01. Data Acquisition & Status

Online documentation

This vignette displays pre-computed results. Run the targets pipeline locally for interactive analysis.

Overview

See the Glossary for term definitions (read count, library size, gene detection, outlier, etc.).

• RNA-seq and clinical data from GDC via the targets pipeline
• Open Access RNA-seq from the GDC S3 bucket (s3://gdc-mmrf-commpass-phs000748-2-open/)
• Clinical metadata via TCGAbiolinks::GDCquery_clinic()

Data Flow

flowchart LR
  subgraph GDC["GDC Open Access"]
    R["RNA-seq<br/>STAR Counts"]
    C["Clinical<br/>Metadata"]
    T["Treatment<br/>Records"]
  end

  subgraph Clean["Data Cleaning"]
    SE["Summarized<br/>Experiment"]
    CD["Clinical<br/>DataFrame"]
    TD["Treatment<br/>DataFrame"]
  end

  subgraph Analysis
    DE["Differential<br/>Expression"]
    KM["Survival<br/>Analysis"]
    PA["Pathway<br/>Enrichment"]
    DAG["Causal<br/>DAGs"]
  end

  R --> SE
  C --> CD
  T --> TD
  SE --> DE
  CD --> KM
  TD --> KM
  DE --> PA
  SE --> KM
  CD --> DAG

  style GDC fill:#e8f5e9,stroke:#4CAF50
  style Clean fill:#e3f2fd,stroke:#2196F3
  style Analysis fill:#fce4ec,stroke:#F44336

Simplified data flow from GDC to analysis outputs.

Pipeline Configuration

Current pipeline settings including sample limit, random seed, and data paths.

The pipeline downloads RNA-seq and clinical data from the GDC portal (https://portal.gdc.cancer.gov/projects/MMRF-COMMPASS) for the MMRF-COMMPASS project.

Sample limit: 200 patients (GDC has ~900; subsetting speeds local development). In CI, this is capped at 20 samples (see R/01_data_acquisition.R) to keep build times under 10 minutes.

Random seed: 42 — ensures reproducible patient selection when random_sample = TRUE.

Data directory: data | Results directory: results

Note: This vignette was built with sample_limit = 200 patients. Numbers below reflect this subset, not the full ~900-patient CoMMpass cohort. For pipeline execution details, see issue #46 (telemetry vignette).

RNA-seq Data

RNA-seq gene expression data is downloaded from GDC as a SummarizedExperiment object containing STAR-Counts.

RNA-seq Data Summary

• Samples: 100
• Genes: 60,660
• Total counts: 6,045,546,169 (sum of all read counts across all genes and samples)
• Median counts per sample: 58,030,298 (median library size)
• Sparsity (% zero entries): 49.1%

Per-sample RNA-seq summary for 100 samples. Total Counts = library size (sum of all read counts for one sample). Genes Detected = number of genes with at least 1 mapped read. Sample IDs are GDC barcodes. Data: GDC STAR-Counts pipeline. This table is NOT repeated in the QC section; see Quality Control for outlier flags and size factors.

