proker 4.3.1

Pro-ker Proteomics Analysis — an interactive browser-based proteomics data visualization tool

Pro-ker Proteomics Analysis

An interactive browser-based tool for proteomics data visualization and statistical analysis. Upload MaxQuant proteinGroups.txt files and explore your data through configurable plots, group comparisons, and export-ready figures.

Installation

pip install proker

Quick Start

proker                      # launch the viewer on default port 8050
proker data.txt             # launch and auto-load a file
proker --port 9000          # use a custom port
proker --install            # create a desktop shortcut
proker --update             # check for updates

One-click installers are also provided for Windows (Install_Windows.bat) and macOS (Install_macOS.command).

Features

• Upload & Parse — MaxQuant proteinGroups.txt (tab-separated)
• Sample Grouping — Assign samples to groups by shared prefix or manually
• Processing — Intensity/LFQ selection, normalization, pruning, missing-value filtering
• Derived Groups — Create ratio, difference, sum, or product groups from existing groups
• Visualizations — Volcano plots, dot plots, enrichment plots, unique protein plots, PCA
• Graph Settings — Per-plot marker styling, grid, background, font, threshold line toggles
• Canvas — Drag, resize, freeze, annotate, and right-click label multiple plots
• Themes — Built-in dark/light presets and custom color themes
• Export — SVG (vector), PNG (high-resolution raster), and CSV (full analysis data)
• Sessions — Save/load full analysis state as JSON; auto-save on every change

Statistical Methods

Volcano Plot — Differential Expression Analysis

Volcano plots visualize fold change vs. statistical significance for pairwise group comparisons. Each point represents a protein; its x-position is the log2 fold change and y-position is the -log10 FDR-adjusted p-value.

Welch's t-test

Group means are compared using Welch's t-test (two-sample, unequal variance). The test statistic is:

t = |mean_A - mean_B| / (SE + S0)

where SE = sqrt(var_A/n_A + var_B/n_B) is the pooled standard error and S0 is an optional fudge factor (see below). Degrees of freedom are estimated via the Welch-Satterthwaite approximation:

df = (var_A/n_A + var_B/n_B)^2 / ((var_A/n_A)^2/(n_A-1) + (var_B/n_B)^2/(n_B-1))

P-values are computed from the regularized incomplete beta function.

Benjamini-Hochberg FDR Correction

Raw p-values are adjusted for multiple testing using the Benjamini-Hochberg procedure to control the false discovery rate:

adjusted_p[i] = min(adjusted_p[i+1], raw_p[i] * n / rank[i])

where p-values are sorted in ascending order and adjusted from the largest rank downward. This controls the expected proportion of false positives among rejected hypotheses.

S0 Low-Abundance Correction

The S0 parameter (Tusher et al., 2001; used by Perseus/SAM) is a fudge factor added to the t-test denominator to penalize fold changes driven by low-abundance noise:

t_s0 = |mean_A - mean_B| / (SE + S0)

• At S0 = 0 (default), this is a standard Welch's t-test
• At S0 > 0, proteins with small standard error (typically low-abundance) require larger absolute differences to reach significance
• Typical values: 0.1 to 2.0 (Perseus default: 0.1)

This prevents the common problem where low-abundance proteins show extreme fold changes purely due to measurement noise, flooding volcano plot extremes with unreliable hits.

Log2 Fold Change

Fold change is calculated as:

log2FC = log2(mean_GroupX / mean_GroupY)

• Positive values (right side of the plot) = higher abundance in Group X
• Negative values (left side of the plot) = higher abundance in Group Y

Missing Value Imputation

When enabled, zero/missing intensity values are replaced with half the minimum detected non-zero value for that protein across the relevant samples. This is a simple down-shift imputation approach that assumes missing values arise from proteins below the detection limit.

Significance Thresholds

Points are classified as significant when both conditions are met:

• FDR-adjusted p-value < FDR threshold (default: 0.05)
• |log2 FC| >= fold change threshold (default: 1.0)

Dotted reference lines on the plot mark these thresholds and can be toggled off via Graph Settings.

PCA — Principal Component Analysis

PCA reduces high-dimensional proteomics data to two principal components for sample-level visualization, revealing batch effects, outliers, and group separation.

Algorithm

Pro-ker uses dual-space PCA (kernel method), optimized for proteomics datasets where the number of samples (n) is much smaller than the number of features/proteins (m):

1. Mean centering — Each protein's values are centered by subtracting the column mean across all samples
2. Kernel matrix — The n x n kernel matrix K = X * X^T is computed (instead of the m x m covariance matrix)
3. Eigendecomposition — Jacobi eigendecomposition extracts eigenvalues and eigenvectors from K
4. Projection — PC scores are computed as eigenvectors scaled by the square root of their eigenvalues

Output

• PC1, PC2 — The first two principal components (axes of greatest variance)
• Variance explained — Percentage of total variance captured by each component, shown in axis labels
• Requires at least 3 samples

Configuration Options

Parameter	Plot	Default	Description
FDR threshold	Volcano	0.05	Significance cutoff for adjusted p-values (0.05, 0.01, 0.001)
|Log2 FC| threshold	Volcano	1.0	Minimum absolute fold change for significance
S0	Volcano	0	Low-abundance correction fudge factor (0 = off)
Impute missing	Volcano	On	Replace zeros with min(non-zero)/2
Threshold lines	Volcano	On	Show/hide dotted threshold reference lines (Graph Settings)

Export

CSV Export

The Export Analysis as CSV button (Analysis tab) produces a comprehensive file containing:

• Metadata — Software version, source file, protein counts
• Processing settings — Quantification type, normalization, pruning parameters
• Sample groups — Group assignments and derived groups
• Raw data — Pre-processing quantification values per sample
• Processed data — Post-filtering/normalization values with group means
• Volcano plot statistics — Per-protein fold change, p-value, FDR-adjusted p-value, and significance classification for each volcano plot on the canvas
• PCA scores — PC1/PC2 coordinates per sample with variance explained for each PCA plot on the canvas

Canvas Export

Plots on the canvas can be exported as:

• SVG — Fully vectorized, editable in Illustrator/Inkscape/Figma
• PNG — High-resolution raster at 2x scale

References

• Benjamini, Y. & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 57(1), 289-300.
• Tusher, V.G., Tibshirani, R. & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. PNAS, 98(9), 5116-5121.
• Tyanova, S. et al. (2016). The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods, 13(9), 731-740.
• Cox, J. & Mann, M. (2008). MaxQuant enables high peptide identification rates. Nature Biotechnology, 26(12), 1367-1372.

Version History

See CHANGELOG.md for the full version history.

Current version: 4.2.2 (April 2026)

Citation

Ngo, B.M. (2026). Pro-ker Proteomics Analysis [Software].

License

Proprietary. See LICENSE for details.