sample_dataset: Sample scores for a random dataset

In compomics/search-all-assess-subset: An implementation of the Search All, Asses Subset strategy for FDR estimation shotgun proteomics.

Description

The scores are sampled from a two-component mixture distribution of Gaussians.

Usage

sample_dataset(H1_n = 160, H0_n = 40, decoy_n = H0_n,
  decoy_large_n = 2000, H0_mean = 2.75, H1_mean = 3.31, H0_sd = 0.13,
  H1_sd = 0.28, decoy_mean = H0_mean, decoy_sd = H0_sd,
  decoy_large_mean = H0_mean, decoy_large_sd = H0_sd)

Arguments

H1_n	number of correct subset target PSMs.
H0_n	number of incorrect subset target PSMs.
decoy_n	number of subset decoy PSMs.
decoy_large_n	number of non subset decoy PSMs.
H0_mean	mean of the incorrect subset target PSM distribution.
H1_mean	mean of the correct subset target PSM distribution.
H0_sd	sd of the incorrect subset target PSM distribution.
H1_sd	sd of the correct subset target PSM distribution.
decoy_mean	mean of the subset decoy PSM distribution.
decoy_sd	sd of the subset decoy PSM distribution.
decoy_large_mean	mean of the non subset decoy PSM distribution.
decoy_large_sd	sd of the non subset decoy PSM distribution.

Value

dataframe with 4 columns:

score

score assigned to the peptide to spectrum match (PSM).

decoy

TRUE if decoy PSM, FALSE otherwise.

H0

TRUE if incorrect target PSM, FALSE otherwise.

subset

TRUE if PSM belongs to the subset in interest, FALSE or otherwise.

Examples

## Simulate a dataset with 140 correct target subset PSMs, 60 incorrect target subset PSMS,
##60 decoy subset PSMs and 2000 additional decoy PSMs.
## In this first example, incorrect subset target and decoy PSMs have a similar distribution
set.seed(10)
d = sample_dataset(H1_n = 140,H0_n = 60, decoy_n = 60 ,decoy_large_n = 2000,
                   H0_mean = 2.7, H1_mean = 3, decoy_mean = 2.7, decoy_large_mean = 2.7)
# visualize the decoy distribution and the correct and incorrect target distribution
plot(density(d$score[d$decoy]), xlim = c(2,4), xlab = 'score', ylab = 'density')
lines(density(d$score[!d$decoy & d$H0]), col = 'red')
lines(density(d$score[!d$decoy & !d$H0]), col = 'green')
legend('topright', c('decoys', 'incorrect targets', 'correct targets'),
       text.col = c('black', 'red', 'green'))
## In this first example, incorrect subset target and decoy PSMs have not a similar distribution
## because the additional set of decoy PSMs are not representative for the incorrect subset PSMs.
set.seed(10)
d = sample_dataset(H1_n = 140,H0_n = 60, decoy_n = 60 ,decoy_large_n = 2000,
                   H0_mean = 2.7, H1_mean = 3, decoy_mean = 2.7, decoy_large_mean = 2.6)
# visualize the decoy distribution and the correct and incorrect target distribution
plot(density(d$score[d$decoy]), xlim = c(2,4), xlab = 'score', ylab = 'density')
lines(density(d$score[!d$decoy & d$H0]), col = 'red')
lines(density(d$score[!d$decoy & !d$H0]), col = 'green')
legend('topright', c('decoys', 'incorrect targets', 'correct targets'),
       text.col = c('black', 'red', 'green'))

compomics/search-all-assess-subset documentation built on May 13, 2019, 9:55 p.m.