	Sample	Total_Counts	Genes_Detected	Median_Count	Max_Count
MMRF_1618_1_BM_CD138pos	MMRF_1618_1_BM_CD138pos	37391891	27609	0	4187802
MMRF_2229_1_BM_CD138pos	MMRF_2229_1_BM_CD138pos	51745321	28905	0	9973468
MMRF_2143_1_BM_CD138pos	MMRF_2143_1_BM_CD138pos	59392637	28249	0	7755820
MMRF_2054_1_BM_CD138pos	MMRF_2054_1_BM_CD138pos	63680199	32356	1	6909884
MMRF_1700_2_BM_CD138pos	MMRF_1700_2_BM_CD138pos	96656035	37358	3	7104305
MMRF_1871_1_BM_CD138pos	MMRF_1871_1_BM_CD138pos	46761239	28751	0	4537227
MMRF_1293_1_BM_CD138pos	MMRF_1293_1_BM_CD138pos	50232734	30109	0	2463705
MMRF_1715_1_BM_CD138pos	MMRF_1715_1_BM_CD138pos	58060644	30504	1	4930116
MMRF_1637_1_BM_CD138pos	MMRF_1637_1_BM_CD138pos	41424544	30874	1	4524926
MMRF_1543_1_BM_CD138pos	MMRF_1543_1_BM_CD138pos	44701993	27289	0	4206708
MMRF_1698_1_BM_CD138pos	MMRF_1698_1_BM_CD138pos	46684067	25832	0	14630370
MMRF_1716_1_BM_CD138pos	MMRF_1716_1_BM_CD138pos	46903826	27259	0	7237680
MMRF_2341_1_BM_CD138pos	MMRF_2341_1_BM_CD138pos	71582628	33440	1	3881877
MMRF_2085_3_BM_CD138pos	MMRF_2085_3_BM_CD138pos	101561094	34138	1	5931977
MMRF_2422_1_BM_CD138pos	MMRF_2422_1_BM_CD138pos	49591878	28663	0	12074308
MMRF_2429_1_BM_CD138pos	MMRF_2429_1_BM_CD138pos	44521218	29397	0	6632843
MMRF_2245_1_BM_CD138pos	MMRF_2245_1_BM_CD138pos	55815128	30949	1	8274019
MMRF_2043_1_BM_CD138pos	MMRF_2043_1_BM_CD138pos	70996601	30698	1	9214029
MMRF_2401_2_BM_CD138pos	MMRF_2401_2_BM_CD138pos	76584241	32974	1	9970865
MMRF_2098_1_BM_CD138pos	MMRF_2098_1_BM_CD138pos	57538387	29719	0	13565805
MMRF_2238_1_BM_CD138pos	MMRF_2238_1_BM_CD138pos	66314228	33476	1	9190976
MMRF_1436_1_BM_CD138pos	MMRF_1436_1_BM_CD138pos	57999952	29968	0	12652198
MMRF_1108_1_BM_CD138pos	MMRF_1108_1_BM_CD138pos	42289892	28079	0	6358971
MMRF_1856_1_BM_CD138pos	MMRF_1856_1_BM_CD138pos	48755441	28313	0	7487960
MMRF_2746_1_BM_CD138pos	MMRF_2746_1_BM_CD138pos	74850398	35919	2	10146793
MMRF_1137_4_BM_CD138pos	MMRF_1137_4_BM_CD138pos	55587700	32731	1	5680921
MMRF_1361_1_BM_CD138pos	MMRF_1361_1_BM_CD138pos	76274233	34427	1	7338549
MMRF_1048_1_BM_CD138pos	MMRF_1048_1_BM_CD138pos	46040142	28127	0	4406314
MMRF_2828_1_BM_CD138pos	MMRF_2828_1_BM_CD138pos	68349206	27973	0	16875619
MMRF_2601_1_BM_CD138pos	MMRF_2601_1_BM_CD138pos	39797530	28415	0	5453712
MMRF_2232_1_BM_CD138pos	MMRF_2232_1_BM_CD138pos	81630421	31401	1	17517416
MMRF_2253_1_BM_CD138pos	MMRF_2253_1_BM_CD138pos	63958386	32111	1	13127382
MMRF_1824_1_BM_CD138pos	MMRF_1824_1_BM_CD138pos	37401747	28419	0	6215715
MMRF_2197_1_BM_CD138pos	MMRF_2197_1_BM_CD138pos	65930119	33595	1	8263777
MMRF_2664_1_BM_CD138pos	MMRF_2664_1_BM_CD138pos	64857697	31545	1	3502164
MMRF_1819_1_BM_CD138pos	MMRF_1819_1_BM_CD138pos	49401746	28052	0	8330859
MMRF_2716_1_BM_CD138pos	MMRF_2716_1_BM_CD138pos	91426018	34504	1	19264905
MMRF_1617_1_BM_CD138pos	MMRF_1617_1_BM_CD138pos	37826511	27819	0	3335089
MMRF_2199_1_BM_CD138pos	MMRF_2199_1_BM_CD138pos	52651580	28971	0	8173315
MMRF_1223_2_BM_CD138pos	MMRF_1223_2_BM_CD138pos	64253003	30235	0	12418148
MMRF_1216_1_BM_CD138pos	MMRF_1216_1_BM_CD138pos	54430074	31804	1	9765806
MMRF_2705_1_BM_CD138pos	MMRF_2705_1_BM_CD138pos	149432793	35625	2	15342982
MMRF_2257_1_BM_CD138pos	MMRF_2257_1_BM_CD138pos	52847814	32503	1	5837429
MMRF_2815_1_BM_CD138pos	MMRF_2815_1_BM_CD138pos	54076776	31299	1	14840619
MMRF_2636_1_BM_CD138pos	MMRF_2636_1_BM_CD138pos	79930092	31067	1	15922682
MMRF_1651_1_BM_CD138pos	MMRF_1651_1_BM_CD138pos	38531104	29803	0	4537182
MMRF_1991_1_BM_CD138pos	MMRF_1991_1_BM_CD138pos	69001805	31154	1	10006934
MMRF_1502_1_BM_CD138pos	MMRF_1502_1_BM_CD138pos	60836136	31923	1	7502143
MMRF_1049_4_BM_CD138pos	MMRF_1049_4_BM_CD138pos	67752904	31171	1	6880242
MMRF_1250_1_BM_CD138pos	MMRF_1250_1_BM_CD138pos	41495680	30909	1	3727969
