The DataSummary table summarized the list of available
studies and their metadata information contained in the curatedTBData
package.
The table helps users to query avialable datasets quickly.
Users can use curatedTBData() function to access data.
There are three arguments in the function. The first argument
study_name represents the names of the data that are used
to determine the resources of interests. Users can find all available
resource names from DataSummary$Study. The second argument
dry.run enables users to determine the resources’s
availability before actually downloading them. When dry.run
is set to TRUE, the output includes names of the resources.
The third argument curated.only allows the users to access
the curated ready-to-use data. If curated.only is
TRUE, the function only download the curated gene
expression profile and the clinical annotation of the corresponding
data. If curated.only is FALSE, the function
downloads all available resources for input studies.
## dry.run = TRUE, listing dataset(s) to be downloaded
## Set dry.run = FALSE to download dataset(s).## Will download the following resources for GSE19439 from the ExperimentHub:
## GSE19439_assay_raw
## GSE19439_assay_curated
## GSE19439_column_data
## GSE19439_row_data
## GSE19439_meta_dataTo download complete data for a Microarry study (e.g. GSE19439),
we set dry.run = FALSE and
curated.only = FALSE. There are two experiments assay being
included in the output Microarray studies. The first experiment is
assay_curated, which is a matrix that
represents normalized, curated version of the gene expression profile.
The second experiment is assay_raw, which is a SummarziedExperiment
object that contains the raw gene expression profile and information
about probe features.
## Downloading: GSE19439## | | | 0% | |============== | 20%
## | |============================ | 40%
## | |========================================== | 60%
## | |======================================================== | 80%
## | |======================================================================| 100%## Finished!## $GSE19439
## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] assay_curated: matrix with 25417 rows and 42 columns
## [2] object_raw: SummarizedExperiment with 48803 rows and 42 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesWhen accessing data for RNA sequencing studies, another assay called
assay_reprocess is included. This matrix
represents the reprocessed version of gene expression profile from the
raw .fastq files using Rsubread.
## Downloading: GSE79362## | | | 0% | |========== | 14%
## | |==================== | 29%
## | |============================== | 43%
## | |======================================== | 57%
## | |================================================== | 71%
## | |============================================================ | 86%
## | |======================================================================| 100%## Finished!## $GSE79362
## A MultiAssayExperiment object of 4 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 4:
## [1] assay_curated: matrix with 13419 rows and 355 columns
## [2] assay_reprocess_hg19: matrix with 25369 rows and 355 columns
## [3] assay_reprocess_hg38: matrix with 60642 rows and 355 columns
## [4] object_raw: SummarizedExperiment with 134866 rows and 355 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesA list of MultiAssayExperiment
objects is returned when dry.run is
FALSE.
To save running time, in the following example, we set the
curated.only = TRUE and selected five studies that belong
to GSE19491
from the Gene Expression Omnibus.
myGEO <- c("GSE19435", "GSE19439", "GSE19442", "GSE19444", "GSE22098")
object_list <- curatedTBData(myGEO, dry.run = FALSE, curated.only = TRUE)## curated.only = TRUE. Download curated version.
## Set curated.only = FALSE if want to download both raw and curated data.## Downloading: GSE19435## Downloading: GSE19439## Downloading: GSE19442## Downloading: GSE19444## Downloading: GSE22098## Finished!## $GSE19435
## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] assay_curated: matrix with 25417 rows and 33 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat files
##
## $GSE19439
## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] assay_curated: matrix with 25417 rows and 42 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesA full version example for RNA sequencing data: GSE79362.
## Downloading: GSE79362## Finished!## $GSE79362
## A MultiAssayExperiment object of 4 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 4:
## [1] assay_curated: matrix with 13419 rows and 355 columns
## [2] assay_reprocess_hg19: matrix with 25369 rows and 355 columns
## [3] assay_reprocess_hg38: matrix with 60642 rows and 355 columns
## [4] object_raw: SummarizedExperiment with 134866 rows and 355 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThe major advantage of using MultiAssayExperiment
is the coordination of the meta-data and assays when sub-setting. The
MultiAssayExperiment object has built-in function for subsetting samples
based on column condition.
The following code shows how to select samples with only active TB.
GSE19439 <- object_list$GSE19439
GSE19439[, GSE19439$TBStatus == "PTB"]["assay_curated"] # 13 samples## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] assay_curated: matrix with 0 rows and 13 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThe following example shows how to subset patients with active TB and LTBI
## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] assay_curated: matrix with 0 rows and 30 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThe combine_objects() function provides an easy
implementation for combining different studies based on common gene
symbols. The function returns a SummarizedExperiment
object that contains the merged assay and associated clinical
annotation. Noticed that GSE74092 is
usually removed from merging, because it used quantitative PCR, which
did not have enough coverage to capture all genes.
