Tuberculosis (TB) is the leading cause of infectious disease mortality worldwide, causing on average nearly 1.4 million deaths per year. A consistent issue faced in controlling TB outbreak is difficulty in diagnosing individuals with particular types of TB infections for which bacteria tests (e.g., via GeneXpert, sputum) prove inaccurate. As an alternative mechanism of diagnosis for these infections, researchers have discovered and published multiple gene expression signatures as blood-based disease biomarkers. In this context, gene signatures are defined as a combined group of genes with a uniquely characteristic pattern of gene expression that occurs as a result of a medical condition. To date, more than 75 signatures have been published by researchers, though most have relatively low cross-condition validation (e.g., testing TB in samples from diverse geographic and comorbidity backgrounds). Furthermore, these signatures have never been formally collected and made available as a single unified resource.
We aim to provide the scientific community with a resource to access these aggregated signatures and to create an efficient means for their visual and quantitative comparison via open source software. This necessitated the development of the TBSignatureProfiler, a novel R package which delivers a computational profiling platform for researchers to characterize the diagnostic ability of existing signatures in multiple comorbidity settings. This software allows for signature strength estimation via several enrichment methods and subsequent visualization of single- and multi-pathway results. Its signature evaluation functionalities include signature profiling, AUC bootstrapping, and leave-one-out cross-validation (LOOCV) of logistic regression to approximate TB samples’ status. Its plotting functionalities include sample-signature score heatmaps, bootstrap AUC and LOOCV boxplots, and tables for presenting results.
More recently, the TBSignatureProfiler has undertaken a new role in
analyzing signatures across multiple chronic airway diseases, the most
recent being COVID-19 (see the COVIDsignatures object). As
we grow and expand the TBSignatureProfiler, we hope to add signatures
from multiple diseases to improve the package’s utility in the area of
gene signature comparison.
In order to install the TBSignatureProfiler from Bioconductor, run the following code:
While the TBSignatureProfiler often allows for the form of a
data.frame or matrix as input data, the most
ideal form of input for this package is that of the
SummarizedExperiment object. This is an amazing data
structure that is being developed by the as part of the SummarizedExperiment
package, and is imported as part of the TBSignatureProfiler package. It
is able to store data matrices along with annotation information,
metadata, and reduced dimensionality data (PCA, t-SNE, etc.). To learn
more about proper usage and context of the
SummarizedExperiment object, you may want to take a look at
the package
vignette. A basic understanding of the assay and
colData properties of a SummarizedExperiment
will be useful for the purposes of this vignette.
This command is to start the TBSignatureProfiler shiny app. The Shiny app implements several functions described in this vignette.
The basic functions of the shiny app are also included in the command line version of the TBSignatureProfiler, which is the focus of the remainder of this vignette.
In this tutorial, we will work with HIV and Tuberculosis (TB) gene
expression data in a SummarizedExperiment format. This
dataset is included in the TBSignatureProfiler package and can be loaded
into the global environment with data("TB_hiv"). The 31
samples in the dataset are marked as either having both TB and HIV
infection, or HIV infection only.
We begin by examining the dataset, which contains a matrix of counts information (an “assay” in SummarizedExperiment terms) and another matrix of meta data information on our samples (the “colData”). We will also generate a few additional assays; these are the log(counts), the counts per million (CPM) reads mapped, and the log(CPM) assays.
## HIV/TB gene expression data, included in the package
hivtb_data <- get0("TB_hiv", envir = asNamespace("TBSignatureProfiler"))
### Note that we have 25,369 genes, 33 samples, and 1 assay of counts
dim(hivtb_data)## [1] 25369 31## List of length 1
## names(1): countsWe now make a log counts, CPM and log CPM assay.
## Make a log counts, CPM and log CPM assay
hivtb_data <- mkAssay(hivtb_data, log = TRUE, counts_to_CPM = TRUE)
### Check to see that we now have 4 assays
assays(hivtb_data)## List of length 4
## names(4): counts log_counts counts_cpm log_counts_cpmThe TBSignatureProfiler enables comparison of multiple Tuberculosis
gene signatures. The package currently contains information on 79
signatures for comparison. The default signature list object for most
functions here is TBsignatures, although a list with
publication-given signature names is also available as
TBcommon. Data frames of annotation information for these
signatures, including information on associated disease and tissue type,
can be accessed as sigAnnotData and
common_sigAnnotData respectively.
With the runTBSigProfiler function, we are able to score
these signatures with a selection of algorithms, including gene
set variation analysis (GSVA) (Hänzelmann et al, 2013), single-sample
GSEA (ssGSEA) (Barbie et al, 2009), and the ASSIGN
pathway profiling toolkit (Shen et al, 2015). For a complete list of
included scoring methods, run ?runTBsigProfiler in the
terminal.