MMRF_1496_1_PB_CD138pos	MMRF_1496_1_PB_CD138pos	79637573	32560	1	7554268
MMRF_1496_1_BM_CD138pos	MMRF_1496_1_BM_CD138pos	149615704	34145	1	8690744
MMRF_1235_1_BM_CD138pos	MMRF_1235_1_BM_CD138pos	60749662	31612	1	10475083
MMRF_1978_1_BM_CD138pos	MMRF_1978_1_BM_CD138pos	58788425	31621	1	8629307
MMRF_2089_3_BM_CD138pos	MMRF_2089_3_BM_CD138pos	87760456	34575	1	6546283
MMRF_1900_1_BM_CD138pos	MMRF_1900_1_BM_CD138pos	52217137	32128	1	6369728
MMRF_2224_1_BM_CD138pos	MMRF_2224_1_BM_CD138pos	54403435	31139	1	2535879
MMRF_2458_1_BM_CD138pos	MMRF_2458_1_BM_CD138pos	56294068	29650	0	7076758
MMRF_1092_1_BM_CD138pos	MMRF_1092_1_BM_CD138pos	36748951	29135	0	4034219
MMRF_2822_1_BM_CD138pos	MMRF_2822_1_BM_CD138pos	60054775	32643	1	10858052
MMRF_1538_1_BM_CD138pos	MMRF_1538_1_BM_CD138pos	61613394	29038	0	7612687
MMRF_1992_1_PB_CD138pos	MMRF_1992_1_PB_CD138pos	40521003	26760	0	6365931
MMRF_2729_1_BM_CD138pos	MMRF_2729_1_BM_CD138pos	94208666	35598	2	12749799
MMRF_1625_1_BM_CD138pos	MMRF_1625_1_BM_CD138pos	38223103	25966	0	6474610
MMRF_2107_1_BM_CD138pos	MMRF_2107_1_BM_CD138pos	63872226	31475	1	12848329
MMRF_2055_1_BM_CD138pos	MMRF_2055_1_BM_CD138pos	41457997	29706	0	5020097
MMRF_1944_1_BM_CD138pos	MMRF_1944_1_BM_CD138pos	47103245	30647	1	5660731
MMRF_1110_2_BM_CD138pos	MMRF_1110_2_BM_CD138pos	77343762	30631	1	6714330
MMRF_2228_1_BM_CD138pos	MMRF_2228_1_BM_CD138pos	68086550	33922	1	7433572
MMRF_1129_1_BM_CD138pos	MMRF_1129_1_BM_CD138pos	63899643	31634	1	12076907
MMRF_1783_2_BM_CD138pos	MMRF_1783_2_BM_CD138pos	82754792	35563	2	16490062
MMRF_1783_1_BM_CD138pos	MMRF_1783_1_BM_CD138pos	50776425	30821	1	15358485
MMRF_2832_1_BM_CD138pos	MMRF_2832_1_BM_CD138pos	61360677	28685	0	5405622
MMRF_1778_1_PB_CD138pos	MMRF_1778_1_PB_CD138pos	67550482	34614	2	9375160
MMRF_1267_1_BM_CD138pos	MMRF_1267_1_BM_CD138pos	30377770	27628	0	7257740
MMRF_2843_1_BM_CD138pos	MMRF_2843_1_BM_CD138pos	77359789	31052	1	2570969
MMRF_2047_1_BM_CD138pos	MMRF_2047_1_BM_CD138pos	39751015	29294	0	7309250
MMRF_1613_1_BM_CD138pos	MMRF_1613_1_BM_CD138pos	44241364	29485	0	6563231
MMRF_1445_1_BM_CD138pos	MMRF_1445_1_BM_CD138pos	53144209	30277	0	9002435
MMRF_1556_1_BM_CD138pos	MMRF_1556_1_BM_CD138pos	74700167	32015	1	9120954
MMRF_2827_1_BM_CD138pos	MMRF_2827_1_BM_CD138pos	61996840	32601	1	6359115
MMRF_1252_1_BM_CD138pos	MMRF_1252_1_BM_CD138pos	22018481	26953	0	4914261
MMRF_1533_2_BM_CD138pos	MMRF_1533_2_BM_CD138pos	46280409	30627	1	4543360
MMRF_1912_1_PB_CD138pos	MMRF_1912_1_PB_CD138pos	55599192	32521	1	6738655
MMRF_2153_1_BM_CD138pos	MMRF_2153_1_BM_CD138pos	62246722	32147	1	7229388
MMRF_1356_1_BM_CD138pos	MMRF_1356_1_BM_CD138pos	48785009	30935	1	8235691
MMRF_1033_1_BM_CD138pos	MMRF_1033_1_BM_CD138pos	52817165	28544	0	16876687
MMRF_1079_1_BM_CD138pos	MMRF_1079_1_BM_CD138pos	81118832	32913	1	6687030
MMRF_1401_2_BM_CD138pos	MMRF_1401_2_BM_CD138pos	67018253	27599	0	8923547
MMRF_1621_1_BM_CD138pos	MMRF_1621_1_BM_CD138pos	46791382	29873	0	6646887
MMRF_2225_1_BM_CD138pos	MMRF_2225_1_BM_CD138pos	58724738	32595	1	2236229
MMRF_1501_1_BM_CD138pos	MMRF_1501_1_BM_CD138pos	60234555	32331	1	5609859
MMRF_2763_1_BM_CD138pos	MMRF_2763_1_BM_CD138pos	48752427	29503	0	5358227
MMRF_2150_1_BM_CD138pos	MMRF_2150_1_BM_CD138pos	55307603	27982	0	9654610
MMRF_1730_1_BM_CD138pos	MMRF_1730_1_BM_CD138pos	60872279	29038	0	10674765
MMRF_2675_1_BM_CD138pos	MMRF_2675_1_BM_CD138pos	73461172	32431	1	8861444
MMRF_1690_1_BM_CD138pos	MMRF_1690_1_BM_CD138pos	56634991	29767	0	8512297
MMRF_1932_1_BM_CD138pos	MMRF_1932_1_BM_CD138pos	58691302	32437	1	2462320
MMRF_2072_1_BM_CD138pos	MMRF_2072_1_BM_CD138pos	78601932	34471	2	5425361
MMRF_1462_3_BM_CD138pos	MMRF_1462_3_BM_CD138pos	49218987	29169	0	6212039

