Run bistro step-by-step • bistro

If you haven’t yet, please check out vignette("bistro") before reading this vignette. It will provide you with more background and context, and take you through the outputs in more detail than we do here. Then if you want to learn how to run sub-functions of bistro individually, or run bistro step-by-step, you’re in the right place.

We will go through the following steps to run bistro step-by-step using the example data provided in the bistro package:

First, you’ll have to load the bistro library:

library(bistro)
#> Loading required package: euroformix

Calculate human population allele frequencies

If needed, you can first calculate population allele frequencies from your human profile database:

pop_freqs_computed <- calc_allele_freqs(human_profiles)

We will use the built-in population allele frequencies here since they are more accurate than computing allele frequencies for only the three example profiles in human_profiles.

Prepare input STR profiles

Before calculating log10LRs, you must ensure that the bloodmeal and human profiles are prepared correctly. You can do this using prep_bloodmeal_profiles() and prep_human_profiles(), respectively.

Both functions:

Removes markers the user does not want to use
Removes duplicate rows to ensure that homozygous alleles are only included once

prep_bloodmeal_profiles() also optionally filters peakes below a user-defined threshold

prep_human_profiles() also optionally removes identical twins (which cannot be resolved)

bm_profs <- prep_bloodmeal_profiles(bloodmeal_profiles, peak_thresh = 200)
#> Removing 6 peaks under the threshold of 200 RFU.
#> For 1/4 bloodmeal ids, all peaks are below the threshold
hu_profs <- prep_human_profiles(human_profiles)

Calculate log10LRs for bloodmeal-human pairs

Next, you can use the prepared profiles to compute log10LRs for each bloodmeal-human pair (or a subset of pairs). Note that to identify matches, you will have to have the log10LRs between each bloodmeal and all human profiles in the reference database.

Here is an example of how to compute the log10LRs for one bloodmeal and all humans in the database:

log10_lrs <- calc_log10_lrs(bm_profs,
  hu_profs,
  bloodmeal_ids = "evid1",
  pop_allele_freqs = pop_allele_freqs,
  kit = "ESX17",
  peak_thresh = 200
)
#> 1/17 markers in kit but not in bloodmeal_profiles: AMEL
#> 1/17 markers in kit but not in human_profiles: AMEL
#> # bloodmeal ids: 1
#> # human ids: 3
#> Bloodmeal id 1/1
#> Human id 1/3
#> Human id 2/3
#> Human id 3/3

Identify bloodmeal-human matches

Next, we can identify matches to the bloodmeal:

matches <- identify_matches(log10_lrs)