Package 'TBSignatureProfiler'

Description

A function to obtain predicted score for TB gene signatures that do not need to be retrained.

Usage

.OriginalModel_NoRetraining(input, useAssay, geneSignaturesName, BPPARAM)

Arguments

Details

Anderson_42 and Anderson_OD_51 used difference of sums to calculate prediction scores. Difference of sums is obtained by subtracting the sum of the expression of genes within signatures that are down-regulated from the sum of the expression of genes that are up-regulated within signatures. Kaforou_27, Kaforou_OD_44, and Kaforou_OD_53 used difference of arithmetic means to calculate prediction scores. Sweeney_OD_3 used difference of arithmetic mean to calculate prediction score.

Value

A SummarizedExperiment object with predicted scores for each sample obtained from the signature's original model.

Description

A function to obtain predicted score for TB gene signatures that need retraining of original models.

Usage

.OriginalModel_Retraining(input, useAssay, geneSignaturesName, adj, BPPARAM)

Arguments

Details

Maertzdorf_4 and Maertzdorf_15 were trained using a random forest to distinguish patients with active TB from healthy controls.

Verhagen_10 was also trained using a random forest to distinguish samples with active TB from either latent infection or healthy controls. The random forest model was build using randomForest.

Jacobsen_3 were trained using linear discriminant analysis (LDA) to distinguish samples with active TB from latent infection status.

Sambarey_HIV_10 were also trained using LDA to distinguish samples with active TB from either latent infection, healthy control, or other disease (HIV). The LDA model was built using lda.

Berry_OD_86 and Berry_393 were trained using K-nearest neighbors (KNN) model to differentiate samples with active TB from latent infection status. The KNN model was built using knn.

Suliman_RISK_4 and Zak_RISK_16 were trained using support vector machines (SVM) to distinguish TB progressor from non-progressors. The input gene expression features for Suliman_RISK_4 used the paired ratio of GAS6/CD1C, SEPTIN4/BLK, SEPTIN4/CD1C, GAS6/BLK. The SVM model was built using svm.

Value

A SummarizedExperiment object with predicted scores for each sample obtained from the signature's original model.

Description

This function allows users to integrate new signatures into the TBSP with a function that updates the TBsignatures, TBcommon, sigAnnotData and common_sigAnnotData objects. Users that wish to use this function should do so with the downloaded package as a working directory, and not as a casual package function. This function does not complete all required updates to the package for a signature to be full added; users should check the vignette "Submitting Signatures to the TBSP Package" on the TBSP website for a walkthrough of this complete process. Also note that this function only adds one signature at a time, and must me run multiple times to add subsequent signatures.

Usage

addTBsignature(
  sigsymbols,
  authname,
  signame_common = NULL,
  sigtype,
  tissuetype,
  saveobjs = FALSE,
  views = TRUE
)

Arguments

Value

Either data objects TBsignatures, TBcommon, sigAnnotData, and common_sigAnnotData will be updated with the new signature and overwritten if saveobjs = FALSE, or no output will be produced except errors and messages for checking that the function runs correctly given the inputs.

Examples

# Mock example signature
TBSignatureProfiler:::addTBsignature(sigsymbols = c("GBP5", "BATF2", "GZMA"),
                            authname = "Odom",
                            signame_common = NULL,
                            sigtype = "Disease/HIV",
                            tissuetype = "PBMC",
                            saveobjs = FALSE,
                            views = FALSE)

Description

Bootstrap on Leave-one-out CV with Logistic Regression.

Usage

Bootstrap_LOOCV_LR_AUC(df, targetVec, nboot)

Arguments

Value

A list of length 2 with elements

Description

Run bootstrapping of the AUC and derive the p-value for a 2-sample t-test for all signatures tested on a given dataset.

Usage

bootstrapAUC(
  SE_scored,
  annotationColName,
  signatureColNames,
  num.boot = 100,
  pb.show = TRUE
)

Arguments

Value

A list of length 5 returning a vector of p-values for a 2-sample t-test, bootstrapped AUC values, an AUC value for using all scored values for all signatures specified in signatureColNames, and values for the lower and upper bounds of a bootstrapped AUC confidence interval using pROC::roc().

Examples

# Run signature profiling
 choose_sigs <- list("madeupsig" = c("FCRL3", "OAS2", "IFITM3"))
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "ssGSEA",
                                 combineSigAndAlgorithm = TRUE,
                                 signatures = choose_sigs,
                                 parallel.sz = 1)
 # Bootstrapping
 booted <- bootstrapAUC(SE_scored = prof_indian, annotationColName = "label",
                        signatureColNames = names(choose_sigs), num.boot = 2)
 booted

Description

A data.frame of annotation information for published tuberculosis signatures. This table differs from that of sigAnnotData as it refers to signatures via the name given in scientific publications, and via a consistent naming system otherwise. Currently, this table includes two variables, disease and tissue type.

Usage

common_sigAnnotData

Format

data.frame

Details

The disease variable indicates whether the signature was developed to distinguish TB from LTBI ("Disease"), TB from some combination of other diseases and possibly LTBI ("OD"), TB from Human Immunodeficiency Virus ("HIV"), TB from pneumonia ("PNA"), or identify risk of progression to TB ("RISK"), risk of TB treatment failure ("FAIL"), or classify treatment responses (i.e., failures from cures, "RES").

The tissue type variable denotes whether the signature was developed using samples of either whole blood/paxgene or peripheral blood mononuclear cells (PBMCs). Due to the manipulation of cells inherently required to obtain PBMCs, many scientists prefer to use only whole blood samples for analysis.

Source

See ?TBcommon for reference information.

Examples

data("common_sigAnnotData")

Description

It may be useful to compare the results of scoring across several different scoring algorithms via a method of visualization, such as a heatmap. The compareSigs function allows the input of a SummarizedExperiment data object and conducts profiling on each signature desired, and outputting a heatmap or boxplot for each signature.