Generating code

{
    if (!file.exists(raw_rnaseq))
        return(NULL)
    se <- readRDS(raw_rnaseq)
    if (!inherits(se, "SummarizedExperiment"))
        return(NULL)
    counts <- get_counts_assay(se)
    log_counts <- log10(counts + 1)
    mean_expr <- rowMeans(log_counts)
    gene_meta <- as.data.frame(SummarizedExperiment::rowData(se))
    has_biotype <- "gene_type" %in% names(gene_meta)
    if (has_biotype) {
        gene_meta$biotype_group <- dplyr::case_when(gene_meta$gene_type ==
            "protein_coding" ~ "protein-coding", gene_meta$gene_type %in%
            c("lncRNA", "processed_pseudogene", "unprocessed_pseudogene",
                "transcribed_unprocessed_pseudogene", "transcribed_processed_pseudogene") ~
            "lncRNA / pseudogene", TRUE ~ "other (miRNA, snoRNA, etc.)")
        plot_df <- data.frame(mean_expr = mean_expr, biotype = gene_meta$biotype_group,
            stringsAsFactors = FALSE)
        biotype_colors <- c(`protein-coding` = "#0066CC", `lncRNA / pseudogene` = "#DC3545",
            `other (miRNA, snoRNA, etc.)` = "#6C757D")
        p <- ggplot2::ggplot(plot_df, ggplot2::aes(x = mean_expr,
            fill = biotype)) + ggplot2::geom_histogram(bins = 50,
            alpha = 0.6, position = "identity") + ggplot2::scale_fill_manual(values = biotype_colors) +
            ggplot2::labs(title = "Distribution of Mean Gene Expression by Biotype",
                subtitle = paste0(format(nrow(counts), big.mark = ","),
                  " genes, ", ncol(counts), " samples"), x = "Mean log10(counts + 1)",
                y = "Number of Genes", fill = NULL) + ggplot2::theme_minimal()
    }
    else {
        plot_df <- data.frame(mean_expr = mean_expr)
        p <- ggplot2::ggplot(plot_df, ggplot2::aes(x = mean_expr)) +
            ggplot2::geom_histogram(bins = 50, fill = "steelblue",
                alpha = 0.7) + ggplot2::labs(title = "Distribution of Mean Gene Expression",
            subtitle = paste0(format(nrow(counts), big.mark = ","),
                " genes, ", ncol(counts), " samples"), x = "Mean log10(counts + 1)",
            y = "Number of Genes") + ggplot2::theme_minimal()
    }
    p
}

Clinical Data

Clinical metadata from GDC provides patient demographics, disease characteristics, and outcomes. See the data dictionary for variable definitions and the glossary for units.

Structure of all 88 clinical variables for 995 patients. Time variables are in DAYS (GDC convention). Columns reordered: IDs > demographics > time > vital status > disease > other. See data dictionary for full definitions and glossary for units reference.

	Column	Type	Non_NA	Pct_Complete	N_Unique	Example
1	project	character	995	100%	1	MMRF-COMMPASS
2	submitter_id	character	995	100%	995	MMRF_1016, MMRF_1020, MMRF_1021
3	submitter_id.1	character	995	100%	995	MMRF_1016, MMRF_1020, MMRF_1021
4	submitter_sample_ids	character	995	100%	995	MMRF_1016_1_BM_CD138pos,MMRF_1016_1_PB_WBC, MMRF_1020_3_BM_CD138pos,MMRF_1020_3_PB_Whole, MMRF_1021_1_BM_CD138pos,MMRF_1021_2_PB_CD3pos
5	submitter_id.2	character	995	100%	995	MMRF_10161, MMRF_10201, MMRF_10211
6	age_at_diagnosis_days	character	995	100%	927	NA, 17034, 18560
7	race	character	995	100%	6	white, not reported, black or african american
8	gender	character	995	100%	2	male, female
9	ethnicity	character	995	100%	3	not hispanic or latino, not reported, hispanic or latino
10	age_at_index_days	integer	995	100%	59	min=27 med=63 max=89
11	days_to_last_known_disease_status_days	character	995	100%	693	1, 997, 617
12	days_to_best_overall_response	character	995	100%	1	NA
13	days_to_diagnosis	character	995	100%	1	NA
14	days_to_last_follow_up_days	character	995	100%	693	1, 997, 617
15	year_of_diagnosis	character	995	100%	1	NA
16	days_to_recurrence	character	995	100%	1	NA
17	days_to_birth	integer	953	96%	926	min=-32800 med=-23200 max=-10100
18	year_of_birth	logical	0	0%	0	(all NA)
19	days_to_death_days	integer	191	19%	176	min=8 med=475 max=1750
20	year_of_death	logical	0	0%	0	(all NA)
21	last_known_disease_status	character	995	100%	1	Unknown tumor status
22	cause_of_death	character	188	19%	2	Cancer Related, Not Cancer Related
23	vital_status	character	995	100%	2	Alive, Dead
24	primary_site	character	995	100%	1	Hematopoietic and reticuloendothelial systems
25	irs_stage	character	995	100%	1	NA
26	iss_stage	character	995	100%	4	II, I, III
27	ajcc_pathologic_stage	character	995	100%	1	NA
28	ann_arbor_clinical_stage	character	995	100%	1	NA
29	enneking_msts_stage	character	995	100%	1	NA
30	inrg_stage	character	995	100%	1	NA
31	tissue_or_organ_of_origin	character	995	100%	1	Bone marrow
32	cog_liver_stage	character	995	100%	1	NA
33	inpc_grade	character	995	100%	1	NA
34	wilms_tumor_histologic_subtype	character	995	100%	1	NA
35	classification_of_tumor	character	995	100%	1	NA
36	cog_renal_stage	character	995	100%	1	NA
37	figo_stage	character	995	100%	1	NA
38	inss_stage	character	995	100%	1	NA
39	tumor_confined_to_organ_of_origin	character	995	100%	1	NA
40	primary_diagnosis	character	995	100%	1	Multiple myeloma
41	ajcc_clinical_stage	character	995	100%	1	NA
42	metastasis_at_diagnosis	character	995	100%	1	NA
43	enneking_msts_tumor_site	character	995	100%	1	NA
44	ann_arbor_pathologic_stage	character	995	100%	1	NA
45	method_of_diagnosis	character	995	100%	1	NA
46	diagnosis_id	character	995	100%	995	0073d350-15ca-4e89-9326-d95543cf778c, 00a8b803-c44b-41ba-9d44-79c1b87a74aa, 00fe922e-5ecb-4936-a91d-6680d3f393c6
47	site_of_resection_or_biopsy	character	995	100%	1	Bone marrow
48	first_symptom_prior_to_diagnosis	character	995	100%	1	NA
49	tumor_grade	character	995	100%	1	Unknown
50	enneking_msts_grade	character	995	100%	1	NA
51	disease_type	character	995	100%	1	Plasma Cell Tumors
52	created_datetime	character	995	100%	1	2018-07-10T14:08:13.021252-05:00
53	enneking_msts_metastasis	character	995	100%	1	NA
54	esophageal_columnar_dysplasia_degree	character	995	100%	1	NA
55	child_pugh_classification	character	995	100%	1	NA
56	state	character	995	100%	1	released
57	prior_treatment	character	995	100%	1	NA
58	cog_rhabdomyosarcoma_risk_group	character	995	100%	1	NA
59	ajcc_pathologic_t	character	995	100%	1	NA
60	morphology	character	995	100%	1	9732/3
61	ajcc_pathologic_n	character	995	100%	1	NA
62	ajcc_pathologic_m	character	995	100%	1	NA
63	irs_group	character	995	100%	1	NA
64	medulloblastoma_molecular_classification	character	995	100%	1	NA
65	residual_disease	character	995	100%	1	NA
66	ann_arbor_b_symptoms	character	995	100%	1	NA
67	icd_10_code	character	995	100%	1	NA
68	synchronous_malignancy	character	995	100%	1	NA
69	burkitt_lymphoma_clinical_variant	character	995	100%	1	NA
70	supratentorial_localization	character	995	100%	1	NA
71	ishak_fibrosis_score	character	995	100%	1	NA
72	goblet_cells_columnar_mucosa_present	character	995	100%	1	NA
73	laterality	character	995	100%	1	NA
74	cog_neuroblastoma_risk_group	character	995	100%	1	NA
75	updated_datetime	character	995	100%	2	2019-06-24T08:07:19.797044-05:00, 2019-08-21T12:47:37.999949-05:00
76	prior_malignancy	character	995	100%	1	NA
77	best_overall_response	character	995	100%	1	NA
78	ann_arbor_extranodal_involvement	character	995	100%	1	NA
79	mitosis_karyorrhexis_index	character	995	100%	1	NA
80	ajcc_staging_system_edition	character	995	100%	1	NA
81	esophageal_columnar_metaplasia_present	character	995	100%	1	NA
82	ajcc_clinical_m	character	995	100%	1	NA
83	ajcc_clinical_n	character	995	100%	1	NA
84	ajcc_clinical_t	character	995	100%	1	NA
85	inpc_histologic_group	character	995	100%	1	NA
86	gastric_esophageal_junction_involvement	character	995	100%	1	NA
87	progression_or_recurrence	character	995	100%	1	unknown
88	demographic_id	character	995	100%	995	0059d727-d660-43b7-bfc5-e8e374f813bf, 00aa6c25-74bf-4a10-b015-3873314906b5, 00af936c-0b23-4c49-94ac-254f89600cf0

