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The {maldipickr} package helps microbiologists reduce
duplicate/clonal bacteria from their cultures and eventually exclude
previously selected bacteria. {maldipickr} achieve this
feat by grouping together data from MALDI Biotyper and helps choose
representative bacteria from each group using user-relevant metadata – a
process known as cherry-picking.
{maldipickr} cherry-picks bacterial isolates with MALDI
Biotyper:
First make sure {maldipickr} is installed and loaded,
alternatively follow
the instructions to install the package.
Cherry-picking four isolates based on their taxonomic identification
by the MALDI Biotyper is done in a few steps with
{maldipickr}.
We import an example Biotyper CSV report and glimpse at the table.
report_tbl <- read_biotyper_report(
system.file("biotyper_unknown.csv", package = "maldipickr")
)
report_tbl %>%
dplyr::select(name, bruker_species, bruker_log) %>% knitr::kable()| name | bruker_species | bruker_log |
|---|---|---|
| unknown_isolate_1 | not reliable identification | 1.33 |
| unknown_isolate_2 | not reliable identification | 1.40 |
| unknown_isolate_3 | Faecalibacterium prausnitzii | 1.96 |
| unknown_isolate_4 | Faecalibacterium prausnitzii | 2.07 |
Delineate clusters from the identifications after filtering the reliable ones and cherry-pick one representative spectra.
Unreliable identifications based on the log-score are replaced by “not reliable identification”, but stay tuned as they do not represent the same isolates!
report_tbl <- report_tbl %>%
dplyr::mutate(
bruker_species = dplyr::if_else(bruker_log >= 2, bruker_species,
"not reliable identification")
)
knitr::kable(report_tbl)| name | sample_name | hit_rank | bruker_quality | bruker_species | bruker_taxid | bruker_hash | bruker_log |
|---|---|---|---|---|---|---|---|
| unknown_isolate_1 | NA | 1 | - | not reliable identification | NA | 3e920566-2734-43dd-85d0-66cf23a2d6ef | 1.33 |
| unknown_isolate_2 | NA | 1 | - | not reliable identification | NA | 88a85875-eeb5-4858-966e-98a077325dc3 | 1.40 |
| unknown_isolate_3 | NA | 1 | + | not reliable identification | 137408536 | 2d266f20-5428-428d-96ec-ddd40200794b | 1.96 |
| unknown_isolate_4 | NA | 1 | +++ | Faecalibacterium prausnitzii | 137408536 | 2d266f20-5428-428d-96ec-ddd40200794b | 2.07 |
The chosen ones are indicated by to_pick column.
report_tbl %>%
delineate_with_identification() %>%
pick_spectra(report_tbl, criteria_column = "bruker_log") %>%
dplyr::relocate(name, to_pick, bruker_species) %>%
knitr::kable()
#> Generating clusters from single report| name | to_pick | bruker_species | membership | cluster_size | sample_name | hit_rank | bruker_quality | bruker_taxid | bruker_hash | bruker_log |
|---|---|---|---|---|---|---|---|---|---|---|
| unknown_isolate_1 | TRUE | not reliable identification | 2 | 1 | NA | 1 | - | NA | 3e920566-2734-43dd-85d0-66cf23a2d6ef | 1.33 |
| unknown_isolate_2 | TRUE | not reliable identification | 3 | 1 | NA | 1 | - | NA | 88a85875-eeb5-4858-966e-98a077325dc3 | 1.40 |
| unknown_isolate_3 | TRUE | not reliable identification | 4 | 1 | NA | 1 | + | 137408536 | 2d266f20-5428-428d-96ec-ddd40200794b | 1.96 |
| unknown_isolate_4 | TRUE | Faecalibacterium prausnitzii | 1 | 1 | NA | 1 | +++ | 137408536 | 2d266f20-5428-428d-96ec-ddd40200794b | 2.07 |
In parallel to taxonomic identification reports,
{maldipickr} process spectra data. Make sure
{maldipickr} is installed and loaded, alternatively follow
the instructions to install the package.
Cherry-picking six isolates from three species based on their spectra
data obtained from the MALDI Biotyper is done in a few steps with
{maldipickr}.
We set up the directory location of our example spectra data, but
adjust for your requirements. We import and process the spectra which
gives us a named list of three objects: spectra, peaks and metadata
(more details in Value section of process_spectra()).
Delineate spectra clusters using Cosine similarity and cherry-pick
one representative spectra. The chosen ones are indicated by
to_pick column.
processed %>%
list() %>%
merge_processed_spectra() %>%
coop::tcosine() %>%
delineate_with_similarity(threshold = 0.92) %>%
set_reference_spectra(processed$metadata) %>%
pick_spectra() %>%
dplyr::relocate(name, to_pick) %>%
knitr::kable()| name | to_pick | membership | cluster_size | SNR | peaks | is_reference |
|---|---|---|---|---|---|---|
| species1_G2 | FALSE | 1 | 4 | 5.089590 | 21 | FALSE |
| species2_E11 | FALSE | 2 | 2 | 5.543735 | 22 | FALSE |
| species2_E12 | TRUE | 2 | 2 | 5.633540 | 23 | TRUE |
| species3_F7 | FALSE | 1 | 4 | 4.889949 | 26 | FALSE |
| species3_F8 | TRUE | 1 | 4 | 5.558884 | 25 | TRUE |
| species3_F9 | FALSE | 1 | 4 | 5.398429 | 25 | FALSE |
This provides only a brief overview of the features of
{maldipickr}, browse the other vignettes to learn more
about additional features.
sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.6 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Europe/Berlin
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] maldipickr_1.3.1
#>
#> loaded via a namespace (and not attached):
#> [1] vctrs_0.6.4 cli_3.6.1 knitr_1.48
#> [4] rlang_1.1.4 xfun_0.44 coop_0.6-3
#> [7] purrr_1.0.2 generics_0.1.3 jsonlite_1.8.7
#> [10] glue_1.6.2 htmltools_0.5.6.1 sass_0.4.7
#> [13] fansi_1.0.5 rmarkdown_2.28 tibble_3.2.1
#> [16] evaluate_0.22 jquerylib_0.1.4 fastmap_1.1.1
#> [19] yaml_2.3.7 lifecycle_1.0.4 compiler_4.3.1
#> [22] dplyr_1.1.4 pkgconfig_2.0.3 tidyr_1.3.0
#> [25] readBrukerFlexData_1.9.1 rstudioapi_0.15.0 digest_0.6.33
#> [28] R6_2.5.1 tidyselect_1.2.1 utf8_1.2.3
#> [31] pillar_1.9.0 parallel_4.3.1 magrittr_2.0.3
#> [34] bslib_0.5.1 withr_2.5.1 tools_4.3.1
#> [37] MALDIquant_1.22.1 cachem_1.0.8These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.