Here, we evaluate all signatures included in the package with ssGSEA. Paraphrasing from the ssGSEA documentation, for each pairing of one of the 31 samples and its gene set, ssGSEA calculates a separate enrichment score independent of the phenotypic labeling (in this case, whether a sample has HIV/TB, or HIV only). The single sample’s gene expression profile is then transformed to a gene set enrichment profile. A score from the set profile represents the activity level of the biological process in which the gene set’s members are coordinately up- or down-regulated.
## [1] "Anderson_42" "Anderson_OD_51" "Berry_393"
## [4] "Berry_OD_86" "Blankley_380" "Blankley_5"
## [7] "Bloom_OD_144" "Bloom_RES_268" "Bloom_RES_558"
## [10] "Chen_5" "Chen_HIV_4" "Chendi_HIV_2"
## [13] "Darboe_RISK_11" "Dawany_HIV_251" "Duffy_23"
## [16] "Esmail_203" "Esmail_82" "Esmail_OD_893"
## [19] "Estevez_133" "Estevez_259" "Francisco_OD_2"
## [22] "Gjoen_10" "Gjoen_7" "Gliddon_2_OD_4"
## [25] "Gliddon_HIV_3" "Gliddon_OD_3" "Gliddon_OD_4"
## [28] "Gong_OD_4" "Heycken_FAIL_22" "Hoang_OD_13"
## [31] "Hoang_OD_20" "Hoang_OD_3" "Huang_OD_13"
## [34] "Jacobsen_3" "Jenum_8" "Kaforou_27"
## [37] "Kaforou_OD_44" "Kaforou_OD_53" "Kaul_3"
## [40] "Kulkarni_HIV_2" "Kwan_186" "LauxdaCosta_OD_3"
## [43] "Lee_4" "Leong_24" "Leong_RISK_29"
## [46] "Li_3" "Long_RES_10" "Maertzdorf_15"
## [49] "Maertzdorf_4" "Maertzdorf_OD_100" "Natarajan_7"
## [52] "PennNich_RISK_6" "Qian_OD_17" "Rajan_HIV_5"
## [55] "Roe_3" "Roe_OD_4" "Sambarey_HIV_10"
## [58] "Singhania_OD_20" "Sivakumaran_11" "Sloot_HIV_2"
## [61] "Suliman_4" "Suliman_RISK_2" "Suliman_RISK_4"
## [64] "Sweeney_OD_3" "Tabone_OD_11" "Tabone_RES_25"
## [67] "Tabone_RES_27" "Thompson_9" "Thompson_FAIL_13"
## [70] "Thompson_RES_5" "Tornheim_71" "Tornheim_RES_25"
## [73] "Vargas_18" "Vargas_42" "Verhagen_10"
## [76] "Walter_51" "Walter_PNA_119" "Walter_PNA_47"
## [79] "Zak_RISK_16" "Zhao_NANO_6"A more complete tutorial is available by going to our website, and selecting the tab labeled “Command Line Analysis.”
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## time zone: Etc/UTC
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##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [3] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [5] IRanges_2.41.2 S4Vectors_0.45.2
## [7] BiocGenerics_0.53.3 generics_0.1.3
## [9] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [11] TBSignatureProfiler_1.19.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] Matrix_1.7-1 limma_3.63.2 jsonlite_1.8.9
## [4] crayon_1.5.3 compiler_4.4.2 BiocManager_1.30.25
## [7] jquerylib_0.1.4 statmod_1.5.0 yaml_2.3.10
## [10] fastmap_1.2.0 lattice_0.22-6 R6_2.5.1
## [13] XVector_0.47.0 S4Arrays_1.7.1 knitr_1.49
## [16] DelayedArray_0.33.3 maketools_1.3.1 GenomeInfoDbData_1.2.13
## [19] bslib_0.8.0 rlang_1.1.4 cachem_1.1.0
## [22] xfun_0.49 sass_0.4.9 sys_3.4.3
## [25] SparseArray_1.7.2 cli_3.6.3 magrittr_2.0.3
## [28] zlibbioc_1.52.0 locfit_1.5-9.10 grid_4.4.2
## [31] digest_0.6.37 edgeR_4.5.1 lifecycle_1.0.4
## [34] evaluate_1.0.1 buildtools_1.0.0 abind_1.4-8
## [37] rmarkdown_2.29 httr_1.4.7 tools_4.4.2
## [40] htmltools_0.5.8.1 UCSC.utils_1.3.0