Usage

compareAlgs(
  input,
  signatures = NULL,
  annotationColName,
  useAssay = "counts",
  algorithm = c("GSVA", "ssGSEA", "ASSIGN", "PLAGE", "Zscore", "singscore"),
  showColumnNames = TRUE,
  showRowNames = TRUE,
  scale = FALSE,
  colorSets = c("Set1", "Set2", "Set3", "Pastel1", "Pastel2", "Accent", "Dark2",
    "Paired"),
  choose_color = c("blue", "gray95", "red"),
  colList = list(),
  show.pb = FALSE,
  parallel.sz = 0,
  output = "heatmap",
  num.boot = 100,
  column_order = NULL
)

Arguments

Value

A heatmap or boxplot for each signature specified comparing the enumerated algorithms.

Examples

compareAlgs(TB_indian,
            signatures = TBsignatures[c("Gliddon_OD_3")],
            annotationColName = "label",
            algorithm = c("ssGSEA", "PLAGE"),
            scale = TRUE, parallel.sz = 1, output = "heatmap")

Description

Present the results of AUC bootstrapping for a collection of scored signatures via boxplots.

Usage

compareBoxplots(
  SE_scored,
  annotationColName,
  signatureColNames,
  num.boot = 100,
  name = "Boxplot Comparison of Signature AUCs",
  pb.show = TRUE,
  abline.col = "red",
  fill.col = "gray79",
  outline.col = "black",
  rotateLabels = FALSE,
  violinPlot = FALSE
)

Arguments

Value

A plot with side-by-side boxplots of bootstrapped AUC values for each specified signature.

Examples

# Run signature profiling
 choose_sigs <- TBsignatures[c("Zak_RISK_16", "Zhao_NANO_6")]
 prof_indian <- runTBsigProfiler(TB_indian[seq_len(25), ],
                                 useAssay = "logcounts",
                                 algorithm = "ssGSEA",
                                 signatures = choose_sigs,
                                 parallel.sz = 1)
 # Create boxplots
 compareBoxplots(prof_indian, annotationColName = "label",
                 signatureColNames = names(choose_sigs), rotateLabels = TRUE)

Description

A set of 47 COVID-19 gene signatures from various single-cell and bulk RNA-seq publications and preprint manuscripts from early- to mid-2020. This set of signatures uses gene symbols.

Usage

COVIDsignatures

Format

list

Details

Signature names are composed of the last name of the primary author, followed by the type of sequencing data from which the signature was derived, the tissue from which the signature was derived, and ending with a reference to the cell type, infection status, or disease to which the signature belongs, as defined in the original publication.

Note that in some cases signatures will be positive identifiers of COVID-19 as positive markers of immune cell clusters are often provided for single-cell RNA-seq data; this should be taken into account when creating ROC curves and computing any AUC or disease risk estimates.

Source

• Wilk_sc_PBMC_monocytes_up: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMC_monocytes_up: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMC_monocytes_down: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMC_NK_cells_up: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMC_NK_cells_down: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMCs_ISG_signature: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Wilk_sc_PBMC_activated_granulocytes: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "A single-cell atlas of the peripheral immune response in patients with severe COVID-19." Nature Medicine 26 (7): 1070-1076. https://doi.org/10.1038/s41591-020-0944-y

• Huang_sc_PBMC_IFN_signature: Wilk, A.J., Rustagi, A., Zhao, N.Q. et al. 2020. "Blood single cell immune profiling reveals the interferon-MAPK pathway mediated adaptive immune response for COVID-19." medRxiv.org: https://doi.org/10.1101/2020.03.15.20033472

• Wen_sc_PBMC_monocytes: Wen, W., Su, W., Tang, H. et al. 2020. "Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing." Cell Discovery 6 (31). https://doi.org/10.1038/s41421-020-0168-9

• Wen_sc_PBMC_NK_cells: Wen, W., Su, W., Tang, H. et al. 2020. "Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing." Cell Discovery 6 (31). https://doi.org/10.1038/s41421-020-0168-9

• Wen_sc_PBMC_CD4_T_cells: Wen, W., Su, W., Tang, H. et al. 2020. "Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing." Cell Discovery 6 (31). https://doi.org/10.1038/s41421-020-0168-9

• Wen_sc_PBMC_CD8_T_cells: Wen, W., Su, W., Tang, H. et al. 2020. "Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing." Cell Discovery 6 (31). https://doi.org/10.1038/s41421-020-0168-9

• Wen_sc_PBMC_B_cells: Wen, W., Su, W., Tang, H. et al. 2020. "Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing." Cell Discovery 6 (31). https://doi.org/10.1038/s41421-020-0168-9

• Xiong_bulk_PBMC_gene_signature_up: Xiong Y, Liu Y, Cao L, et al. 2020. "Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients." Emerging Microbes & Infections 9 (1):761-770. https://doi/org/10.1080/22221751.2020.1747363

• Xiong_bulk_PBMC_gene_signature_down: Xiong Y, Liu Y, Cao L, et al. 2020. "Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients." Emerging Microbes & Infections 9 (1):761-770. https://doi/org/10.1080/22221751.2020.1747363

• Xiong_sc_PBMC_cytokines_up: Xiong Y, Liu Y, Cao L, et al. 2020. "Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients." Emerging Microbes & Infections 9 (1):761-770. https://doi/org/10.1080/22221751.2020.1747363

• Xiong_sc_PBMC_cytokines_down: Xiong Y, Liu Y, Cao L, et al. 2020. "Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients." Emerging Microbes & Infections 9 (1):761-770. https://doi/org/10.1080/22221751.2020.1747363

• Liao_sc_BALF_G1_macrophages: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_G1_2_macrophages: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_G2_macrophages: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_G3_macrophages: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_G4_macrophages: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_CD8_T_cells_up: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Liao_sc_BALF_CD8_T_cells_down: Liao, M., Liu, Y., Yuan, J. et al. 2020. "Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19." Nature Medicine 26 (6): 842-844. https://doi.org/10.1038/s41591-020-0901-9

• Hadjadj_nanostring_WB_gene_signature_up: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_gene_signature_down: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_ISG_signature: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_mild_moderate_up: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_mild_moderate_down: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_severe_up: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_WB_severe_down: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_critical_up: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Hadjadj_nanostring_critical_down: Hadjadj J, Yatim N, Barnabei L, et al. 2020. "Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients." Science 369 (6504): 718-724. https://doi.org/10.1126/science.abc6027

