This function creates a synthetic limited proteolysis proteomics dataset that can be used to test functions while knowing the ground truth.

create_synthetic_data(
n_proteins,
frac_change,
n_replicates,
n_conditions,
method = "effect_random",
concentrations = NULL,
median_offset_sd = 0.05,
mean_protein_intensity = 16.88,
sd_protein_intensity = 1.4,
mean_n_peptides = 12.75,
size_n_peptides = 0.9,
mean_sd_peptides = 1.7,
sd_sd_peptides = 0.75,
mean_log_replicates = -2.2,
sd_log_replicates = 1.05,
effect_sd = 2,
dropout_curve_inflection = 14,
dropout_curve_sd = -1.2,
)

## Arguments

n_proteins a numeric value that specifies the number of proteins in the synthetic dataset. a numeric value that specifies the fraction of proteins that has a peptide changing in abundance. So far only one peptide per protein is changing. a numeric value that specifies the number of replicates per condition. a numeric value that specifies the number of conditions. a character value that specifies the method type for the random sampling of significantly changing peptides. If method = "effect_random", the effect for each condition is randomly sampled and conditions do not depend on each other. If method = "dose_response", the effect is sampled based on a dose response curve and conditions are related to each other depending on the curve shape. In this case the concentrations argument needs to be specified. a numeric vector of length equal to the number of conditions, only needs to be specified if method = "dose_response". This allows equal sampling of peptide intensities. It ensures that the same positions of dose response curves are sampled for each peptide based on the provided concentrations. a numeric value that specifies the standard deviation of normal distribution that is used for sampling of inter-sample-differences. Default is 0.05. a numeric value that specifies the mean of the protein intensity distribution. Default: 16.8. a numeric value that specifies the standard deviation of the protein intensity distribution. Default: 1.4. a numeric value that specifies the mean number of peptides per protein. Default: 12.75. a numeric value that specifies the dispersion parameter (the shape parameter of the gamma mixing distribution). Can be theoretically calculated as mean + mean^2/variance, however, it should be rather obtained by fitting the negative binomial distribution to real data. This can be done by using the optim function (see Example section). Default: 0.9. a numeric value that specifies the mean of peptide intensity standard deviations within a protein. Default: 1.7. a numeric value that specifies the standard deviation of peptide intensity standard deviation within a protein. Default: 0.75. a numeric value that specifies the meanlog and sdlog of the log normal distribution of replicate standard deviations. Can be obtained by fitting a log normal distribution to the distribution of replicate standard deviations from a real dataset. This can be done using the optim function (see Example section). Default: -2.2 and 1.05. a numeric value that specifies the standard deviation of a normal distribution around mean = 0 that is used to sample the effect of significantly changeing peptides. Default: 2. a numeric value that specifies the intensity inflection point of a probabilistic dropout curve that is used to sample intensity dependent missing values. This argument determines how many missing values there are in the dataset. Default: 14. a numeric value that specifies the standard deviation of the probabilistic dropout curve. Needs to be negative to sample a droupout towards low intensities. Default: -1.2. a logical value that determines if metadata such as protein coverage, missed cleavages and charge state should be sampled and added to the list.

## Value

A data frame that contains complete peptide intensities and peptide intensities with values that were created based on a probabilistic dropout curve.

## Examples

create_synthetic_data(
n_proteins = 10,
frac_change = 0.1,
n_replicates = 3,
n_conditions = 2
)
#> # A tibble: 756 × 14
#>    protein   peptide     condition sample peptide_intensi… change change_peptide
#>    <chr>     <chr>       <chr>     <chr>             <dbl> <lgl>  <lgl>
#>  1 protein_1 peptide_1_1 conditio… sampl…             16.0 TRUE   TRUE
#>  2 protein_1 peptide_1_1 conditio… sampl…             16.0 TRUE   TRUE
#>  3 protein_1 peptide_1_1 conditio… sampl…             16.0 TRUE   TRUE
#>  4 protein_1 peptide_1_1 conditio… sampl…             18.3 TRUE   TRUE
#>  5 protein_1 peptide_1_1 conditio… sampl…             18.2 TRUE   TRUE
#>  6 protein_1 peptide_1_1 conditio… sampl…             18.3 TRUE   TRUE
#>  7 protein_1 peptide_1_2 conditio… sampl…             16.3 TRUE   FALSE
#>  8 protein_1 peptide_1_2 conditio… sampl…             16.4 TRUE   FALSE
#>  9 protein_1 peptide_1_2 conditio… sampl…             16.4 TRUE   FALSE
#> 10 protein_1 peptide_1_2 conditio… sampl…             16.4 TRUE   FALSE
#> # … with 746 more rows, and 7 more variables: peptide_intensity_missing <dbl>,
#> #   coverage <dbl>, n_missed_cleavage <int>, charge <dbl>, pep_type <chr>,
#> #   peak_width <dbl>, retention_time <dbl>
# determination of mean_n_peptides and size_n_peptides parameters based on real data (count)
# example peptide count per protein
count <- c(6, 3, 2, 0, 1, 0, 1, 2, 2, 0)
theta <- c(mu = 1, k = 1)
negbinom <- function(theta) {
-sum(stats::dnbinom(count, mu = theta[1], size = theta[2], log = TRUE))
}
fit <- stats::optim(theta, negbinom)
fit
#> $par #> mu k #> 1.699882 2.124010 #> #>$value
#> [1] 17.50891
#>
#> $counts #> function gradient #> 57 NA #> #>$convergence
#> [1] 0
#>
#> $message #> NULL #> # determination of mean_log_replicates and sd_log_replicates parameters # based on real data (standard_deviations) # example standard deviations of replicates standard_deviations <- c(0.61, 0.54, 0.2, 1.2, 0.8, 0.3, 0.2, 0.6) theta2 <- c(meanlog = 1, sdlog = 1) lognorm <- function(theta2) { -sum(stats::dlnorm(standard_deviations, meanlog = theta2[1], sdlog = theta2[2], log = TRUE)) } fit2 <- stats::optim(theta2, lognorm) fit2 #>$par
#>    meanlog      sdlog
#> -0.7606984  0.6093069
#>
#> $value #> [1] 1.302677 #> #>$counts
#> $convergence #> [1] 0 #> #>$message
#>