02. Data Dictionary

Overview

See the Glossary for term definitions and units reference used throughout this project.

  • Describes all variables in the MMRF CoMMpass dataset from the GDC
  • CoMMpass: longitudinal observational study of ~1,000 newly diagnosed multiple myeloma patients
  • Variables span clinical demographics, biospecimen metadata, and RNA-seq gene expression

Data sources:

Complete Data Dictionary

Searchable table of all variables available in the CoMMpass dataset with types, categories, and descriptions.

Clinical Data

  • Patient demographics, disease characteristics, and outcomes
  • Downloaded via TCGAbiolinks::GDCquery_clinic()
  • All time/age variables in days (see units reference)

Sample Rows

Summary

  • Total patients: 995
  • Variables: 88
  • Data completeness: 95.8%

Column Structure

Biospecimen Data

  • Tissue samples collected from patients

  • Includes sample type, tissue type, and preservation method

  • Total records: 2,119

  • Variables: 31

RNA-seq Data

  • RNA-seq gene expression generated using the STAR aligner
  • Quantified against GENCODE annotations
  • Available in multiple quantification types (raw counts, TPM, FPKM)
  • See units reference for quantification units

Dimensions

  • Samples: 100
  • Genes: 60,660
  • Assays: unstranded, stranded_first, stranded_second, tpm_unstrand, fpkm_unstrand, fpkm_uq_unstrand

Treatment Data

  • Treatment records extracted from GDC API (7,184 records, 994 patients)
  • Each row = one drug administration for one patient
  • Includes therapeutic agent, line of therapy, and timing

Sample Rows

Column Name Mappings

GDC uses standardized column names. Common aliases used in analysis:

Units Reference

For the complete units reference table (age/time in days, expression quantification types), see the Glossary units reference.

Key rule: All age and time variables are stored in DAYS by GDC. This is the single most common source of errors in GDC analyses.

For exploratory analysis using these variables, see the EDA vignette.

R Code Examples

Loading Data

How to load CoMMpass data from parquet files and query with DuckDB.

Show code 
safe_tar_read("code_dd_load_data")

library(coMMpass)

Load clinical data from parquet

clinical <- query_commpass_parquet(“clinical”)

Load with DuckDB lazy evaluation

con <- DBI::dbConnect(duckdb::duckdb()) clinical_tbl <- get_commpass_tbl(“clinical”, con = con) result <- clinical_tbl |> dplyr::filter(gender == “female”) |> dplyr::select(submitter_id, age_at_diagnosis, vital_status) |> dplyr::mutate(age_years = age_at_diagnosis / 365.25) |> dplyr::collect() DBI::dbDisconnect(con, shutdown = TRUE)

Exploring the Dictionary

Programmatic access to variable documentation and metadata.

Show code 
safe_tar_read("code_dd_explore_dict")

dd <- get_commpass_data_dictionary()

Find all clinical variables

dplyr::filter(dd, category == “clinical”)

Get detailed docs for a variable

docs <- get_variable_docs(“age_at_diagnosis”) cat(docs$usage_notes)

Data Sources

Results in this vignette are derived from the MMRF CoMMpass study (MMRF-COMMPASS, ~1,143 patients), downloaded via TCGAbiolinks. The pipeline runs with a configurable sample_limit (default 200; CI uses 20).

For full citations, data access tiers, and the distinction between pipeline data and synthetic test data, see the Data Sources vignette.

Recent Changes

Recent project commits with lines added, files changed, and change categories.

Reproducibility

Session Info (click to expand)
Show code 
sessionInfo()
#> R version 4.6.1 (2026-06-24)
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