• Wei_sc_PBMC_inactivated_monocytes: Wei et al. 2020. "Viral Invasion and Type I Interferon Response Characterize the Immunophenotypes during COVID-19 Infection." SSRN: https://dx.doi.org/10.2139/ssrn.3555695

• Wei_sc_PBMC_classical_monocytes: Wei et al. 2020. "Viral Invasion and Type I Interferon Response Characterize the Immunophenotypes during COVID-19 Infection." SSRN: https://dx.doi.org/10.2139/ssrn.3555695

• Wei_sc_PBMCs_T_cells: Wei et al. 2020. "Viral Invasion and Type I Interferon Response Characterize the Immunophenotypes during COVID-19 Infection." SSRN: https://dx.doi.org/10.2139/ssrn.3555695

• Wei_sc_PBMC_B_cells: Wei et al. 2020. "Viral Invasion and Type I Interferon Response Characterize the Immunophenotypes during COVID-19 Infection." SSRN: https://dx.doi.org/10.2139/ssrn.3555695

• Silvin_sc_WB_combined_signature: Silvin A, Chapuis N, Dunsmore G, et al. 2020. "Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19." Cell 182 (6): 1401-1418.E18. https://doi.org/10.1016/j.cell.2020.08.002

• Silvin_sc_WB_monocytes_up: Silvin A, Chapuis N, Dunsmore G, et al. 2020. "Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19." Cell 182 (6): 1401-1418.E18. https://doi.org/10.1016/j.cell.2020.08.002

• Silvin_sc_WB_monocytes_down: Silvin A, Chapuis N, Dunsmore G, et al. 2020. "Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19." Cell 182 (6): 1401-1418.E18. https://doi.org/10.1016/j.cell.2020.08.002

• Silvin_sc_WB_neutrophils_up: Silvin A, Chapuis N, Dunsmore G, et al. "Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19." Cell 182 (6): 1401-1418.E18. https://doi.org/10.1016/j.cell.2020.08.002

• Silvin_sc_WB_neutrophils_down: Silvin A, Chapuis N, Dunsmore G, et al. "Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19." Cell 182 (6): 1401-1418.E18. https://doi.org/10.1016/j.cell.2020.08.002

• Arunachalam_bulk_PBMC_blood_modules: Arunachalam PS, Wimmers F, Mok CKP, et al. 2020. "Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans." Science 369 (6508): 1210-1220. https://doi.org/10.1126/science.abc6261

• Arunachalam_bulk_PBMC_covid_combined: Arunachalam PS, Wimmers F, Mok CKP, et al. 2020. "Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans." Science 369 (6508): 1210-1220. https://doi.org/10.1126/science.abc6261

• Arunachalam_bulk_PBMC_moderate: Arunachalam PS, Wimmers F, Mok CKP, et al. 2020. "Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans." Science 369 (6508): 1210-1220. https://doi.org/10.1126/science.abc6261

• Arunachalam_bulk_PBMC_severe: Arunachalam PS, Wimmers F, Mok CKP, et al. 2020. "Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans." Science 369 (6508): 1210-1220. https://doi.org/10.1126/science.abc6261

• Arunachalam_bulk_PBMC_intensive_care: Arunachalam PS, Wimmers F, Mok CKP, et al. 2020. "Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans." Science 369 (6508): 1210-1220. https://doi.org/10.1126/science.abc6261

• Dunning_bulk_WB_flu: Dunning J, Blankley S, Hoang LT, et al. 2018. "Progression of whole-blood transcriptional signatures from interferon-induced to neutrophil-associated patterns in severe influenza." Nature Immunology 19 (6): 625-635. https://doi.org/10.1038/s41590-018-0111-5

Examples

data("COVIDsignatures")

Description

Train original model for gene signatures Leong_24, Leong_RISK_29, Zhao_NANO_6 using lasso logistic regression.

Usage

cv_glmnet_OriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

Normalize gene expression count data.

Usage

deseq2_norm_rle(inputData)

Arguments

Value

A data.frame or matrix of normalized count data.

Examples

## Example using the counts assay from a SummarizedExperiment
data_in <- SummarizedExperiment::assay(TB_indian, "counts")
res <- deseq2_norm_rle(data_in)

Description

Create a distinct palette for coloring different heatmap clusters. The function returns colors for input into ComplexHeatmap:Heatmap(), signatureGeneHeatmap() and signatureHeatmap().

Usage

distinctColors(
  n,
  hues = c("red", "cyan", "orange", "blue", "yellow", "purple", "green", "magenta"),
  saturation.range = c(0.7, 1),
  value.range = c(0.7, 1)
)

Arguments

Value

A vector of distinct colors that have been converted to HEX from HSV.

Examples

distinctColors(10)

Description

This function computes prediction for multiple TB signatures based on their training models/methods. To avoid naming issues, the gene names for both training data and input gene sets have been updated using the checkGeneSymbols. TB signatures with available original models are: Anderson_42, Anderson_OD_51, Kaforou_27, Kaforou_OD_44, Kaforou_OD_53, Sweeney_OD_3, Maertzdorf_4, Verhagen_10, Jacobsen_3, Sambarey_HIV_10, Leong_24, Berry_OD_86, Berry_393, Bloom_OD_144, Suliman_RISK_4, Zak_RISK_16, Leong_RISK_29, and Zhao_NANO_6. The predicted score for each signature has been stored in the column data section of the input SummarizedExperiment study.

Usage

evaluateOriginalModel(
  input,
  geneSignaturesName,
  useAssay = 1,
  adj = 0.001,
  BPPARAM = BiocParallel::SerialParam(progressbar = TRUE)
)

Arguments

Value

A SummarizedExperiment object with predicted scores for each sample obtained from the signature's original model.

Examples

re <- evaluateOriginalModel(input = TB_indian,
                            geneSignaturesName = c("Anderson_42"),
                            useAssay = "counts")
re$Anderson_42_OriginalModel

Description

Train original model for gene signatures Berry_393 and Berry_OD_86.

Usage

knn_OriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

Train original model for gene signatures Jacobsen_3 and Sambarey_HIV_10.

Usage

lda_OriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

Perform Leave-one-out CV with Logistic Regression.