Data Completeness

Missing data rates across clinical variables, highlighting fields with substantial missingness.

83 of 88 variables are fully complete. Only variables with missing data are shown below. See data dictionary for variable definitions.

Variables with missing data (5 of 88 total). 83 variables are fully complete and omitted.

	Variable	Missing Count	Missing Percent
1	year_of_birth	995	100.0%
2	year_of_death	995	100.0%
3	cause_of_death	807	81.1%
4	days_to_death	804	80.8%
5	days_to_birth	42	4.2%

Quality Control

Quality control identifies outlier samples based on library size and gene detection rate. See R/02_quality_control.R for filtering criteria.

The per-sample expression summary (Total Counts, Genes Detected) is shown in the RNA-seq Data section above. This section adds QC-specific metrics: size factors, count dispersion (MAD), and outlier flags.

Variable definitions (see also Glossary):

• Total Counts — library size: sum of all read counts mapped to genes in one sample
• Detected Genes — number of genes with at least 1 mapped read (count > 0)
• Median Count — median read count across all genes in a sample (most genes have 0 or low counts, so this is typically 0)
• MAD Count — median absolute deviation of gene counts within a single sample (measures spread of the count distribution for that sample)
• Size Factor — library size / median library size across all samples (values near 1.0 = typical; <<1 = under-sequenced; >>1 = over-sequenced)
• Outlier — flagged Yes if the sample falls in the bottom 5th percentile of either library size OR genes detected

QC Metrics Summary

• Samples assessed: 100
• Outliers flagged: 9

Per-sample QC metrics for 100 samples. Total Counts = library size, Detected Genes = genes with >0 counts, Size Factor = library size / median library size. 9 outlier(s) flagged (bottom 5% by library size or genes detected). Data: GDC STAR-Counts pipeline.

