.0603
#>
#> $outliers
#> # A tibble: 3 × 19
#> # Groups: sample_name [1]
#> sample_name task quantity_mean concentration quantity quant_actual dil
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Standard 01 STANDARD NA NA 6.80 6.80 0
#> 2 Standard 01 STANDARD NA NA 6.80 6.80 0
#> 3 Standard 01 STANDARD NA NA 6.80 6.80 0
#> # ℹ 12 more variables: slope <dbl>, efficiency <dbl>, r_superscript_2 <dbl>,
#> # ct <dbl>, no_po_mean <dbl>, no_po_sd <dbl>, keep <lgl>, keep_temp <lgl>,
#> # mean_adj <dbl>, sd_adj <dbl>, quant_adj <dbl>, z <dbl>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:
qc |> pcr_lib_qc_plot_conc()
All QC plotting functions can be run and generate a report using
pcr_lib_qc_report.
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