Usage

LOOAUC_simple_multiple_noplot_one_df(df, targetVec)

Arguments

Value

A list of length 3 with elements

Description

Given a SummarizedExperiment input with a counts or CPM assay, this function creates additional assays for by computing the CPM, log, or both of the input assay to be used in further analysis.

Usage

mkAssay(
  SE_obj,
  input_name = "counts",
  output_name = NULL,
  log = FALSE,
  counts_to_CPM = TRUE,
  prior_counts = 3
)

Arguments

Value

This function returns a SummarizedExperiment object with up to 3 additional assay types attached to the original inputted object.

Author(s)

Aubrey Odom-Mabey

Examples

# Create a log assay of the original assay input
# TB_hiv dataset already has counts data
log_only <- mkAssay(TB_hiv, log = TRUE, counts_to_CPM = FALSE)
log_only

# Create a CPM assay
CPM_only <- mkAssay(TB_hiv)
CPM_only

# Create a logCPM, logcounts, and CPM assay
all_assays <- mkAssay(TB_hiv, log = TRUE)
all_assays

Description

Obtain training data, testing data, and train signature's original model.

Usage

ObtainSampleScore_OriginalModel(
  theObject_train,
  useAssay,
  gene_set,
  input,
  SigName,
  obtainDiagnosis,
  annotationColName,
  FUN,
  adj
)

Arguments

Value

The predicted score for each sample in the test study using corresponding gene signature's original model.

Description

Discovery datasets for corresponding gene signatures.

Usage

OriginalTrainingData

Format

list

Source

See ?TBsignatures for reference information.

Examples

data("OriginalTrainingData")

Description

This function takes as input a data.frame with genetic expression count data, and uses a bootstrapped leave-one-out cross validation procedure with logistic regression to allow for numeric and graphical comparison across any number of genetic signatures. It creates a boxplot of bootstrapped AUC values.

Usage

plotQuantitative(
  df.input,
  targetVec.num,
  signature.list = NULL,
  signature.name.vec = NULL,
  num.boot = 100,
  pb.show = TRUE,
  name = "Signature Evaluation: Bootstrapped AUCs",
  fill.col = "white",
  outline.col = "black",
  abline.col = "red",
  rotateLabels = FALSE
)

Arguments

Value

a boxplot comparing the bootstrapped AUCs of inputted signatures

Examples

inputTest <- matrix(rnorm(1000), 100, 20,
                    dimnames = list(paste0("gene", seq.int(1, 100)),
                                    paste0("sample", seq.int(1, 20))))
inputTest <- as.data.frame(inputTest)
targetVec <- sample(c(0,1), replace = TRUE, size = 20)
signature.list <- list(sig1 = c("gene1", "gene2", "gene3"),
                       sig2 = c("gene4", "gene5", "gene6"))
signature.name.vec <- c("sig1", "sig2")
num.boot <- 5
plotQuantitative(inputTest, targetVec.num = targetVec,
                 signature.list = signature.list,
                 signature.name.vec = signature.name.vec,
                 num.boot = num.boot, rotateLabels = FALSE)

Description

Train original model for gene signatures Maertzdorf_4, Maertzdorf_15, Verhagen_10, and LauxdaCosta_OD_3.

Usage

randomForest_OriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

A function used to perform reference batch correction and imputation in the testing data for gene signatures that require retraining of the model. We used the k-nearest neighbors to impute the expression values for missing gene(s). The imputation operation is achieved using impute.knn. Since the computational time for the imputation step can be excessive for large number of missing genes. We made some constrains to prevent the overflow of imputation operation. The evaluation will not run if more than geneMax*100\ of the genes are not found for the corresponding gene signature in the input study. By default geneMax = 0.8, so the evaluation will not run if more than 80\ of the genes are missing when matching the input study to the reference data.

Usage

ref_combat_impute(
  theObject_train,
  useAssay,
  gene_set,
  input,
  SigName,
  adj,
  geneMax = 0.8
)

Arguments

Value

Gene set subset

Description

Using some subset of the signatures listed in TBsignatures and specified scoring algorithms, this function runs gene signature profiling on an input gene expression dataset. It allows for scores to be computed for these signatures which can be compared using various visualization tools also provided in the TBSignatureProfiler package.

Usage

runTBsigProfiler(
  input,
  useAssay = NULL,
  signatures = NULL,
  algorithm = c("GSVA", "ssGSEA", "ASSIGN", "PLAGE", "Zscore", "singscore"),
  combineSigAndAlgorithm = FALSE,
  assignDir = NULL,
  outputFormat = NULL,
  parallel.sz = 0,
  ASSIGNiter = 1e+05,
  ASSIGNburnin = 50000,
  ssgsea_norm = TRUE,
  update_genes = TRUE
)

Arguments

Value

A SummarizedExperiment object, data.frame, or matrix of signature profiling results. The returned object will be of the format specified in outputFormat. If input is a SummarizedExperiment and outputFormat = "SummarizedExperiment", then the output will retain any input information stored in the input colData. In general, if outputFormat = "SummarizedExperiment" then columns in the colData will include the scores for each desired signature with samples on the rows. If input is a data.frame or matrix, then the returned object will have signatures on the rows and samples on the columns.

Source

Profiling for the Z-Score, PLAGE, GSVA, ssGSEA algorithms are all conducted with the Bioconductor GSVA package. Profiling for the singscore algorithm is conducted with the Bioconductor singscore package.

References

Barbie, D.A., Tamayo, P., Boehm, J.S., Kim, S.Y., Moody, S.E., Dunn, I.F., Schinzel, A.C., Sandy, P., Meylan, E., Scholl, C., et al. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108-112. doi: 10.1038/nature08460.

Foroutan, M. et al. (2018). Single sample scoring of molecular phenotypes. BMC Bioinformatics, 19. doi: 10.1186/s12859-018-2435-4.

Lee, E. et al. (2008). Inferring pathway activity toward precise disease classification. PLoS Comp Biol, 4(11):e1000217. doi: 10.1371/journal.pcbi.1000217

Shen, Y. et al. (2015). ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways. Bioinformatics, 31, 1745-1753. doi: 10.1093/bioinformatics/btv031.