	Sample	Total Counts	Detected Genes	Median Count	MAD Count	Size Factor	Outlier
1	MMRF_1618_1_BM_CD138pos	37,391,891	27,609	0.0	0.0	0.644	Yes
2	MMRF_2229_1_BM_CD138pos	51,745,321	28,905	0.0	0.0	0.892	No
3	MMRF_2143_1_BM_CD138pos	59,392,637	28,249	0.0	0.0	1.023	No
4	MMRF_2054_1_BM_CD138pos	63,680,199	32,356	1.0	1.5	1.097	No
5	MMRF_1700_2_BM_CD138pos	96,656,035	37,358	3.0	4.4	1.666	No
6	MMRF_1871_1_BM_CD138pos	46,761,239	28,751	0.0	0.0	0.806	No
7	MMRF_1293_1_BM_CD138pos	50,232,734	30,109	0.0	0.0	0.866	No
8	MMRF_1715_1_BM_CD138pos	58,060,644	30,504	1.0	1.5	1.001	No
9	MMRF_1637_1_BM_CD138pos	41,424,544	30,874	1.0	1.5	0.714	No
10	MMRF_1543_1_BM_CD138pos	44,701,993	27,289	0.0	0.0	0.770	No
11	MMRF_1698_1_BM_CD138pos	46,684,067	25,832	0.0	0.0	0.804	Yes
12	MMRF_1716_1_BM_CD138pos	46,903,826	27,259	0.0	0.0	0.808	Yes
13	MMRF_2341_1_BM_CD138pos	71,582,628	33,440	1.0	1.5	1.234	No
14	MMRF_2085_3_BM_CD138pos	101,561,094	34,138	1.0	1.5	1.750	No
15	MMRF_2422_1_BM_CD138pos	49,591,878	28,663	0.0	0.0	0.855	No
16	MMRF_2429_1_BM_CD138pos	44,521,218	29,397	0.0	0.0	0.767	No
17	MMRF_2245_1_BM_CD138pos	55,815,128	30,949	1.0	1.5	0.962	No
18	MMRF_2043_1_BM_CD138pos	70,996,601	30,698	1.0	1.5	1.223	No
19	MMRF_2401_2_BM_CD138pos	76,584,241	32,974	1.0	1.5	1.320	No
20	MMRF_2098_1_BM_CD138pos	57,538,387	29,719	0.0	0.0	0.992	No
21	MMRF_2238_1_BM_CD138pos	66,314,228	33,476	1.0	1.5	1.143	No
22	MMRF_1436_1_BM_CD138pos	57,999,952	29,968	0.0	0.0	0.999	No
23	MMRF_1108_1_BM_CD138pos	42,289,892	28,079	0.0	0.0	0.729	No
24	MMRF_1856_1_BM_CD138pos	48,755,441	28,313	0.0	0.0	0.840	No
25	MMRF_2746_1_BM_CD138pos	74,850,398	35,919	2.0	3.0	1.290	No
26	MMRF_1137_4_BM_CD138pos	55,587,700	32,731	1.0	1.5	0.958	No
27	MMRF_1361_1_BM_CD138pos	76,274,233	34,427	1.0	1.5	1.314	No
28	MMRF_1048_1_BM_CD138pos	46,040,142	28,127	0.0	0.0	0.793	No
29	MMRF_2828_1_BM_CD138pos	68,349,206	27,973	0.0	0.0	1.178	No
30	MMRF_2601_1_BM_CD138pos	39,797,530	28,415	0.0	0.0	0.686	No
31	MMRF_2232_1_BM_CD138pos	81,630,421	31,401	1.0	1.5	1.407	No
32	MMRF_2253_1_BM_CD138pos	63,958,386	32,111	1.0	1.5	1.102	No
33	MMRF_1824_1_BM_CD138pos	37,401,747	28,419	0.0	0.0	0.645	Yes
34	MMRF_2197_1_BM_CD138pos	65,930,119	33,595	1.0	1.5	1.136	No
35	MMRF_2664_1_BM_CD138pos	64,857,697	31,545	1.0	1.5	1.118	No
36	MMRF_1819_1_BM_CD138pos	49,401,746	28,052	0.0	0.0	0.851	No
37	MMRF_2716_1_BM_CD138pos	91,426,018	34,504	1.0	1.5	1.575	No
38	MMRF_1617_1_BM_CD138pos	37,826,511	27,819	0.0	0.0	0.652	No
39	MMRF_2199_1_BM_CD138pos	52,651,580	28,971	0.0	0.0	0.907	No
40	MMRF_1223_2_BM_CD138pos	64,253,003	30,235	0.0	0.0	1.107	No
41	MMRF_1216_1_BM_CD138pos	54,430,074	31,804	1.0	1.5	0.938	No
42	MMRF_2705_1_BM_CD138pos	149,432,793	35,625	2.0	3.0	2.575	No
43	MMRF_2257_1_BM_CD138pos	52,847,814	32,503	1.0	1.5	0.911	No
44	MMRF_2815_1_BM_CD138pos	54,076,776	31,299	1.0	1.5	0.932	No
45	MMRF_2636_1_BM_CD138pos	79,930,092	31,067	1.0	1.5	1.377	No
46	MMRF_1651_1_BM_CD138pos	38,531,104	29,803	0.0	0.0	0.664	No
47	MMRF_1991_1_BM_CD138pos	69,001,805	31,154	1.0	1.5	1.189	No
48	MMRF_1502_1_BM_CD138pos	60,836,136	31,923	1.0	1.5	1.048	No
49	MMRF_1049_4_BM_CD138pos	67,752,904	31,171	1.0	1.5	1.168	No