There are two arguments in the combine_objects() function.
The first one is object_list, which takes a list of MultiAssayExperiment
objects obtained from curatedTBData(). Notice that the
names(object_list) should not be NULL and must
be unique for each object within the list, so that we can keep track the
original study after merging. The second argument is
experiment_name, which can be a string or vector of strings
representing the name of the assay from the object.
## "update_genes" is TRUE, updating gene symbols## class: SummarizedExperiment
## dim: 25065 454
## metadata(0):
## assays(1): assay1
## rownames(25065): A1BG A1CF ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(454): GSM484368 GSM484369 ... GSM550399 GSM550400
## colData names(32): Age Gender ... StaphStatus StrepStatusWhen the objects are merged, the original individual data tag can be
found in the Study section from the metadata.
## [1] "GSE19435" "GSE19439" "GSE19442" "GSE19444" "GSE22098"It is also possible to combine the given list of objects with
different experiment names. In this case, the
experiment_name is a vector of string that corresponds to
each of object from the input list.
exp <- combine_objects(c(GSE79362[1], object_list[1]),
experiment_name = c("assay_reprocess_hg19",
"assay_curated"),
update_genes = TRUE)## Found more than one "experiment_name".## "update_genes" is TRUE, updating gene symbols## class: SummarizedExperiment
## dim: 18925 388
## metadata(0):
## assays(1): assay1
## rownames(18925): A1BG A1CF ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(388): GSM2092905 GSM2092906 ... GSM484399 GSM484400
## colData names(31): Age Gender ... DiabetesStatus TreatmentIf datasets are merged, it is typically recommended to remove a very
likely batch effect. We use the ComBat() function from
sva to
remove potential batch effect between studies.
In the following example, each study is viewed as one batch. The batch
corrected assay will be stored in a SummarizedExperiment
object.
batch1 <- colData(GSE19491)$Study
combat_edata1 <- sva::ComBat(dat = assay(GSE19491), batch = batch1)
assays(GSE19491)[["Batch_corrected_assay"]] <- combat_edata1
GSE19491## class: SummarizedExperiment
## dim: 25065 454
## metadata(0):
## assays(2): assay1 Batch_corrected_assay
## rownames(25065): A1BG A1CF ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(454): GSM484368 GSM484369 ... GSM550399 GSM550400
## colData names(32): Age Gender ... StaphStatus StrepStatusThe function subset_curatedTBData() allows the users to
subset a list of MultiAssayExperiment
with the output contains the exact conditions given by the
annotationCondition.
With subset_curatedTBData(), users can quickly subset
desired results from curatedTBData
database without checking individual object.
There are four arguments in this function. The theObject
represents a MultiAssayExperiment
or SummarizedExperiment
object. The annotationColName is a character that indicates
the column name in the metadata. The annotationCondition is
a character or vector of characters that the users intend to select.
In the following example, we call subset_curatedTBData()
function to subset samples with active TB (PTB) and latent
TB infection (LTBI) for binary classification.
multi_set_PTB_LTBI <- lapply(object_list, function(x)
subset_curatedTBData(x, annotationColName = "TBStatus",
annotationCondition = c("LTBI", "PTB"),
assayName = "assay_curated"))
# Remove NULL from the list
multi_set_PTB_LTBI <- multi_set_PTB_LTBI[!sapply(multi_set_PTB_LTBI, is.null)]
multi_set_PTB_LTBI[1:3]## $GSE19439
## class: SummarizedExperiment
## dim: 25417 30
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(30): GSM484448 GSM484449 ... GSM484488 GSM484489
## colData names(24): Age Gender ... DiabetesStatus QFT_GIT
##
## $GSE19442
## class: SummarizedExperiment
## dim: 25417 51
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(51): GSM484500 GSM484501 ... GSM484549 GSM484550
## colData names(23): Age Gender ... isolate_sensitivity DiabetesStatus
##
## $GSE19444
## class: SummarizedExperiment
## dim: 25417 42
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(42): GSM484595 GSM484596 ... GSM484645 GSM484646
## colData names(24): Age Gender ... DiabetesStatus QFT_GITThe HIV status (HIVStatus) and diabetes status
(DiabetesStatus) for each subject were also recorded for
each study in the curatedTBData.
In the following example, we select subjects with HIV positive from the
input.
