Filters Features based on the presence of MSn fragments. This can help, for example with the identification of potential homologous molecules.
Usage
filter_msn(
data,
fragments,
min_found,
tolerance = 5,
tolerance_type = "ppm",
show_progress = TRUE
)Arguments
- data
A data frame containing MSn spectra.
- fragments
A numeric. Exact mass of the fragment(s) to filter by.
- min_found
How many of the
fragmentsmust be found in order to keep the row? Ifmin_found = length(fragments), all fragments must be found.- tolerance
A numeric. The tolerance to apply to the fragments. Either an absolute value in Da (if
tolerance_type = "absolute") or in ppm (iftolerance_type = "ppm").- tolerance_type
Either
"absolute"or"ppm". Should the tolerance be an absolute value or in ppm?- show_progress
A
logicalindicating whether the progress of the filtering should be printed to the console. Only important for large tibbles.
Examples
# all of the given fragments (3) must be found
# returns the first row of toy_mgf
toy_mgf %>%
filter_msn(fragments = c(12.3456, 23.4567, 34.5678), min_found = 3)
#> # A tibble: 1 × 5
#> VARIABLEONE VARIABLETWO VARIABLETHREE PEPMASS MSn
#> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 1.1 1.2 1.3 45.7 <tibble [3 × 2]>
# all of the given fragments (3) must be found
# returns an empty tibble because the third fragment
# of row 1 (34.5678)
# is outside of the tolerance (5 ppm):
# Lower bound:
# 34.5688 - 34.5688 * 5 / 1000000 = 34.5686
# Upper bound:
# 34.5688 + 34.5688 * 5 / 1000000 = 34.5690
toy_mgf %>%
filter_msn(fragments = c(12.3456, 23.4567, 34.5688), min_found = 3)
#> # A tibble: 0 × 5
#> # ℹ 5 variables: VARIABLEONE <dbl>, VARIABLETWO <dbl>, VARIABLETHREE <dbl>,
#> # PEPMASS <dbl>, MSn <list>
# only 2 of the 3 fragments must be found
# returns the first row of toy_mgf
toy_mgf %>%
filter_msn(fragments = c(12.3456, 23.4567, 34.5688), min_found = 2)
#> # A tibble: 1 × 5
#> VARIABLEONE VARIABLETWO VARIABLETHREE PEPMASS MSn
#> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 1.1 1.2 1.3 45.7 <tibble [3 × 2]>