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This article lists the standard names biocohort uses for columns, R objects, functions, and files.
subject_tbl)| Column | Type | Description |
|---|---|---|
subject_id |
character | A unique subject ID. Works the same for any species. |
species |
character | The subject’s species, for example “rat” or “mouse”. Free-form, not a fixed list, and stored lower case. |
sex |
character | Biological sex: “M”, “F”, or NA if unknown. |
strain |
character | Strain or breed, for example “Fischer 344” or “B6”. |
genotype |
character | Genetic background or modification, for example “WT”, “KO”, “HET”. |
cohort |
character | Treatment group or cohort membership, for example “Control”, “Treatment_A”. |
timepoint |
character | Study visit, age, or collection date, for example “Day_0”, “Week_12”, “8wks”. |
notes |
character | Free-form annotations. |
sample_map)sample_map is the canonical, long-format sample table:
one row per sample, covering any number of assays and roles. A new assay
is a new row, never a new column or a new table.
| Column | Type | Description |
|---|---|---|
subject_id |
character | Reference to a subject in subject_tbl. |
assay |
character | The assay, a free-form value, for example "wgs",
"wes", "atac", "bulk_rna",
"scrna". |
sample_id |
character | A unique sample ID. |
role |
character | The sample’s role within its assay, for example
"tumor", "normal", or NA when it
does not apply. |
Other sample-level columns, such as fastq_1,
fastq_2, bam, lane, or
replicate, stay in sample_map when
validate_manifest() already knows the name, or when it is
passed in the sample_cols argument. A column that varies
within a subject but is not recognized or declared trips the
subject-level conflict check instead.
completeness_tbl)| Column | Type | Description |
|---|---|---|
subject_id |
character | Reference to a subject in subject_tbl. |
assay |
character | The assay, matching values in sample_map. |
n_samples |
integer | Sample count for the subject within the assay. |
assay is a free-form, lowercase value, not a fixed list.
Pick a stable label per assay and reuse it. Common examples:
| Assay | Code | Description |
|---|---|---|
| Whole genome sequencing | "wgs" |
Whole genome DNA sequencing. |
| Whole exome sequencing | "wes" |
Exome capture and sequencing. |
| ATAC-seq | "atac" |
Chromatin accessibility. |
| Bulk RNA-seq | "bulk_rna" |
Bulk transcriptomics. |
| Single-cell / single-nucleus RNA-seq | "scrna" |
Single-cell or single-nucleus transcriptomics. |
biocohort does not enforce a sample ID format. Pick one convention for a project and keep it. Putting the assay and role in the ID makes it easier to read:
{assay}_T{subject}, for example
"wes_T101", "wgs_T001".{assay}_N{subject}, for
example "wes_N101", "wgs_N001".{assay}_{subject}_{rep}, for example
"scrna_101_1", "bulk_rna_101_2".Tumor and normal pairs are not stored in
sample_map. Derive them on demand with
sample_pairs(), which builds a pair_id of
{tumor_sample_id}__{normal_sample_id}, for example
"wes_T101__wes_N101".
my_study, pilot_study, or just
study in examples.cohort, pilot_cohort,
study_cohort.subject(cohort, subject_id).name, which is also its key in the registry._tbl
suffix, or a descriptive name such as subject_data or
wes_samples.# Study
study <- study_new(
study_id = "STUDY_001",
title = "Example Genomics Study"
)
# Cohort
cohort <- cohort_new(
subject_tbl = subject_data,
sample_map = sample_data,
study = study
)
# Read one subject as a Subject object
rat_101 <- subject(cohort, "RAT_101")
# Read a stored analysis result
result <- cohort@analyses[["my_analysis_name"]]{noun}_new(), for
example study_new(), cohort_new().validate_{noun}(), for
example validate_manifest(),
validate_cohort().subjects(), samples(), or read the property
directly with @, for example
cohort@subject_tbl.read_{format}() or
write_{format}(), for example
read_manifest().sample_pairs().print() on a Cohort.classes.R.constructors.R.validate.R,
validate_manifest.R.io.R.{feature}.R,
for example analysis_registry.R, print.R.manifest_study_001.csv,
cohort_pilot_data.rda.{description}_{date}.rda or
{description}_{version}.fst.{analysis}_{date}_{version}.csv or
{analysis}_results.rda.manifest_pilot_wes_rna_v1.csv
manifest_pilot_cohort.csv
cohort_complete_metadata.csvmanifest, subject_tbl,
sample_map.is_ or
has_, for example is_valid,
has_missing.n_, for example
n_subjects, n_samples.strict,
verbose, allow_*, for example
allow_duplicates.cohort@analyses[["my_analysis"]].{title-in-kebab-case}.Rmd, for example
glossary.Rmd, naming-conventions.Rmd.@param, @return, and a runnable
@examples block. Link related functions with
[function_name()] or [ClassName].Study, Subject,
Cohort)."wes", "atac", "scrna"._id or
_sample_id.has_ or is_, or
read as a plain yes-or-no question.n_._tbl or _table.See the Glossary article for what these terms mean.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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