Subramanian, A. et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS, 102, 15545-15550. doi: 10.1073/pnas.0506580102.

Tomfohr, J. et al. (2005). Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics, 6:225. doi: 10.1186/1471-2105-6-225

Examples

## Using a data.frame input/output
 # Create some toy data to test Zak_RISK_16 signature, using 5 samples with low
 # expression & five samples with high expression of the signatures genes.
df_testdata <- as.data.frame(rbind(matrix(c(rnorm(80), rnorm(80) + 5), 16, 10,
                             dimnames = list(TBsignatures$Zak_RISK_16,
                             paste0("sample", seq_len(10)))),
                      matrix(rnorm(1000), 100, 10,
                             dimnames = list(paste0("gene", seq_len(100)),
                             paste0("sample", seq_len(10))))))
res <- runTBsigProfiler(input = df_testdata,
                        signatures = TBsignatures["Zak_RISK_16"],
                        algorithm = c("GSVA", "ssGSEA"),
                        combineSigAndAlgorithm = FALSE,
                        parallel.sz = 1)
subset(res, res$pathway == "Zak_RISK_16")

## Using a SummarizedExperiment input/output
 # The TB_indian SummarizedExperiment data is included in the package.
GSVA_res <- runTBsigProfiler(input = TB_indian,
                             useAssay = "logcounts",
                             signatures = TBsignatures["Zak_RISK_16"],
                             algorithm = c("GSVA"),
                             combineSigAndAlgorithm = FALSE,
                             parallel.sz = 1)
GSVA_res$Zak_RISK_16

Description

A data.frame of annotation information for published tuberculosis signatures. Currently, this table includes two variables, disease and tissue type.

Usage

sigAnnotData

Format

data.frame

Details

The disease variable indicates whether the signature was developed to distinguish TB from LTBI ("Disease"), TB from some combination of other diseases and possibly LTBI ("OD"), TB from Human Immunodeficiency Virus ("HIV"), TB from pneumonia ("PNA"), or identify risk of progression to TB ("RISK"), risk of TB treatment failure ("FAIL"), or classify treatment responses (i.e., failures from cures, "RES").

The tissue type variable denotes whether the signature was developed using samples of either whole blood/paxgene or peripheral blood mononuclear cells (PBMCs). Due to the manipulation of cells inherently required to obtain PBMCs, many scientists prefer to use only whole blood samples for analysis.

Source

See ?TBsignatures for reference information.

Examples

data("sigAnnotData")

Description

Plot a boxplot of signature genes.

Usage

signatureBoxplot(
  inputData,
  annotationData,
  signatureColNames,
  annotationColName,
  name = "Signatures",
  scale = FALSE,
  violinPlot = FALSE,
  includePoints = TRUE,
  notch = FALSE,
  rotateLabels = FALSE,
  nrow = NULL,
  ncol = NULL,
  fill_colors = NULL
)

Arguments

Value

A ggplot2 boxplot of the signature data using the provided annotation information.

Examples

library(SummarizedExperiment)

# Generate some artificial data that shows a difference in Zak_RISK_16
mat_testdata <- rbind(matrix(c(rnorm(80), rnorm(80) + 5), 16, 10,
                             dimnames = list(TBsignatures$Zak_RISK_16,
                                             paste0("sample", seq_len(10)))),
                      matrix(rnorm(1000), 100, 10,
                             dimnames = list(paste0("gene", seq_len(100)),
                                             paste0("sample", seq_len(10)))))

# Create a SummarizedExperiment object that contains the data
testdataSE <- SummarizedExperiment(assays = SimpleList(data = mat_testdata),
                                   colData = DataFrame(sample =
                                                         c(rep("down", 5),
                                                           rep("up", 5))))

# Run profiler using GSVA and ssGSEA on Zak_RISK_16 signature
res <- runTBsigProfiler(testdataSE, useAssay = "data",
                        signatures = TBsignatures["Zak_RISK_16"],
                        algorithm = c("GSVA", "ssGSEA"), parallel.sz = 1,
                        combineSigAndAlgorithm = TRUE)
signatureBoxplot(res, signatureColNames = c("GSVA_Zak_RISK_16",
                                            "ssGSEA_Zak_RISK_16"),
                 annotationColName = "sample", name = "Zak_RISK_16 Signature")

Description

This function takes the profiled gene expression data for a single signature and creates a heatmap based on the expression scores.

Usage

signatureGeneHeatmap(
  inputData,
  useAssay,
  sigGenes,
  name = "Signature",
  signatureColNames = NULL,
  annotationColNames = NULL,
  scale = TRUE,
  showColumnNames = TRUE,
  showRowNames = TRUE,
  colList = list(),
  colorSets = c("Set1", "Set2", "Set3", "Pastel1", "Pastel2", "Accent", "Dark2",
    "Paired"),
  choose_color = c("blue", "gray95", "red"),
  column_order = NULL,
  ...
)

Arguments

Value

A ComplexHeatmap plot.

Examples

library(SummarizedExperiment)
# Generate some artificial data that shows a difference in Zak_RISK_16
mat_testdata <- rbind(matrix(c(rnorm(80), rnorm(80) + 5), 16, 10,
                             dimnames = list(TBsignatures$Zak_RISK_16,
                                             paste0("sample", seq_len(10)))),
                      matrix(rnorm(1000), 100, 10,
                             dimnames = list(paste0("gene", seq_len(100)),
                                             paste0("sample", seq_len(10)))))

# Create a SummarizedExperiment object that contains the data
testdataSE <- SummarizedExperiment(assays = SimpleList(data = mat_testdata),
                                   colData = DataFrame(sample =
                                                       c(rep("down", 5),
                                                         rep("up", 5))))
# Run profiler using GSVA and ssGSEA on Zak_RISK_16
res <- runTBsigProfiler(testdataSE, useAssay = "data",
                        signatures = TBsignatures["Zak_RISK_16"],
                        algorithm = c("GSVA", "ssGSEA"), parallel.sz = 1,
                        combineSigAndAlgorithm = TRUE)

# Plot a heatmap of signature genes and pathway predictions
signatureGeneHeatmap(res, useAssay = "data",
                     sigGenes = TBsignatures[["Zak_RISK_16"]],
                     signatureColNames = c("GSVA_Zak_RISK_16",
                                           "ssGSEA_Zak_RISK_16"),
                     annotationColNames = c("sample"), showColumnNames = FALSE,
                     name = "Zak_RISK_16")

Description

This function takes a dataset of scored gene expression data as an input and returns a ComplexHeatmap plot for for visual comparison of signature performance. The function takes arguments listed here as well as any others to be passed on to ComplexHeatmap::Heatmap().

