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Bugfix/corems conversion #31

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Merged
clabornd merged 3 commits into from
Dec 4, 2024
Merged

Bugfix/corems conversion #31

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f156fba
fix order-sensitive parsing of elemental counts from formula in CoreM…
clabornd Dec 2, 2024
d8338ed
fix plotting bug for CoreMS data where some iso samples were missing
clabornd Dec 2, 2024
db62ad5
remove CoreMS formula column and let as.peakData auto-create it
clabornd Dec 2, 2024
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11 changes: 8 additions & 3 deletions R/coreMSDataToFtmsData.R
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Original file line number Diff line number Diff line change
Expand Up @@ -82,9 +82,14 @@ coreMSDataToFtmsData <- function(cmsObj) {
`Calibrated m/z` = mean(.data[[calib_mass]])) %>%
dplyr::select(Mass, .data[[formula]], `Calibrated m/z`, .data[[calc_mass]],
.data[[heteroatom]], .data[[ion_type]]) %>%
dplyr::distinct() %>%
tidyr::separate(col = .data[[formula]], into = element_cnames, sep = " ") %>%
dplyr::mutate_at(element_cnames, ~ as.integer(gsub("[^0-9]", "", .x))) %>%
dplyr::distinct()

counts_df = lapply(element_cnames, ftmsRanalysis:::atom_count_vectorizer, e_meta[[formula]]) %>% as.data.frame(col.names = element_cnames)

e_meta[element_cnames] <- counts_df

e_meta <- e_meta %>%
dplyr::ungroup() %>%
dplyr::select(Mass, all_of(element_cnames), `Calibrated m/z`, .data[[calc_mass]],
.data[[heteroatom]], .data[[ion_type]]) %>%
dplyr::arrange(Mass)
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23 changes: 11 additions & 12 deletions R/plot.CoreMSData.R
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Expand Up @@ -37,21 +37,20 @@ plot.CoreMSData <- function(x,
sample_id <- attr(x, "cnames")$file_cname
mass_id <- attr(x, "cnames")$calc_mass_cname

unique_masses_per_sample <- x$monoiso_data %>%
dplyr::group_by(dplyr::pull(x$monoiso_data, sample_id)) %>%
dplyr::distinct(dplyr::pull(x$monoiso_data, mass_id)) %>%
dplyr::tally() %>%
dplyr::rename(Sample = `dplyr::pull(x$monoiso_data, sample_id)`, Monoisotopic = n)
unique_masses_per_sample <- x$monoiso_data %>%
dplyr::group_by(!!rlang::sym(sample_id)) %>%
dplyr::distinct(!!rlang::sym(mass_id)) %>%
dplyr::tally() %>% dplyr::rename(Sample = sample_id, Monoisotopic = n)

Isotopic <- x$iso_data %>%
dplyr::group_by(dplyr::pull(x$iso_data, sample_id)) %>%
dplyr::distinct(dplyr::pull(x$iso_data, mass_id)) %>%
dplyr::tally() %>%
dplyr::pull(n)
Isotopic <- x$iso_data %>% dplyr::group_by(!!sym(sample_id)) %>%
dplyr::distinct(!!sym(mass_id)) %>%
dplyr::tally() %>%
dplyr::rename(Sample = sample_id, Isotopic = n)

unique_masses_per_sample <- cbind(unique_masses_per_sample, Isotopic) %>%
unique_masses_per_sample <- dplyr::full_join(unique_masses_per_sample, Isotopic) %>%
tidyr::pivot_longer(cols = c(!Sample), names_to = "Peak_type", values_to = "Count") %>%
dplyr::mutate(Peak_type = factor(Peak_type, levels = c('Monoisotopic', 'Isotopic'))) # set factor levels manually so bars will be in descending order by count
dplyr::mutate(Peak_type = factor(Peak_type, levels = c("Monoisotopic", "Isotopic")))
# set factor levels manually so bars will be in descending order by count

plot <- unique_masses_per_sample %>%
ggplot2::ggplot(ggplot2::aes(x = Sample,
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