amplify automates routine pcr-based tasks - including plate planning, dilution making, visualizing, and analyzing - so rather than thinking about your experiments themselves, you can think about what your experiments mean.
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
#> * ...
#> # 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 pcr_tidy to automatically tidy these files:
tidy_pcr <- untidy_file_path |>
pcr_tidy()
tidy_pcr |>
select(1:10) |>
head()
#> # A tibble: 6 × 10
#> well well_position omit sample_name target_name task reporter quencher
#> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr>
#> 1 26 B2 FALSE RD1 GENE1 UNKNOWN FAM NFQ-MGB
#> 2 27 B3 FALSE RD1 GENE1 UNKNOWN FAM NFQ-MGB
#> 3 28 B4 FALSE RD1 GENE1 UNKNOWN FAM NFQ-MGB
#> 4 29 B5 FALSE RD1 GENE2 UNKNOWN FAM NFQ-MGB
#> 5 30 B6 FALSE RD1 GENE2 UNKNOWN FAM NFQ-MGB
#> 6 31 B7 FALSE RD1 GENE2 UNKNOWN FAM NFQ-MGB
#> # … with 2 more variables: quantity <dbl>, quantity_mean <dbl>pcr_tidy 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 <- pcr_tidy(untidy_lib_path, pad_zero = TRUE)
tidy_lib |>
select(1:10) |>
head()
#> # A tibble: 6 × 10
#> well well_position omit sample_name target_name task reporter quencher
#> <chr> <chr> <lgl> <chr> <chr> <chr> <chr> <chr>
#> 1 1 A1 FALSE Standard 01 Target 1 STANDARD FAM NFQ-MGB
#> 2 2 A2 FALSE Standard 01 Target 1 STANDARD FAM NFQ-MGB
#> 3 3 A3 FALSE Standard 01 Target 1 STANDARD FAM NFQ-MGB
#> 4 5 A5 FALSE Sample 01 Target 1 UNKNOWN FAM NFQ-MGB
#> 5 6 A6 FALSE Sample 01 Target 1 UNKNOWN FAM NFQ-MGB
#> 6 8 A8 FALSE Sample 09 Target 1 UNKNOWN FAM NFQ-MGB
#> # … with 2 more variables: ct <dbl>, ct_mean <dbl>Calculating the concentration of library (before dilution) can be
performed using pcr_lib_calc:
calc_lib <- pcr_lib_calc(tidy_lib)
calc_lib |>
select(task, sample_name, concentration) |>
head()
#> # A tibble: 6 × 3
#> # Groups: task [2]
#> task sample_name concentration
#> <chr> <chr> <dbl>
#> 1 STANDARD Standard 01 NA
#> 2 STANDARD Standard 01 NA
#> 3 STANDARD Standard 01 NA
#> 4 UNKNOWN Sample 06 2039.
#> 5 UNKNOWN Sample 06 2039.
#> 6 UNKNOWN Sample 06 2039.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
#> # … with 12 more variables: slope <dbl>, efficiency <dbl>, r2 <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
#> # … with 12 more variables: slope <dbl>, efficiency <dbl>, r2 <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
#> # … with 12 more variables: slope <dbl>, efficiency <dbl>, r2 <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