Users can also find HIV status information for each study by looking at
the column: HIVStatus from DataSummary.
When the the length of the annotationCondition equals to 1,
we can subset using either MultiAssayExperiment built-in
procedure or subset_curatedTBData.
multi_set_HIV <- lapply(object_list, function(x)
subset_curatedTBData(x, annotationColName = "HIVStatus",
annotationCondition = "Negative",
assayName = "assay_curated"))
# Remove NULL from the list
multi_set_HIV <- multi_set_HIV[!vapply(multi_set_HIV, is.null, TRUE)]
multi_set_HIV[1:3]## $GSE19435
## class: SummarizedExperiment
## dim: 25417 33
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(33): GSM484368 GSM484369 ... GSM484399 GSM484400
## colData names(24): Age Gender ... DiabetesStatus Treatment
##
## $GSE19439
## class: SummarizedExperiment
## dim: 25417 42
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(42): GSM484448 GSM484449 ... GSM484488 GSM484489
## colData names(24): Age Gender ... DiabetesStatus QFT_GIT
##
## $GSE19442
## class: SummarizedExperiment
## dim: 25417 51
## metadata(0):
## assays(1): assay1
## rownames(25417): 7A5 A1BG ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(51): GSM484500 GSM484501 ... GSM484549 GSM484550
## colData names(23): Age Gender ... isolate_sensitivity DiabetesStatus## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 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-r0.3.32.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] sva_3.60.0 BiocParallel_1.46.0
## [3] genefilter_1.94.0 mgcv_1.9-4
## [5] nlme_3.1-171 SummarizedExperiment_1.42.0
## [7] Biobase_2.72.0 GenomicRanges_1.64.0
## [9] Seqinfo_1.2.0 IRanges_2.46.0
## [11] S4Vectors_0.50.3 BiocGenerics_0.58.1
## [13] generics_0.1.4 MatrixGenerics_1.24.0
## [15] matrixStats_1.5.0 dplyr_1.2.1
## [17] curatedTBData_2.8.0 BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 blob_1.3.0
## [3] filelock_1.0.3 Biostrings_2.80.2
## [5] fastmap_1.2.0 BiocFileCache_3.2.0
## [7] XML_3.99-0.25 digest_0.6.39
## [9] lifecycle_1.0.5 statmod_1.5.2
## [11] survival_3.8-12 KEGGREST_1.52.2
## [13] RSQLite_3.53.3 magrittr_2.0.5
## [15] compiler_4.6.1 rlang_1.3.0
## [17] sass_0.4.10 tools_4.6.1
## [19] yaml_2.3.12 data.table_1.18.6.1
## [21] knitr_1.52 htmlwidgets_1.6.4
## [23] S4Arrays_1.12.1 bit_4.6.0
## [25] curl_8.1.0 splitstackshape_1.4.8.1
## [27] DelayedArray_0.38.2 abind_1.4-8
## [29] purrr_1.2.2 withr_3.0.3
## [31] sys_3.4.3 grid_4.6.1
## [33] ExperimentHub_3.2.2 xtable_1.8-8
## [35] edgeR_4.10.6 MultiAssayExperiment_1.38.0
## [37] cli_3.6.6 rmarkdown_2.32
## [39] crayon_1.5.3 otel_0.2.0
## [41] httr_1.4.9 BiocBaseUtils_1.14.2
## [43] DBI_1.3.0 cachem_1.1.0
## [45] splines_4.6.1 parallel_4.6.1
## [47] AnnotationDbi_1.74.0 BiocManager_1.30.27
## [49] XVector_0.52.0 vctrs_0.7.3
## [51] Matrix_1.7-6 jsonlite_2.0.0
## [53] bit64_4.8.6 crosstalk_1.2.2
## [55] maketools_1.3.2 locfit_1.5-9.12
## [57] limma_3.68.5 jquerylib_0.1.4
## [59] annotate_1.90.0 glue_1.8.1
## [61] codetools_0.2-20 DT_0.34.0
## [63] BiocVersion_3.23.1 tibble_3.3.1
## [65] pillar_1.11.1 rappdirs_0.3.4
## [67] htmltools_0.5.9 R6_2.6.1
## [69] dbplyr_2.6.0 httr2_1.3.0
## [71] evaluate_1.0.5 lattice_0.23-1
## [73] AnnotationHub_4.2.2 png_0.1-9
## [75] memoise_2.0.1 bslib_0.12.0
## [77] SparseArray_1.12.3 HGNChelper_0.8.15
## [79] xfun_0.61 buildtools_1.0.0
## [81] pkgconfig_2.0.3