The subset_T2D.obj dataset contains 16S rRNA data for 79 patients with type 2 diabetes (T2D) over multiple visits, gathered as part of the Integrative Human Microbiome Project (iHMP) or HMP2 study. The dataset includes a mStat object with 12,062 taxa and 2,208 samples.
Usage
data(subset_T2D.obj)Format
A MicrobiomeStat Data Object with the following components:
- feature.tab
A matrix of microbial abundances
- meta.dat
A data frame with 575 observations and 14 variables:
- file_id
Character. Unique identifier for each file
- md5
Character. MD5 hash of the file
- size
Character. File size in bytes
- urls
Character. URL for file download
- sample_id
Character. Unique identifier for each sample
- file_name
Character. Name of the file
- subject_id
Character. Unique identifier for each subject
- sample_body_site
Character. Body site of sample collection (e.g., "feces")
- visit_number
Character. Visit number as a string
- subject_gender
Character. Gender of the subject
- subject_race
Character. Race/ethnicity of the subject
- study_full_name
Character. Full name of the study (e.g., "prediabetes")
- project_name
Character. Name of the project
- visit_number_num
Numeric. Visit number as a numeric value
- tree
Phylogenetic tree of the taxa
- tax.tab
Taxonomic classification of the taxa
Source
Data source: Integrative Human Microbiome Project (iHMP) https://www.hmpdacc.org/ihmp/
References
Zhou, W., Sailani, M.R., Contrepois, K. et al. Longitudinal multi-omics of host–microbe dynamics in prediabetes. Nature 569, 663–671 (2019). https://doi.org/10.1038/s41586-019-1236-x
Examples
data(subset_T2D.obj)
str(subset_T2D.obj$meta.dat)
#> 'data.frame': 575 obs. of 14 variables:
#> $ file_id : chr "6cca313bce90a4392c3d5cf23fdb43db" "6cca313bce90a4392c3d5cf23fda16c7" "6cca313bce90a4392c3d5cf23fd946a5" "6cca313bce90a4392c3d5cf23fdb6783" ...
#> $ md5 : chr "52ae217fbb3b5888906574e7c0eda10a" "f7f85c444703ec4259d5358e2662fe72" "4b9bf6bf9cc0e62372ecb73ebd303cae" "30251158c0b00b8d15f0027786cad84d" ...
#> $ size : chr " 96000" " 86000" " 83000" " 98000" ...
#> $ urls : chr "fasp://aspera.ihmpdcc.org/t2d/genome/microbiome/16s/analysis/hmqcp/HMP2_J00825_1_ST_T0_B0_0120_ZN9YTFN-01_AA31J.biom" "fasp://aspera.ihmpdcc.org/t2d/genome/microbiome/16s/analysis/hmqcp/HMP2_J00826_1_ST_T0_B0_0120_ZN9YTFN-1011_AA31J.biom" "fasp://aspera.ihmpdcc.org/t2d/genome/microbiome/16s/analysis/hmqcp/HMP2_J00827_1_ST_T0_B0_0120_ZN9YTFN-1012_AA31J.biom" "fasp://aspera.ihmpdcc.org/t2d/genome/microbiome/16s/analysis/hmqcp/HMP2_J00828_1_ST_T0_B0_0120_ZN9YTFN-1013_AA31J.biom" ...
#> $ sample_id : chr "932d8fbc70ae8f856028b3f67c4ef6ac" "932d8fbc70ae8f856028b3f67c4f0716" "932d8fbc70ae8f856028b3f67c4f21b0" "932d8fbc70ae8f856028b3f67c4f35bd" ...
#> $ file_name : chr "HMP2_J00825_1_ST_T0_B0_0120_ZN9YTFN-01_AA31J" "HMP2_J00826_1_ST_T0_B0_0120_ZN9YTFN-1011_AA31J" "HMP2_J00827_1_ST_T0_B0_0120_ZN9YTFN-1012_AA31J" "HMP2_J00828_1_ST_T0_B0_0120_ZN9YTFN-1013_AA31J" ...
#> $ subject_id : chr "88af6472fb03642dd5eaf8cddc36aacc" "88af6472fb03642dd5eaf8cddc36aacc" "88af6472fb03642dd5eaf8cddc36aacc" "88af6472fb03642dd5eaf8cddc36aacc" ...
#> $ sample_body_site: chr "feces" "feces" "feces" "feces" ...
#> $ visit_number : chr " 1" " 2" " 3" " 4" ...
#> $ subject_gender : chr "female" "female" "female" "female" ...
#> $ subject_race : chr "hispanic_or_latino" "hispanic_or_latino" "hispanic_or_latino" "hispanic_or_latino" ...
#> $ study_full_name : chr "prediabetes" "prediabetes" "prediabetes" "prediabetes" ...
#> $ project_name : chr "Integrative Human Microbiome Project" "Integrative Human Microbiome Project" "Integrative Human Microbiome Project" "Integrative Human Microbiome Project" ...
#> $ visit_number_num: num 1 2 3 4 5 6 1 2 4 6 ...