Usage

signatureHeatmap(
  inputData,
  annotationData = NULL,
  name = "Signatures",
  signatureColNames,
  annotationColNames = NULL,
  colList = list(),
  scale = FALSE,
  showColumnNames = TRUE,
  showRowNames = TRUE,
  colorSets = c("Set1", "Set2", "Set3", "Pastel1", "Pastel2", "Accent", "Dark2",
    "Paired"),
  choose_color = c("blue", "gray95", "red"),
  split_heatmap = "none",
  annotationSignature = sigAnnotData,
  column_order = NULL,
  ...
)

Arguments

Details

If both annotationData = NULL and annotationColNames = NULL, no annotation bar will be drawn on the heatmap.

Value

A ComplexHeatmap plot.

Examples

library(SummarizedExperiment)
# Generate some artificial data that shows a difference in Zak_RISK_16
mat_testdata <- rbind(matrix(c(rnorm(80), rnorm(80) + 5), 16, 10,
                             dimnames = list(TBsignatures$Zak_RISK_16,
                                             paste0("sample", seq_len(10)))),
                      matrix(rnorm(1000), 100, 10,
                             dimnames = list(paste0("gene", seq_len(100)),
                                             paste0("sample", seq_len(10)))))
# Create a SummarizedExperiment object that contains the data
testdataSE <- SummarizedExperiment(assays = SimpleList(data = mat_testdata),
                                     colData = DataFrame(sample =
                                                           c(rep("down", 5),
                                                                rep("up", 5))))
res <- runTBsigProfiler(testdataSE, useAssay = "data",
                        signatures = TBsignatures["Zak_RISK_16"],
                        algorithm = c("GSVA", "ssGSEA"), parallel.sz = 1,
                        combineSigAndAlgorithm = TRUE)
signatureHeatmap(res, signatureColNames = c("GSVA_Zak_RISK_16",
                                            "ssGSEA_Zak_RISK_16"),
                 annotationColNames = "sample", scale = TRUE,
                 showColumnNames = FALSE, split_heatmap = "none")

# Example using custom colors for the annotation information
color2 <- stats::setNames(c("purple", "black"), c("down", "up"))
color.list <- list("sample" = color2)

signatureHeatmap(res, signatureColNames = c("GSVA_Zak_RISK_16",
                                            "ssGSEA_Zak_RISK_16"),
                 annotationColNames = "sample", scale = TRUE,
                 showColumnNames = FALSE,
                 colList = color.list, split_heatmap = "none")

Description

This function takes as input a data.frame with genetic expression count data, and uses a bootstrapped leave-one-out cross validation procedure with logistic regression to allow for numeric and graphical comparison across any number of genetic signatures.

Usage

SignatureQuantitative(
  df.input,
  targetVec.num,
  signature.list = NULL,
  signature.name.vec = NULL,
  num.boot = 100,
  pb.show = TRUE
)

Arguments

Value

the AUC, sensitivity and specificity

Examples

inputTest <- matrix(rnorm(1000), 100, 20,
                    dimnames = list(paste0("gene", seq.int(1, 100)),
                                    paste0("sample", seq.int(1, 20))))
inputTest <- as.data.frame(inputTest)
targetVec <- sample(c(0,1), replace = TRUE, size = 20)
signature.list <- list(sig1 = c("gene1", "gene2", "gene3"),
                       sig2 = c("gene4", "gene5", "gene6"))
signature.name.vec <- c("sig1", "sig2")
num.boot <- 2
SignatureQuantitative(inputTest, targetVec.num = targetVec,
                      signature.list = signature.list,
                      signature.name.vec = signature.name.vec,
                      num.boot = num.boot)

Description

Create an array of ROC plots to compare signatures.

Usage

signatureROCplot(
  inputData,
  annotationData,
  signatureColNames,
  annotationColName,
  scale = FALSE,
  choose_colors = c("cornflowerblue", "gray24"),
  name = "Signatures",
  nrow = NULL,
  ncol = NULL
)

Arguments

Value

An array of ROC plots.

Examples

# Run signature profiling
 choose_sigs <- subset(TBsignatures,
                       !(names(TBsignatures) %in% c("Lee_4", "Roe_OD_4")))[c(1,2)]
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "ssGSEA",
                                 signatures = choose_sigs,
                                 parallel.sz = 1)

# Create ROC plots
signatureROCplot(prof_indian, signatureColNames = names(choose_sigs),
                 annotationColName = "label")

Description

Create an array of ROC plots with confidence interval bands to compare signatures.

Usage

signatureROCplot_CI(
  inputData,
  annotationData,
  signatureColNames,
  annotationColName,
  scale = FALSE,
  choose_colors = c("cornflowerblue", "gray50", "gray79"),
  name = NULL,
  nrow = NULL,
  ncol = NULL,
  ci.lev = 0.95,
  pb.show = TRUE
)

Arguments

Value

An array of ROC plots.

Examples

# Run signature profiling

 choose_sigs <- TBsignatures[c(1, 2)]
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "Zscore",
                                 signatures = choose_sigs,
                                 parallel.sz = 1)

# Create ROC plots with confidence intervals
signatureROCplot_CI(prof_indian, signatureColNames = names(choose_sigs),
                    annotationColName = "label")

Description

A function used to subset gene expression value matrix based on certain gene sets.

Usage

subsetGeneSet(
  theObject,
  gene_set,
  useAssay,
  obtainDiagnosis = FALSE,
  annotationColName = NULL
)

Arguments

Value

A matrix with selected gene expression value if obtainDiagnosis == FALSE. If obtainDiagnosis == TRUE, return a list contains the selected gene expression value and diagnosis results for each sample.

Description

Train original model gene signature Suliman_RISK_4.

