TPHP

This repository contains the core code for the TPHP project. It performs per-cancer, per-protein tumor–paired non-tumor comparisons using linear mixed-effects regression and exports the results as RDS objects for downstream analysis.

Repository layout

• tumor_dysregulation_analysis.R — main analysis script
• data/tumor_compare_data.parquet — input table (paired tumor/non-tumor)
• output/compare_report_output.rds — main result object (generated)
• output/package_versions.txt — R + package versions used (generated)

1. System requirements

1.1 Operating systems

The workflow is expected to run on every common desktop/server platforms that support R. The test environment is on Windows 11 x64 system.

1.2 Software dependencies

Required

• R (recommended R 4.0+)
• CRAN packages
  • tidyverse
  • arrow
  • lme4
  • lmerTest
  • plyr

Exact tested version recorded in output/package_versions.txt

1.3 Hardware

• CPU-only execution (no GPU required)
• Recommended RAM: ≥8 GB (larger datasets may require more)
• No required non-standard hardware

2. Installation guide

2.1 Get the code

git clone <REPO_URL>
cd <REPO_DIR>

2.2 Install R

Install R for the operating system from CRAN.

Optionally, install packages manually:

install.packages(c("tidyverse","arrow","lmerTest","lme4"))

This project is script-based, and no software installation step is required beyond installing dependencies.

3. Demo

Quick start

From the repository root:

Rscript tumor_dysregulation_analysis.R

3.1 Input files

Default input path:

• data/tumor_compare_data.parquet

3.2 Expected output files

After a successful run, the script writes:

• output/compare_report_output.rds

  An R list DEA with:

  • DEA$Diff.report: full per-(cancer, protein) model results
  • DEA$Diff.report.filter: filtered results using Hedges'g >= 0.5 and p_adj_BH < 0.05

• output/package_versions.txt R version and package versions used.

3.3 Expected runtime

Runtime scales primarily with the number of model fits, i.e., cancer types × proteins × samples.

As a rough guide, on a standard desktop CPU (8–16 threads, 16–32 GB RAM), throughput is ~25 model fits per second, corresponding to < 10 minutes per cancer type under typical settings.

4. Instructions

4.1 Input data format

The script expects a Parquet table with metadata columns:

• patient_ID (string)
• sample_type (must include NT for non-tumor and T for tumor)
• cancer_abbr (string)
• cancer_subtype (string; may be constant within a subset)
• Gender (categorical)
• Age (integer-like)
• Dataset (categorical)

All remaining columns are treated as numeric features (proteins).

4.2 What the script does

For each cancer_abbr and each protein, the script fits a mixed-effects model:

• value ~ 1 + sample_type + (optional covariates) + (1 | patient_ID)

Optional covariates among {cancer_subtype, Gender, Age_c, Dataset} are included only when estimable within the cancer/protein subset.

The reported tumor effect is the fixed-effect coefficient for sample_typeT.

4.3 Output columns

Each row in DEA$Diff.report corresponds to one (cancer, protein) fit and includes:

• effect, se, t
• df(degree of freedom)
• p, p_adj_BH (p value)
• sigma (residual SD)
• es_adj = effect / sigma (standardized effect)
• g_adj = J(df) * es_adj (small-sample adjusted standardized effect)
• formula (the exact model formula used)
• is_singular, re_var_patient (fit diagnostics)