function to find top 10 recalibrant series #44
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R/FindRecalSeries.R
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| tolerance <- 100 | ||
| global_min <- min(df$Min.Mass.Range) + tolerance | ||
| global_max <- max(df$Max.Mass.Range) - tolerance | ||
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| # Create all combinations of ions | ||
| iter <- combinations(nrow(df), 5, v = 1:nrow(df)) | ||
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| # Helper dataframe with information which combinations do cover range | ||
| coversRange <- data.frame(iter, coversRange = 0) | ||
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| # Check if the combinations cover the whole data range | ||
| for (i in 1:nrow(iter)) { | ||
| comb <- iter[i, ] | ||
| subset <- df[comb, ] | ||
| local_min <- min(subset$Min.Mass.Range) | ||
| local_max <- max(subset$Max.Mass.Range) | ||
| if (local_min <= global_min & local_max >= global_max) { | ||
| coversRange$coversRange[i] <- 1 | ||
| } | ||
| } | ||
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| # Subset only those, which cover whole range | ||
| coversRangeTrue <- coversRange[coversRange$coversRange == 1, ] |
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This should also be its own function which creates the combinations and filters them based on the coverage criteria. The tall peaks every 100mz should also be included as a optional criterion - so a boolean parameter should be added.
R/FindRecalSeries.R
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| for (i in 1:nrow(coversRangeTrue)) { | ||
| comb <- iter[i, ] | ||
| subset <- df[comb, ] | ||
| comb_score <- score_combination(subset) | ||
| scores <- append(scores, list(comb_score)) | ||
| } |
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This section can be parallelized single function call
R/FindRecalSeries.R
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| # Append all scored combinations into a dataframe | ||
| scores_df <- do.call(rbind, lapply(scores, as.data.frame)) |
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This can be part of the combination statement of the parallel for loop
R/FindRecalSeries.R
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| local_min <- min(subset$Min.Mass.Range) | ||
| local_max <- max(subset$Max.Mass.Range) | ||
| if (local_min <= global_min & local_max >= global_max) { | ||
| coversRange$coversRange[i] <- 1 | ||
| } |
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This first rough check should actually be removed and replaced with the finer detailed check and the actual coverage percentage which is currently part of the score calculation.
R/FindRecalSeries.R
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| # Filter for the 10 top scoring series | ||
| finalSeries <- scores_df %>% | ||
| filter(coverage_percent > 90) %>% | ||
| rowwise() %>% | ||
| mutate(sum_score = sum(total_abundance, total_series_length, peak_proximity, peak_distance_proximity, coverage_percent)) %>% | ||
| arrange(desc(sum_score)) %>% | ||
| filter(!duplicated(series)) %>% | ||
| head(10) |
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This should be moved into its own function to choose the best series and the coverage percent should be part of the combination filtering.
R/FindRecalSeries.R
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| head(10) | ||
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| # Return the top scoring series | ||
| return(finalSeries) |
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this should be kept as the main output, maybe we can also do something with the scoring and create some report or so?
R/FindRecalSeries.R
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| mutate(sum_score = sum(total_abundance, total_series_length, peak_proximity, peak_distance_proximity, coverage_percent)) %>% | ||
| arrange(desc(sum_score)) %>% | ||
| filter(!duplicated(series)) %>% | ||
| head(10) |
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The filling in to 10 should be removed or actually made optional.
R/FindRecalSeries.R
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| #' @param df An output from RecalList, containing recalibrant CH2 series. | ||
| #' @return A dataframe of 10 best-scoring series. | ||
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missing descriptions for the other parameters
This PR implements a function to find 10 most suitable recalibrant series and resolves #22