Usage

SulimanOriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

Train original model for gene signatures Bloom_OD_144 and Zak_RISK_16.

Usage

svm_OriginalModel(dat_list, dat_test_sig)

Arguments

Value

The predicted score for each sample in the test study.

Description

This function collects the results of bootstrapping and t-tests for a scored gene expression dataset and presents them using a JavaScript table with an R interface, or as a data.frame.

Usage

tableAUC(
  SE_scored,
  annotationColName,
  signatureColNames,
  num.boot = 100,
  pb.show = TRUE,
  output = "DataTable"
)

Arguments

Value

A JavaScript table with an R interface using the DT package.

Examples

# Run signature profiling
 choose_sigs <- TBsignatures[c(1, 2)]
 prof_indian <- runTBsigProfiler(TB_indian, useAssay = "logcounts",
                                 algorithm = "ssGSEA",
                                 signatures = choose_sigs,
                                 parallel.sz = 1,
                                 update_genes = FALSE)
 # Create table
 tableAUC(SE_scored = prof_indian, annotationColName = "label",
          signatureColNames = names(choose_sigs))

 # Create data.frame object
h <-  tableAUC(SE_scored = prof_indian, annotationColName = "label",
               signatureColNames = names(choose_sigs),
               output = "data.frame",
               num.boot = 5)
head(h)

Description

An example dataset containing the gene expression and metadata in a SummarizedExperiment object for 31 subjects with HIV and/or Tuberculosis diseases. Information on subject infection status can be accessed with TB_hiv$Disease. Samples with both TB and HIV contamination are marked as tb_hiv, while samples with HIV and no TB are marked as hiv_only.

Usage

TB_hiv

Format

SummarizedExperiment

Details

This dataset was published as part of a study to assess whether gene expression signatures and cytokine levels would distinguish active TB in advanced HIV in a cohort residing in Sub-Saharan Africa (Verma et. al 2018). Participants were severely immunosuppressed TB-HIV patients who had not yet received TB treatment or anti-retroviral therapy (ART). The dataset included in this package has been lightly edited from the originally published dataset due to the removal of one participant who was HIV positive, on ART and developed TB during follow-up. Whole blood RNA-Seq analysis was performed on all 31 participants.

References

Verma S., Du P., et. al. (2018). Tuberculosis in advanced HIV infection is associated with increased expression of IFN and its downstream targets. BMC Infectious Diseases 18:220. doi: https://doi.org/10.1186/s12879-018-3127-410.1186/s12879-018-3127-4.

Examples

data("TB_hiv")

Description

An example dataset containing the gene expression and metadata in a SummarizedExperiment object for an Indian population. Active TB contamination of the 44 subjects is denoted for each as a "1"(active) or "0" (latent/not present), and can be accessed via TB_indian$label. The SummarizedExperiment object contains 2 assays (counts and log(counts)), and the column names give the unique subject identification number along with the subject's gender.

Usage

TB_indian

Format

SummarizedExperiment

Details

This dataset was published as part of a study to assess performance of published TB signatures in a South Indian population (Leong et. al 2018). RNA sequencing was performed on whole blood PAX gene samples collected from 28 TB patients and 16 latent TB infected (LTBI) subjects enrolled as part of an ongoing household contact study. Whole blood RNA-Seq analysis was performed on all 44 participants.

References

Leong S., Zhao Y., et. al. (2018). Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India. Tuberculosis 109, 41-51. doi: https://doi.org/10.1016/j.tube.2018.01.00210.1016/j.tube.2018.01.002.

Examples

data("TB_indian")

Description

A set of Tuberculosis gene signatures from various publications. This set of signatures uses gene symbols. Attempts have been made to use updated gene symbols and remove symbols that did not match the most recent annotation. Additional sets for Entrez IDs and Ensembl IDs are forthcoming.

Usage

TBcommon

Format

list

Details

This list differs from TBsignatures in that signatures with names specified in their originating publication (or that of a peer) are given that common name rather than using the TBSignatureProfiler naming system. Otherwise, signature names are composed of the last name of the primary author, followed by a possible context for the signature, and ending with either the number of gene transcripts or genes in the signature with respect to however it was described in the original publication.

Possible signature contexts:

• <blank>: TB vs LTBI or Healthy Controls

• OD: Other diseases

• HIV: Human Immunodeficiency Virus

• PNA: Pneumonia

• RISK: Risk of developing active TB

• RES: Response to TB treatment

• FAIL: Failure of TB treatment

Note that in some cases signatures will be positive identifiers of TB whereas others are negative identifiers; this should be taken into account when creating ROC curves and computing any AUC estimates.

Source

• Anderson_42: Anderson, Suzanne T., Myrsini Kaforou, Andrew J. Brent, Victoria J. Wright, Claire M. Banwell, George Chagaluka, Amelia C. Crampin, et al. 2014. "Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa." The New England Journal of Medicine 370 (18): 1712-23. https://dx.doi.org/10.1056/NEJMoa130365710.1056/NEJMoa1303657

• Anderson_OD_51: Anderson, Suzanne T., Myrsini Kaforou, Andrew J. Brent, Victoria J. Wright, Claire M. Banwell, George Chagaluka, Amelia C. Crampin, et al. 2014. "Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa." The New England Journal of Medicine 370 (18): 1712-23. https://dx.doi.org/10.1056/NEJMoa130365710.1056/NEJMoa1303657

• Berry_393: Berry, Matthew P. R., Christine M. Graham, Finlay W. McNab, Zhaohui Xu, Susannah A. A. Bloch, Tolu Oni, Katalin A. Wilkinson, et al. 2010. "An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis." Nature 466 (7309): 973-77. https://dx.doi.org/10.1038/nature0924710.1038/nature09247

• Berry_OD_86: Berry, Matthew P. R., Christine M. Graham, Finlay W. McNab, Zhaohui Xu, Susannah A. A. Bloch, Tolu Oni, Katalin A. Wilkinson, et al. 2010. "An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis." Nature 466 (7309): 973-77. https://dx.doi.org/10.1038/nature0924710.1038/nature09247