50	MMRF_1250_1_BM_CD138pos	41,495,680	30,909	1.0	1.5	0.715	No
51	MMRF_1496_1_PB_CD138pos	79,637,573	32,560	1.0	1.5	1.372	No
52	MMRF_1496_1_BM_CD138pos	149,615,704	34,145	1.0	1.5	2.578	No
53	MMRF_1235_1_BM_CD138pos	60,749,662	31,612	1.0	1.5	1.047	No
54	MMRF_1978_1_BM_CD138pos	58,788,425	31,621	1.0	1.5	1.013	No
55	MMRF_2089_3_BM_CD138pos	87,760,456	34,575	1.0	1.5	1.512	No
56	MMRF_1900_1_BM_CD138pos	52,217,137	32,128	1.0	1.5	0.900	No
57	MMRF_2224_1_BM_CD138pos	54,403,435	31,139	1.0	1.5	0.938	No
58	MMRF_2458_1_BM_CD138pos	56,294,068	29,650	0.0	0.0	0.970	No
59	MMRF_1092_1_BM_CD138pos	36,748,951	29,135	0.0	0.0	0.633	Yes
60	MMRF_2822_1_BM_CD138pos	60,054,775	32,643	1.0	1.5	1.035	No
61	MMRF_1538_1_BM_CD138pos	61,613,394	29,038	0.0	0.0	1.062	No
62	MMRF_1992_1_PB_CD138pos	40,521,003	26,760	0.0	0.0	0.698	Yes
63	MMRF_2729_1_BM_CD138pos	94,208,666	35,598	2.0	3.0	1.623	No
64	MMRF_1625_1_BM_CD138pos	38,223,103	25,966	0.0	0.0	0.659	Yes
65	MMRF_2107_1_BM_CD138pos	63,872,226	31,475	1.0	1.5	1.101	No
66	MMRF_2055_1_BM_CD138pos	41,457,997	29,706	0.0	0.0	0.714	No
67	MMRF_1944_1_BM_CD138pos	47,103,245	30,647	1.0	1.5	0.812	No
68	MMRF_1110_2_BM_CD138pos	77,343,762	30,631	1.0	1.5	1.333	No
69	MMRF_2228_1_BM_CD138pos	68,086,550	33,922	1.0	1.5	1.173	No
70	MMRF_1129_1_BM_CD138pos	63,899,643	31,634	1.0	1.5	1.101	No
71	MMRF_1783_2_BM_CD138pos	82,754,792	35,563	2.0	3.0	1.426	No
72	MMRF_1783_1_BM_CD138pos	50,776,425	30,821	1.0	1.5	0.875	No
73	MMRF_2832_1_BM_CD138pos	61,360,677	28,685	0.0	0.0	1.057	No
74	MMRF_1778_1_PB_CD138pos	67,550,482	34,614	2.0	3.0	1.164	No
75	MMRF_1267_1_BM_CD138pos	30,377,770	27,628	0.0	0.0	0.523	Yes
76	MMRF_2843_1_BM_CD138pos	77,359,789	31,052	1.0	1.5	1.333	No
77	MMRF_2047_1_BM_CD138pos	39,751,015	29,294	0.0	0.0	0.685	No
78	MMRF_1613_1_BM_CD138pos	44,241,364	29,485	0.0	0.0	0.762	No
79	MMRF_1445_1_BM_CD138pos	53,144,209	30,277	0.0	0.0	0.916	No
80	MMRF_1556_1_BM_CD138pos	74,700,167	32,015	1.0	1.5	1.287	No
81	MMRF_2827_1_BM_CD138pos	61,996,840	32,601	1.0	1.5	1.068	No
82	MMRF_1252_1_BM_CD138pos	22,018,481	26,953	0.0	0.0	0.379	Yes
83	MMRF_1533_2_BM_CD138pos	46,280,409	30,627	1.0	1.5	0.798	No
84	MMRF_1912_1_PB_CD138pos	55,599,192	32,521	1.0	1.5	0.958	No
85	MMRF_2153_1_BM_CD138pos	62,246,722	32,147	1.0	1.5	1.073	No
86	MMRF_1356_1_BM_CD138pos	48,785,009	30,935	1.0	1.5	0.841	No
87	MMRF_1033_1_BM_CD138pos	52,817,165	28,544	0.0	0.0	0.910	No
88	MMRF_1079_1_BM_CD138pos	81,118,832	32,913	1.0	1.5	1.398	No
89	MMRF_1401_2_BM_CD138pos	67,018,253	27,599	0.0	0.0	1.155	No
90	MMRF_1621_1_BM_CD138pos	46,791,382	29,873	0.0	0.0	0.806	No
91	MMRF_2225_1_BM_CD138pos	58,724,738	32,595	1.0	1.5	1.012	No
92	MMRF_1501_1_BM_CD138pos	60,234,555	32,331	1.0	1.5	1.038	No
93	MMRF_2763_1_BM_CD138pos	48,752,427	29,503	0.0	0.0	0.840	No
94	MMRF_2150_1_BM_CD138pos	55,307,603	27,982	0.0	0.0	0.953	No
95	MMRF_1730_1_BM_CD138pos	60,872,279	29,038	0.0	0.0	1.049	No
96	MMRF_2675_1_BM_CD138pos	73,461,172	32,431	1.0	1.5	1.266	No
97	MMRF_1690_1_BM_CD138pos	56,634,991	29,767	0.0	0.0	0.976	No
98	MMRF_1932_1_BM_CD138pos	58,691,302	32,437	1.0	1.5	1.011	No
99	MMRF_2072_1_BM_CD138pos	78,601,932	34,471	2.0	3.0	1.354	No
100	MMRF_1462_3_BM_CD138pos	49,218,987	29,169	0.0	0.0	0.848	No

