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Resubmission to CRAN. The package code is unchanged from 0.1.0; every change is in the documentation and the repository.
?biocohort names the three articles as
vignette() calls instead of linking to them by a relative
path that only exists on the website, and the package README links to
the contributing guide by its full URL. CRAN’s pretest rejected 0.1.0
for both links.liftover_rtracklayer() example is wrapped in
\donttest{}. Loading rtracklayer alone takes
longer than the five seconds CRAN allows an example.Description field is rewritten in plainer
sentences.translate() on a cohort,
translation_report(), load_analyses(),
cohort_read(), register_liftover_backend(),
register_ortholog_backend(), liftover_vcf(),
and the Study, Subject, Cohort,
AnalysisSpec, and TranslationResult
constructors. The translation_report() example was a
comment before, not code.orthologize() now
point to translate().citation("biocohort") includes the Zenodo DOI.cran workflow runs the check the way CRAN’s
pretest does.First release. Earlier drafts carried the numbers 0.1.0 to 0.3.0 and were never tagged.
Study, Subject,
Cohort, and AnalysisSpec, built with
study_new(), subject_new(),
cohort_new(), and analysis_spec_new(). Each
class validates on construction, including the raw Study(),
Subject(), and Cohort() constructors.validate_manifest() takes one long-format manifest (one
row per sample, required columns subject_id,
assay, sample_id, optional role
plus subject-level columns) and returns subject_tbl,
sample_map, and completeness_tbl. Species and
assays are free-form values, never a fixed list and never columns. Every
column is coerced to character, so a factor or a numeric column never
breaks a downstream join.validate_manifest(sample_cols = ) keeps named extra
columns (fastq_1, fastq_2, lane,
qc_status, and more) at the sample level instead of raising
a false conflict.read_manifest_csv() reads a manifest from CSV and
delegates to validate_manifest().validate_cohort() checks a Cohort for the
four required sample_map columns, non-missing species,
unique subject ids, and referential integrity.sample_pairs() derives tumor and normal pairs from a
sample_map on demand, with configurable role labels and a
sep argument for the pair id.example_cohort ships as a small demonstration
dataset.subjects(), samples(), and
completeness() return plain tibbles read from a cohort.
completeness(wide = TRUE) gives one row per subject and one
column per assay.subject() reads one subject from
subject_tbl as a Subject object.cohort_filter() keeps a subset of subjects or assays
and returns a cohort that is still valid. drop_sample_ids
removes specific sample ids instead of naming every sample to keep.print() for Cohort, Subject,
and AnalysisSpec shows subject and sample counts, extra
sample columns, a QC summary, and registered analyses.cohort_qc() flags or drops subjects or samples,
recording the reason in qc_status/qc_reason
columns (flag) or by removing the matching rows (drop).
qc_log() reads the audit trail every call appends to
cohort@qc, which is not cleared by
cohort_filter(), so the record survives later structural
changes to the cohort.cohort_groups() groups a cohort’s subjects by one or
more subject_tbl columns and returns one row per
combination that actually occurs, with the matching subject ids.
cohort_contrasts() enumerates every pairwise contrast
between those groups, ready to pipe into cohort_filter()
for each side.cohort_derive() bins an existing numeric column at one
or more named cutoffs and writes the result as a new column on
subject_tbl or sample_map, so a cutoff like
“early onset is 120 days or under” is a value passed in, not code.
derive_log() reads the provenance every call appends to
cohort@derived, which, like cohort@qc, is not
cleared by cohort_filter().read_manifest() reads a manifest from CSV, TSV, or
Excel, always as text, and validates it in one call.manifest_from_wide() reshapes a wide,
one-row-per-subject table with one id column per assay into the long
form validate_manifest() expects.write_manifest() writes a cohort’s tables back out as
one manifest. cohort_save() and cohort_read()
keep a whole cohort as an RDS file.read_study_yaml() builds a cohort from one YAML file
that names the study, the manifest, the file paths, and the registered
analyses. write_study_yaml() writes one back. Every path is
resolved relative to the YAML file’s own directory.apply_corrections() applies a table of documented
overrides (level, id, column,
value, reason) to a manifest before it is
validated, and keeps an audit trail. read_corrections()
reads that table from a file.sample_sheet() writes the sample list a pipeline
expects, with built-in templates for nf-core/rnaseq,
nf-core/rnavar, nf-core/atacseq, and
nf-core/sarek. sample_sheet_templates() lists
the built-in names.check_paths() tests that the file paths named in
cohort@paths and in known sample-level path columns exist,
without ever raising an error.as_coldata() returns a Cohort’s per-sample
metadata as row-named data, ready for a
SummarizedExperiment’s colData.
join_metadata() carries that metadata into a
SummarizedExperiment, a Seurat object, or a plain data
frame.project_root(), project_path(),
ensure_dir(), and read_dotenv() find a
project’s root folder, build paths under it, and read a
.env file.analysis_register(), analysis_list(), and
analysis_spec() manage AnalysisSpec entries in
a cohort. format, reader, and
key_cols default from the spec’s path_template
and level when not given.load_analysis() and load_analyses()
resolve a spec’s path_template per subject, per pair, or
per cohort, read the files with the spec’s reader, and record which
files were found. Subject and pair units are enumerated only for the
spec’s own assay. analysis_files() returns that
record.path_template ending in .parquet now
gets arrow::read_parquet as its default reader, the same
way .csv, .tsv, .txt, and
.rds already do.translate() moves a feature table across genome builds
or species. Coordinate features go through a liftover backend; gene
features go through an ortholog backend. orthologize()
still works as an alias and warns once per session.
translation_report() returns the per-analysis results.subject_tbl when every subject shares one species. A cohort
with more than one species is split by species, translated, and
recombined, as long as the analysis table carries a
subject_id column.liftover_intervals() with an rtracklayer
backend and a crossmap backend, plus
liftover_vcf() for allele-aware variant liftover through
CrossMap. Backends are registered with
register_liftover_backend().ortholog_genes() with an offline babelgene
backend, registered with register_ortholog_backend().
Model-to-model pairs pivot through human. The backend accepts a
cache file so repeated lookups skip babelgene.TranslationResult keeps mapped and unmapped features
under one .feature_id key, for both coordinate and gene
features. translation_stats() summarizes them.dev and main.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.
Health stats visible at Monitor.