Proteomics Result Tables

Published

Jun 2026

The Proteomics Analysis System begins with processed result tables.

These tables may come from a proteomics core facility, collaborator, vendor, or internal analysis platform. They usually represent the output of upstream mass spectrometry processing, protein identification, protein quantification, and statistical comparison.

In this results-first guide, the first task is to understand what the table contains before performing filtering, annotation, enrichment analysis, network analysis, or biological interpretation.

Why Table Inspection Matters

Proteomics result tables are not always standardized.

Different software tools, facilities, and collaborators may use different names for similar columns. For example, a protein identifier column may be called Protein_ID, Accession, Protein.Group, Majority protein IDs, UniProt, or Protein Accession.

A fold-change column may appear as:

log2FC
log2_fold_change
Log2 ratio
Difference
Ratio
Fold Change

Similarly, statistical columns may appear as:

p_value
P.Value
p.val
adjusted_p_value
adj.P.Val
q_value
FDR

Before analysis begins, the analyst must identify which columns represent:

  • protein identifiers
  • gene names or symbols
  • protein descriptions
  • abundance or intensity values
  • fold-change values
  • p-values
  • adjusted p-values or false discovery rates
  • peptide counts or protein-level evidence
  • sample or condition-specific measurements

This step prevents downstream errors.

A good proteomics interpretation depends on knowing exactly which columns are being used for filtering, ranking, annotation, and biological interpretation.

System Design Principle

Main chapters in this guide define reusable workflows.

Scripts are designed to be dataset-agnostic.

A dedicated case study later in the guide will run the same scripts end-to-end on a real processed proteomics table.

Reusable chapter
        ↓
Dataset-agnostic script
        ↓
Standard outputs
        ↓
Case study application
        ↓
End-to-end demonstration

This keeps the Proteomics Analysis System reusable across different projects, organisms, facilities, and proteomics platforms.

Example Input for the Guide

This chapter uses a small example proteomics result table to demonstrate the workflow.

The example file should be placed in:

data/example/example-proteomics-results.csv

The example table is included so the guide can be run immediately, even before a real project table or case-study dataset is added.

A real project table can later be placed in:

data/input/proteomics-results.csv

Recommended structure:

data/
├── example/
│   └── example-proteomics-results.tsv
├── input/
│   └── proteomics-results.tsv
└── results/

scripts/
└── R/
    └── 01-inspect-proteomics-table.R

The example table can be populated throughout the guide. Each chapter can reuse the same example input and gradually produce additional outputs.

data/example/example-proteomics-results.csv
        ↓
01 table inspection
        ↓
02 differential abundance filtering
        ↓
03 protein ranking
        ↓
04 identifier cleaning
        ↓
05 GO and pathway enrichment input
        ↓
06 STRING network input
        ↓
07 biological interpretation
        ↓
08 reproducible report

For the later case study, the same scripts can be applied to a real processed proteomics table using case-study-specific input and output folders.

Common Table Types

Processed proteomics outputs may appear in several forms.

Protein Quantification Table

A protein quantification table usually contains one row per protein or protein group and multiple abundance columns.

Example structure:

Protein_ID    Gene_Name    Protein_Name    Sample_1    Sample_2    Sample_3
P12345        ABCD1        Protein ABCD1   123456      145002      132880
Q99999        XYZ2         Protein XYZ2    89012       91022       87500

This type of table is useful for quality control, exploratory analysis, normalization checks, clustering, and visualization.

Differential Abundance Table

A differential abundance table contains statistical comparison results.

Example structure:

Protein_ID    Gene_Name    log2FC    p_value    adjusted_p_value
P12345        ABCD1        1.42      0.0008     0.012
Q99999        XYZ2        -0.91      0.031      0.080

This is the main input for filtering, ranking, enrichment analysis, and interpretation.

Annotation-Enriched Table

Some outputs already include functional annotations.

Example structure:

Protein_ID    Gene_Name    Protein_Name    GO_Terms    Pathway    log2FC    adjusted_p_value
P12345        ABCD1        Protein ABCD1   GO:...      KEGG...    1.42      0.012

This can speed up interpretation, but the annotations should still be checked for completeness and consistency.

Required Minimum Columns

For this results-first workflow, the ideal minimum input table contains:

Protein_ID
Gene_Name
Protein_Name
log2FC
p_value
adjusted_p_value

However, real datasets may not contain all of these columns.

