A small Cohort with two rat and two mouse subjects. Use it to explore the data model, to try the API, or as a template for a cohort built from real data.
Format
A Cohort object (S7 class) with the following structure:
study: A Study object with metadata for a cross-species genomics project
subject_tbl (tibble): 4 subjects (2 rat, 2 mouse) with species, sex, strain, genotype, cohort, timepoint
sample_map (tibble): Long-format map (subject_id, assay, sample_id, role, fastq_1, fastq_2) covering WES tumor/normal and snRNA-seq samples. fastq_1/fastq_2 show that an extra sample-level column survives validate_manifest() alongside the four canonical ones.
paths (list): Empty, ready for file paths
analyses (list): Empty, ready for analysis results
Details
The cohort shows:
Two species in one subject table
Two assays per subject in one long-format sample map
A Study object for project context
Subjects are stored as rows of subject_tbl. Use subject() to read one
of them as a Subject object.
See also
cohort_new() for creating Cohort objects,
subject() for reading one subject,
validate_manifest() for preparing manifest data,
read_manifest_csv() for loading manifest from CSV file
Examples
# Load the example cohort
data(example_cohort)
# View the study metadata
example_cohort@study
#> Study <STUDY001>: Cross-species genomics comparison
#> Example study comparing rat and mouse genomes
# Read one subject as a Subject object
rat1 <- subject(example_cohort, "RAT001")
rat1@species
#> [1] "rat"
rat1@sex
#> [1] "M"
# List all subject ids
example_cohort@subject_tbl$subject_id
#> [1] "RAT001" "RAT002" "MOUSE001" "MOUSE002"
# View all subjects with metadata
example_cohort@subject_tbl
#> # A tibble: 4 × 7
#> subject_id species sex strain genotype cohort timepoint
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 RAT001 rat M Lewis WT Control Day0
#> 2 RAT002 rat F Lewis WT Control Day0
#> 3 MOUSE001 mouse M C57BL/6 WT Control Day0
#> 4 MOUSE002 mouse F C57BL/6 KO Treatment Day0
# View the sample map
example_cohort@sample_map
#> # A tibble: 12 × 6
#> subject_id assay sample_id role fastq_1 fastq_2
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 RAT001 wes WES_R001_T tumor wes_r001_t_R1.fastq.gz wes_r001_t_R2.fast…
#> 2 RAT001 wes WES_R001_N normal wes_r001_n_R1.fastq.gz wes_r001_n_R2.fast…
#> 3 RAT001 scrna SNRNA_R001 tumor snrna_r001_R1.fastq.gz snrna_r001_R2.fast…
#> 4 RAT002 wes WES_R002_T tumor wes_r002_t_R1.fastq.gz wes_r002_t_R2.fast…
#> 5 RAT002 wes WES_R002_N normal wes_r002_n_R1.fastq.gz wes_r002_n_R2.fast…
#> 6 RAT002 scrna SNRNA_R002 tumor snrna_r002_R1.fastq.gz snrna_r002_R2.fast…
#> 7 MOUSE001 wes WES_M001_T tumor wes_m001_t_R1.fastq.gz wes_m001_t_R2.fast…
#> 8 MOUSE001 wes WES_M001_N normal wes_m001_n_R1.fastq.gz wes_m001_n_R2.fast…
#> 9 MOUSE001 scrna SNRNA_M001 tumor snrna_m001_R1.fastq.gz snrna_m001_R2.fast…
#> 10 MOUSE002 wes WES_M002_T tumor wes_m002_t_R1.fastq.gz wes_m002_t_R2.fast…
#> 11 MOUSE002 wes WES_M002_N normal wes_m002_n_R1.fastq.gz wes_m002_n_R2.fast…
#> 12 MOUSE002 scrna SNRNA_M002 tumor snrna_m002_R1.fastq.gz snrna_m002_R2.fast…
# Count subjects by species
table(example_cohort@subject_tbl$species)
#>
#> mouse rat
#> 2 2