Summary statistics table construction

summary-statistics-table-construction

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->

Summary

Construct a summary table that aggregates counts or statistics across multiple filter conditions applied to a compound dataset. This skill is used to compare how data volume changes when different threshold criteria are applied to mass spectrometry results.

When to use

When you need to quantify and compare the effect of multiple filtering thresholds (e.g., Match.Factor ≥65, ≥80, ≥90) on the size of a retained compound set. This is appropriate when the analysis goal is to demonstrate filter sensitivity or to select an optimal threshold that balances data retention with quality criteria.

When NOT to use

• The input data does not contain a Match.Factor column or equivalent quality metric.
• The goal is to retain all compounds without filtering; threshold comparison is not relevant.
• You need to summarize properties of individual compounds (e.g., molecular weight, retention time) rather than counts across threshold conditions.

Inputs

• CSV file with columns 'Compound.Name' and 'Match.Factor' (e.g., standard_data.csv)
• One or more numeric threshold values for Match.Factor filtering

Outputs

• Summary table (data frame or CSV) with columns for threshold condition label, threshold value, and compound count
• Optionally: a saved CSV file containing the summary comparison

How to apply

Load the data file (standard_data.csv) into R and extract the relevant columns (Compound.Name and Match.Factor). Apply each filter threshold independently—for example, Match.Factor >= 65, >= 80, and >= 90—and extract unique compound names satisfying each condition (labeled COND-mf65, COND-mf80, COND-mf90). Count the number of unique compounds retained under each threshold. Construct a summary table with one row per threshold condition and columns for the threshold value and the corresponding compound count. Save the result as a CSV file. The rationale is that visualizing counts side-by-side allows rapid assessment of how stringent each threshold is and supports informed selection of the most appropriate filter level for downstream analysis.

Related tools

• R (Language and environment for loading CSV, filtering by Match.Factor threshold, counting unique compounds, and constructing the summary table)
• uafR (R package providing helper functions (spreadOut, mzExacto) for preprocessing and filtering mass spectrometry data; filtering logic can be applied before or after using these functions) — https://github.com/castratton/uafR

Examples

input_dat = read.csv('standard_data.csv'); counts = c(length(unique(input_dat$Compound.Name[input_dat$Match.Factor >= 65])), length(unique(input_dat$Compound.Name[input_dat$Match.Factor >= 80])), length(unique(input_dat$Compound.Name[input_dat$Match.Factor >= 90]))); summary_tbl = data.frame(Threshold = c('>=65', '>=80', '>=90'), Compound.Count = counts); write.csv(summary_tbl, 'match_factor_summary.csv', row.names = FALSE)

Evaluation signals

• The summary table contains exactly one row per threshold condition tested (e.g., three rows for thresholds ≥65, ≥80, ≥90).
• Compound counts decrease monotonically as the threshold becomes more stringent (count at ≥65 ≥ count at ≥80 ≥ count at ≥90).
• All compound names extracted under each filter are unique (no duplicates within a condition group).
• The threshold values in the summary table match the filter criteria applied (e.g., 65, 80, 90).
• The summary table is saved in a standard format (CSV or tabular object) with named columns and human-readable headers.

Limitations

• The quality of the summary depends on the quality and consistency of the Match.Factor values in the input file; missing or non-numeric Match.Factor entries will cause filtering errors.
• The threshold values must be selected a priori; the skill does not suggest optimal thresholds automatically.
• If compounds have multiple rows (e.g., from different files or runs), the 'unique' counting step may mask important sample-level variation.
• The summary table alone does not validate whether the filtered compounds are biologically or chemically meaningful; it only quantifies retention.

Evidence

• [methods] Match.Factor filtering can be applied at multiple threshold levels: "Apply three independent filters: Match.Factor >= 65, Match.Factor >= 80, and Match.Factor >= 90, retaining unique compound names for each filter."
• [methods] Counting compounds per threshold: "Count the number of unique compounds retained under each threshold condition (COND-mf65, COND-mf80, COND-mf90)."
• [methods] Summary table structure and output: "Construct a summary table with three rows (one per threshold) and two columns (threshold value and compound count). Save the comparison table as a CSV file."
• [readme] Example R filter invocation: "query_chems = standard_dat$Compound.Name[standard_dat$Match.Factor >= 65]"