After Filtering

• Samples retained: 91
• Genes retained: 30,675
• Genes removed: 29,985 (49.4%)

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.

Last 20 project commits with change statistics. Date = commit date; Type = conventional-commit prefix (feat/fix/docs/ci/refactor/test/chore). Files = number of files modified; +Lines/-Lines = lines added/removed. Source: git log –numstat. See changes-by-type table for aggregate breakdown.

date	type	summary	n_files	lines_added	lines_removed	file_categories
2026-03-14	Bug Fix	fix(pipeline): Fix 11 NULL targets — DE condition, ID matching, consensus type	41	146	47	Other, R Source
2026-03-14	Bug Fix	fix(cachix): Remove –watch-mode auto flag (already default)	1	1	1	Other
2026-03-14	Bug Fix	fix(pipeline): Fix 3 NULL-target bugs, auto-generate package.nix (#93)	87	235	80	Config, Docs, Other, R Source
2026-03-14	Bug Fix	fix(nix): Fix cachix signing key, rebuild Bioconductor-dependent targets	2	0	0	Other
2026-03-14	New Feature	feat(captions): Add dynamic captions to 34 table/plot targets	22	579	89	Other, R Source
2026-03-14	Bug Fix	fix(vignettes): Enforce zero-computation rule — 22 violations → 0	32	360	764	Other, R Source, Vignettes
2026-03-13	Bug Fix	fix(vignettes): Convert kable RDS to data.frames, fix telemetry eval guards	18	8	2	Other, Vignettes
2026-03-13	Bug Fix	fix(ci): Save data frames (not DT widgets) to RDS for Nix portability	3	0	0	Other
2026-03-13	Bug Fix	fix(vignettes): Use Quarto #| eval syntax for pkgdown-banner chunks	11	44	11	Vignettes
2026-03-13	Refactoring	refactor(targets): Move Bioconductor packages to per-target declarations	11	35	17	Other, R Source, Vignettes
2026-03-13	New Feature	feat(vignettes): Add code provenance, kable→DT conversion, caption compliance	35	1004	437	CI/CD, Other, R Source, Vignettes
2026-03-13	Bug Fix	fix(vignettes): Skip NULL RDS in safe_tar_read, return invisible(NULL)	11	22	22	Vignettes
2026-03-13	Bug Fix	fix(glossary): Prevent double DT::datatable() wrapping in glossary-table chunk	1	3	1	Vignettes
2026-03-13	CI/CD	ci: Show quarto errors with quiet=FALSE, render individual vignettes in diagnostic	1	20	6	CI/CD
2026-03-13	CI/CD	ci: Add verbose quarto error diagnostics on build failure	1	14	1	CI/CD
2026-03-13	Bug Fix	fix(vignettes): Strip Nix paths from DT widgets, auto-wrap data frames	25	66	28	CI/CD, Other, Vignettes
2026-03-13	CI/CD	ci: Add diagnostic quarto render step to debug build failure	1	17	0	CI/CD
2026-03-13	Bug Fix	fix(vignettes): Revert safe_tar_read placeholder, guard gene-report	11	12	56	Vignettes
2026-03-13	Maintenance	chore: Export vig_count_distribution_plot as ggplot RDS (513KB)	1	0	0	Other
2026-03-13	Bug Fix	fix(vignettes): Enable code eval in CI with RDS fallback	80	113	74	CI/CD, Other, R Source, Vignettes

Reproducibility

Git Commit Info (click to expand)

Generating code

{
    if (!requireNamespace("gert", quietly = TRUE))
        return(NULL)
    tryCatch({
        info <- gert::git_info()
        log1 <- gert::git_log(max = 1)
        git_df <- data.frame(Item = c("Commit Hash", "Author",
            "Time", "Branch"), Value = c(info$commit, log1$author,
            as.character(log1$time), info$shorthand), stringsAsFactors = FALSE)
        DT::datatable(git_df, rownames = FALSE, options = list(pageLength = 10,
            dom = "t", scrollX = TRUE), caption = htmltools::tags$caption(style = "caption-side: top; text-align: left;",
            "Git repository state at pipeline build time."))
    }, error = function(e) NULL)
}

Git repository state at pipeline build time.

Item	Value
Commit Hash	c20a60ac5d4cd228607ef8c3af571c91866a902d
Author	John Gavin <john.b.gavin@gmail.com>
Time	2026-03-13 20:37:26
Branch	main

Session Info (click to expand)

Show code

sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
#>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
#>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
#> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
#> 
#> time zone: UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> loaded via a namespace (and not attached):
#>  [1] base64url_1.4       gtable_0.3.6        jsonlite_2.0.0     
#>  [4] dplyr_1.2.0         compiler_4.5.3      tidyselect_1.2.1   
#>  [7] callr_3.7.6         scales_1.4.0        yaml_2.3.12        
#> [10] fastmap_1.2.0       ggplot2_4.0.2       R6_2.6.1           
#> [13] generics_0.1.4      igraph_2.2.2        knitr_1.51         
#> [16] backports_1.5.0     targets_1.12.0      tibble_3.3.1       
#> [19] pillar_1.11.1       RColorBrewer_1.1-3  rlang_1.1.7        
#> [22] xfun_0.57           S7_0.2.1            otel_0.2.0         
#> [25] cli_3.6.5           withr_3.0.2         magrittr_2.0.4     
#> [28] ps_1.9.1            digest_0.6.39       grid_4.5.3         
#> [31] processx_3.8.6      secretbase_1.2.0    lifecycle_1.0.5    
#> [34] prettyunits_1.2.0   vctrs_0.7.2         evaluate_1.0.5     
#> [37] glue_1.8.0          data.table_1.18.2.1 farver_2.1.2       
#> [40] codetools_0.2-20    rmarkdown_2.30      tools_4.5.3        
#> [43] pkgconfig_2.0.3     htmltools_0.5.9