Using the clusters information, and potential additional metadata as external criteria, spectra are labeled as to be picked for each cluster. Note that some spectra and therefore clusters can be explicitly removed (masked) from the picking decision if they have been previously picked or should be discarded, using logical columns in the metadata table. If no metadata are provided, the reference spectra of each cluster will be picked.
Usage
pick_spectra(
cluster_df,
metadata_df = NULL,
criteria_column = NULL,
hard_mask_column = NULL,
soft_mask_column = NULL,
is_descending_order = TRUE,
is_sorted = FALSE
)Arguments
- cluster_df
A tibble with clusters information from the delineate_with_similarity or the import_spede_clusters function.
- metadata_df
Optional tibble with relevant metadata to guide the picking process (e.g., OD600).
- criteria_column
Optional character indicating the column in
metadata_dfto be used as a criteria.- hard_mask_column
Column name in the
cluster_dformetadata_dftibble indicating whether the spectra, and the clusters to which they belong should be discarded (TRUE) or not (FALSE) before the picking decision.- soft_mask_column
Column name in the
cluster_dformetadata_dftibble indicating whether the spectra should be discarded (TRUE) or not (FALSE) before the picking decision.- is_descending_order
Optional logical indicating whether to sort the
criteria_columnfrom the highest-to-lowest value (TRUE) or lowest-to-highest (FALSE).- is_sorted
Optional logical to indicate that the
cluster_dfis already sorted by cluster based on (usually multiple) internal criteria to pick the first of each cluster. This flag is overridden if ametadata_dfis provided.
Value
A tibble with as many rows as cluster_df with an additional logical
column named to_pick to indicate whether the colony associated to the spectra
should be picked. If metadata_df is provided, then additional columns from
this tibble are added to the returned tibble.
See also
delineate_with_similarity, set_reference_spectra. For a useful utility function to soft-mask specific spectra: is_well_on_edge.
Examples
# 0. Load a toy example of a tibble of clusters created by
# the `delineate_with_similarity` function.
clusters <- readRDS(
system.file("clusters_tibble.RDS",
package = "maldipickr"
)
)
# 1. By default and if no other metadata are provided,
# the function picks reference spectra for each clusters.
#
# N.B: The spectra `name` and `to_pick` columns are moved to the left
# only for clarity using the `relocate()` function.
#
pick_spectra(clusters) %>%
dplyr::relocate(name, to_pick) # only for clarity
#> # A tibble: 6 × 7
#> name to_pick membership cluster_size SNR peaks is_reference
#> <chr> <lgl> <int> <int> <dbl> <dbl> <lgl>
#> 1 species1_G2 FALSE 1 4 5.09 21 FALSE
#> 2 species2_E11 FALSE 2 2 5.54 22 FALSE
#> 3 species2_E12 TRUE 2 2 5.63 23 TRUE
#> 4 species3_F7 FALSE 1 4 4.89 26 FALSE
#> 5 species3_F8 TRUE 1 4 5.56 25 TRUE
#> 6 species3_F9 FALSE 1 4 5.40 25 FALSE
# 2.1 Simulate OD600 values with uniform distribution
# for each of the colonies we measured with
# the Bruker MALDI Biotyper
set.seed(104)
metadata <- dplyr::transmute(
clusters,
name = name, OD600 = runif(n = nrow(clusters))
)
metadata
#> # A tibble: 6 × 2
#> name OD600
#> <chr> <dbl>
#> 1 species1_G2 0.364
#> 2 species2_E11 0.772
#> 3 species2_E12 0.735
#> 4 species3_F7 0.973
#> 5 species3_F8 0.740
#> 6 species3_F9 0.201
# 2.2 Pick the spectra based on the highest
# OD600 value per cluster
pick_spectra(clusters, metadata, "OD600") %>%
dplyr::relocate(name, to_pick) # only for clarity
#> # A tibble: 6 × 8
#> name to_pick membership cluster_size SNR peaks is_reference OD600
#> <chr> <lgl> <int> <int> <dbl> <dbl> <lgl> <dbl>
#> 1 species1_G2 FALSE 1 4 5.09 21 FALSE 0.364
#> 2 species2_E11 TRUE 2 2 5.54 22 FALSE 0.772
#> 3 species2_E12 FALSE 2 2 5.63 23 TRUE 0.735
#> 4 species3_F7 TRUE 1 4 4.89 26 FALSE 0.973
#> 5 species3_F8 FALSE 1 4 5.56 25 TRUE 0.740
#> 6 species3_F9 FALSE 1 4 5.40 25 FALSE 0.201
# 3.1 Say that the wells on the right side of the plate are
# used for negative controls and should not be picked.
metadata <- metadata %>% dplyr::mutate(
well = gsub(".*[A-Z]([0-9]{1,2}$)", "\\1", name) %>%
strtoi(),
is_edge = is_well_on_edge(
well_number = well, plate_layout = 96, edges = "right"
)
)
# 3.2 Pick the spectra after discarding (or soft masking)
# the spectra indicated by the `is_edge` column.
pick_spectra(clusters, metadata, "OD600",
soft_mask_column = "is_edge"
) %>%
dplyr::relocate(name, to_pick) # only for clarity
#> # A tibble: 6 × 10
#> name to_pick membership cluster_size SNR peaks is_reference OD600 well
#> <chr> <lgl> <int> <int> <dbl> <dbl> <lgl> <dbl> <int>
#> 1 species1… FALSE 1 4 5.09 21 FALSE 0.364 2
#> 2 species2… TRUE 2 2 5.54 22 FALSE 0.772 11
#> 3 species2… FALSE 2 2 5.63 23 TRUE 0.735 12
#> 4 species3… TRUE 1 4 4.89 26 FALSE 0.973 7
#> 5 species3… FALSE 1 4 5.56 25 TRUE 0.740 8
#> 6 species3… FALSE 1 4 5.40 25 FALSE 0.201 9
#> # ℹ 1 more variable: is_edge <lgl>
# 4.1 Say that some spectra were picked before
# (e.g., in the column F) in a previous experiment.
# We do not want to pick clusters with those spectra
# included to limit redundancy.
metadata <- metadata %>% dplyr::mutate(
picked_before = grepl("_F", name)
)
# 4.2 Pick the spectra from clusters without spectra
# labeled as `picked_before` (hard masking).
pick_spectra(clusters, metadata, "OD600",
hard_mask_column = "picked_before"
) %>%
dplyr::relocate(name, to_pick) # only for clarity
#> # A tibble: 6 × 11
#> name to_pick membership cluster_size SNR peaks is_reference OD600 well
#> <chr> <lgl> <int> <int> <dbl> <dbl> <lgl> <dbl> <int>
#> 1 species1… FALSE 1 4 5.09 21 FALSE 0.364 2
#> 2 species2… TRUE 2 2 5.54 22 FALSE 0.772 11
#> 3 species2… FALSE 2 2 5.63 23 TRUE 0.735 12
#> 4 species3… FALSE 1 4 4.89 26 FALSE 0.973 7
#> 5 species3… FALSE 1 4 5.56 25 TRUE 0.740 8
#> 6 species3… FALSE 1 4 5.40 25 FALSE 0.201 9
#> # ℹ 2 more variables: is_edge <lgl>, picked_before <lgl>