At minimum, the system needs:

Protein identifier
Fold-change or direction column
Statistical significance column

If gene names are missing, they can often be mapped later using UniProt, organism-specific annotation packages, or identifier conversion tools.

Modern R Tooling

The R scripts in this guide use a modern tidyverse-oriented workflow.

Core packages include:

library(readr)
library(dplyr)
library(stringr)
library(tibble)

This keeps the scripts readable, reproducible, and easier to extend in later chapters.

The first script uses:

readr   → input and output tables
dplyr   → table summaries and transformations
stringr → column-pattern detection
tibble  → clean structured outputs

Table Inspection Script

The executable script is saved as:

scripts/R/01-inspect-proteomics-table.R

The script reads a processed proteomics result table and reports:

  • number of rows and columns
  • column names
  • column classes
  • missing value counts
  • missing value percentages
  • guessed identifier columns
  • guessed gene-name columns
  • guessed protein-description columns
  • guessed fold-change columns
  • guessed p-value columns
  • guessed adjusted p-value or FDR columns
  • a preview of the first rows

The full script is maintained separately in the scripts/R/ directory so it can be reused by the main chapters and the later case study.

Running the Script on the Example Input

From the project root, run:

Rscript scripts/R/01-inspect-proteomics-table.R \
  data/example/example-proteomics-results.csv \
  results

The expected outputs are:

results/
├── table-inspection-summary.txt
├── table-column-summary.tsv
├── table-missing-values.tsv
└── table-detected-columns.tsv

Running the Script on a Real Project Input

When working with a real processed proteomics result table, place the file in:

data/input/proteomics-results.csv

Then run:

Rscript scripts/R/01-inspect-proteomics-table.R \
  data/input/proteomics-results.csv \
  results

The command is the same. Only the input file changes.

Output 1: Human-Readable Summary

The .txt summary is designed for quick review.

results/table-inspection-summary.txt

It contains a readable overview of the table, detected columns, generated output files, and a preview of the first rows.

This file is useful during manual inspection and documentation.

Output 2: Column Summary

The column summary is machine-readable.

results/table-column-summary.tsv

It contains one row per column.

Example structure:

column_name    column_index    column_type    non_missing_values    missing_values    missing_percent
Protein_ID     1               character      2500                  0                 0
log2FC         4               numeric        2488                  12                0.48

This file can be used by later scripts to understand table structure.

Output 3: Missing Values

The missing value table is also machine-readable.

results/table-missing-values.tsv

It contains:

column_name
missing_values
missing_percent

This helps identify columns that may need cleaning before filtering or interpretation.

Output 4: Detected Columns

The detected column table stores candidate analysis columns.

results/table-detected-columns.tsv

Example structure:

category                    column_name
protein_identifier           Protein_ID
gene_name                    Gene_Name
fold_change                  log2FC
p_value                      p_value
adjusted_p_value_or_fdr      adjusted_p_value

This file helps downstream scripts identify likely key columns.

The analyst should still review the detected columns before relying on them for filtering.

Interpreting the Inspection Summary

The inspection summary helps answer the first set of practical questions:

Is the table readable?
        ↓
Are the expected columns present?
        ↓
Which columns represent identifiers?
        ↓
Which columns represent fold change?
        ↓
Which columns represent statistical significance?
        ↓
Are missing values present?
        ↓
Is the table ready for filtering?

If the required columns are clear, the analysis can proceed to differential abundance filtering.

If the columns are unclear, the next step is to rename or standardize them.

Standardization Comes Next

The next chapter will focus on differential abundance results.

Before filtering proteins, the table should have standardized column names such as:

protein_id
gene_name
protein_name
log2fc
p_value
adjusted_p_value

Standard names make downstream scripts easier to reuse across proteomics projects.

Case Study Placement

This chapter does not depend on a specific dataset.

A later case study will apply the same script to a real processed proteomics table and run the workflow end-to-end.

General system chapter
        ↓
Reusable inspection script
        ↓
Case study input table
        ↓
End-to-end outputs

For a case study, the same command structure can be used with case-study-specific paths.

Example:

Rscript scripts/R/01-inspect-proteomics-table.R \
  case-studies/results-first-proteomics/data/input/proteomics-results.csv \
  case-studies/results-first-proteomics/results

Looking Ahead

The next step is to move from table inspection to differential abundance interpretation.

Proteomics result table
        ↓
Column inspection
        ↓
Column standardization
        ↓
Differential abundance results