Plot diagnostic plots to evaluate assumptions from the search all, search subset strategy.

Four diagnostic plots are created:

a

pi0plot according the number of subset target and decoy PSMs.

b

PPplot of the decoy distribution against the subset target distribution.

c

PPplot of the decoy distribution against the subset decoy distribution.

d

PPplot of the subset decoy distribution against the subset target distribution.

plot_diag(df, score_higher = TRUE)

Arguments

df	dataframe with at least 3 columns: score score assigned to the peptide to spectrum match (PSM). subset TRUE if PSM belongs to the subset in interest, FALSE otherwise. decoy TRUE if decoy PSM, FALSE otherwise.
score_higher	TRUE if a higher score means a better PSM. Additional columns are allowed but ignored. Target and decoy PSMs are assumbed to be from a competitive target decoy database search.

Value

ggplot object.

Examples

## Simulate a dataset with 140 correct target subset PSMs, 60 incorrect target subset PSMS,
## 60 decoy subset PSMs and 2000 additional decoy 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 = 3.2, decoy_mean = 2.7, decoy_large_mean = 2.7)
##pi_0 can be estimated with the target-decoy aproach

plot_diag(d)
#> $pi0plot
#> 
#> $decoyall_targetsubset
#> 
#> $decoyall_decoy_subset
#> 
#> $decoysubset_target_subset
#> 
#> $all
#>