qp is a toolkit for working with MicroBCA assays and their analyses. It leverages work from some of my other projects, like gplate and mop.

The old process was to slice and dice data in Excel. While it certainly worked, it was subjective (outliers removed by eye), tedious (the same analysis every time), and was a leak in the ‘flow of data’, as excel-based data munging often is: no one knows how (and certainly not why) you did something, only that something was done.

Shiny App

qp was created as an attempt to fix all these issues. Furthermore, to facilitate its use, I’ve used it in a Shiny app. However, it’s a fully (perhaps even more) capable package without a GUI.

Usage

library(qp)
head(absorbances) # Some example absorbances

  .row .col   .abs sample_type index
1    1    1 0.0707    standard     1
2    2    1 0.0786    standard     1
3    3    1 0.0714    standard     1
4    1    2 0.0795    standard     2
5    2    2 0.0799    standard     2
6    3    2 0.0805    standard     2

To get a feel for what it might look like on the bench:

qp_plot_plate(absorbances)

# These arguments are the defaults
out <- qp(
  absorbances,
  replicate_orientation = "v",
  sample_names = paste0("Sample_", 1:8),
  remove_empty = TRUE,
  ignore_outliers = "all",
  standard_scale = c(0, 2^((2:7) - 5)),
  n_replicates = 3,
  wavelength = 562
)

Warning: `sample_type` contains values other than `standard` and `unknown`
! These values may be ignored downstream!
`sample_type` contains values other than `standard` and `unknown`
! These values may be ignored downstream!

out

$fit

Call:
stats::lm(formula = .log2_conc ~ .log2_abs, data = fit_data)

Coefficients:
(Intercept)    .log2_abs  
      2.378        0.850  


$qp
# A tibble: 45 × 13
    .row  .col   .abs sample_type index .conc .is_outlier  .mean .log2_abs
   <int> <dbl>  <dbl> <fct>       <dbl> <dbl> <lgl>        <dbl>     <dbl>
 1     1     1 0.0707 standard        1 0     FALSE       0.0710     -3.82
 2     2     1 0.0786 standard        1 0     TRUE        0.0710     -3.67
 3     3     1 0.0714 standard        1 0     FALSE       0.0710     -3.81
 4     1     2 0.0795 standard        2 0.125 FALSE       0.0800     -3.65
 5     2     2 0.0799 standard        2 0.125 FALSE       0.0800     -3.65
 6     3     2 0.0805 standard        2 0.125 FALSE       0.0800     -3.63
 7     1     3 0.0999 standard        3 0.25  FALSE       0.0977     -3.32
 8     2     3 0.0955 standard        3 0.25  FALSE       0.0977     -3.39
 9     3     3 0.0976 standard        3 0.25  FALSE       0.0977     -3.36
10     1     4 0.151  standard        4 0.5   TRUE        0.148      -2.72
# ℹ 35 more rows
# ℹ 4 more variables: .pred <dbl>, .pred_conc <dbl>, .pred_conc_mean <dbl>,
#   .sample_name <chr>

We can make a plot of the fit:

qp_plot_standards(out)

We can also calculate dilutions:

out |>
  qp_summarize() |>
  qp_dilute()

`target_conc` is missing, using lowest sample concentration

# A tibble: 15 × 7
   .sample_name sample_type .pred_conc_mean sample_to_add  add_to .target_conc
   <chr>        <fct>                 <dbl>         <dbl>   <dbl>        <dbl>
 1 Standard 1   standard             0.0493        282    -267           0.926
 2 Standard 2   standard             0.107         129.   -114.          0.926
 3 Standard 3   standard             0.220          63.2   -48.2         0.926
 4 Standard 4   standard             0.522          26.6   -11.6         0.926
 5 Standard 5   standard             0.902          15.4    -0.4         0.926
 6 Standard 6   standard             2.27            6.11    8.89        0.926
 7 Standard 7   standard             3.79            3.67   11.3         0.926
 8 Sample_1     unknown              2.16            6.42    8.58        0.926
 9 Sample_2     unknown              2.12            6.54    8.46        0.926
10 Sample_3     unknown              2.41            5.77    9.23        0.926
11 Sample_4     unknown              2.10            6.63    8.37        0.926
12 Sample_5     unknown              2.64            5.26    9.74        0.926
13 Sample_6     unknown              2.69            5.17    9.83        0.926
14 Sample_7     unknown              1.77            7.84    7.16        0.926
15 Sample_8     unknown              0.926          15       0           0.926
# ℹ 1 more variable: .target_vol <dbl>

There’s plenty more that can be done (particularly with less typical workflows) - see the vignette for more information