• Blankley_380: Blankley, Simon, Christine M. Graham, Joe Levin, Jacob Turner, Matthew P. R. Berry, Chloe I. Bloom, Zhaohui Xu, et al. 2016. "A 380-Gene Meta-Signature of Active Tuberculosis Compared with Healthy Controls." The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology 47 (6): 1873-76. https://dx.doi.org/10.1183/13993003.02121-201510.1183/13993003.02121-2015

• Blankley_5: Blankley, Simon, Christine M. Graham, Joe Levin, Jacob Turner, Matthew P. R. Berry, Chloe I. Bloom, Zhaohui Xu, et al. 2016. "A 380-Gene Meta-Signature of Active Tuberculosis Compared with Healthy Controls." The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology 47 (6): 1873-76. https://dx.doi.org/10.1183/13993003.02121-201510.1183/13993003.02121-2015

• Bloom_OD_144: Bloom, Chloe I., Christine M. Graham, Matthew P. R. Berry, Fotini Rozakeas, Paul S. Redford, Yuanyuan Wang, Zhaohui Xu, et al. 2013. "Transcriptional Blood Signatures Distinguish Pulmonary Tuberculosis, Pulmonary Sarcoidosis, Pneumonias and Lung Cancers." PloS One 8 (8): e70630. https://dx.doi.org/10.1371/journal.pone.007063010.1371/journal.pone.0070630

• Bloom_RES_268: Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One. 2012;7(10):e46191. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Bloom_RES_558: Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One. 2012;7(10):e46191. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Chen_5: Chen L, Hua J, He X. Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis. Dis Markers. 2022;2022:2090560. http://dx.doi.org/10.1155/2022/209056010.1155/2022/2090560

• Chen_HIV_4: Chen Y, Wang Q, Lin S, et al. Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV. Front Cell Infect Microbiol. 2021;11:585919. Published 2021 Mar 19. https://dx.doi.org/10.3389/fcimb.2021.58591910.3389/fcimb.2021.585919

• Chendi_HIV_2: Chendi BH, Tveiten H, Snyders CI, et al. CCL1 and IL-2Ra differentiate Tuberculosis disease from latent infection Irrespective of HIV infection in low TB burden countries. J Infect. 2021;S0163-4453(21)00379-0. https://dx.doi.org/10.1016/j.jinf.2021.07.03610.1016/j.jinf.2021.07.036

• RISK11: Darboe, F. et al. Diagnostic performance of an optimized transcriptomic signature of risk of tuberculosis in cryopreserved peripheral blood mononuclear cells. Tuberculosis 108, 124-126 (2018). https://dx.doi.org/ 10.1016/j.tube.2017.11.001 10.1016/j.tube.2017.11.001

• Dawany_HIV_251: Dawany, N. et al. Identification of a 251 gene expression signature that can accurately detect M. tuberculosis in patients with and without HIV co-infection. PLoS One 9, (2014). https://dx.doi.org/10.1371/journal.pone.008992510.1371/journal.pone.0089925

• CMTB_CT: Duffy FJ, Olson GS, Gold ES, Jahn A, Aderem A, Aitchison J, Rothchild AC, Diercks AH, Nemeth J. A contained Mycobacterium tuberculosis mouse infection model predicts active disease and containment in humans. The Journal of Infectious Diseases. 2021 Mar 10. https://dx.doi.org/10.1093/infdis/jiab13010.1093/infdis/jiab130

• Esmail_203: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Esmail_82: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Esmail_OD_893: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Estevez_133: Estévez O, Anibarro L, Garet E, et al. An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile. Front Immunol. 2020;11:1470. Published 2020 Jul 14. https://dx.doi.org/10.3389/fimmu.2020.0147010.3389/fimmu.2020.01470

• Estevez_259: Estévez O, Anibarro L, Garet E, et al. An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile. Front Immunol. 2020;11:1470. Published 2020 Jul 14. https://dx.doi.org/10.3389/fimmu.2020.0147010.3389/fimmu.2020.01470

• Francisco_OD_2: Francisco NM, Fang YM, Ding L, et al. Diagnostic accuracy of a selected signature gene set that discriminates active pulmonary tuberculosis and other pulmonary diseases. J Infect. 2017;75(6):499-510. http://dx.doi.org/10.1016/j.jinf.2017.09.01210.1016/j.jinf.2017.09.012

• Gjoen_10: Gjøen, J.E., Jenum, S., Sivakumaran, D. et al. 'Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children.' Sci Rep 7, 5839 (2017). https://doi.org/10.1038/s41598-017-05057-x10.1038/s41598-017-05057-x

• Gjoen_7: Gjøen, J.E., Jenum, S., Sivakumaran, D. et al. 'Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children.' Sci Rep 7, 5839 (2017). https://doi.org/10.1038/s41598-017-05057-x10.1038/s41598-017-05057-x

• Gliddon_2_OD_4: Gliddon HD, Kaforou M, Alikian M, et al. Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification. Front Immunol. 2021;12:637164. Published 2021 Mar 2. https://dx.doi.org/10.3389/fimmu.2021.63716410.3389/fimmu.2021.637164

• Gliddon_HIV_3: Gliddon HD, Kaforou M, Alikian M, et al. Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification. Front Immunol. 2021;12:637164. Published 2021 Mar 2. https://dx.doi.org/10.3389/fimmu.2021.63716410.3389/fimmu.2021.637164

• Gliddon_OD_3: Gliddon, Harriet D., Kaforou, Myrsini, Alikian, Mary, Habgood-Coote, Dominic, Zhou, Chenxi, Oni, Tolu, Anderson, Suzanne T., Brent, Andrew J., Crampin, Amelia C., Eley, Brian, Kern, Florian, Langford, Paul R., Ottenhoff, Tom H. M., Hibberd, Martin L., French, Neil, Wright, Victoria J., Dockrell, Hazel M., Coin, Lachlan J., Wilkinson, Robert J., Levin, Michael. 2019 "Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification" biorxiv.org: . https://dx.doi.org/10.1101/58367410.1101/583674

• Gliddon_OD_4: Gliddon, Harriet D., Kaforou, Myrsini, Alikian, Mary, Habgood-Coote, Dominic, Zhou, Chenxi, Oni, Tolu, Anderson, Suzanne T., Brent, Andrew J., Crampin, Amelia C., Eley, Brian, Kern, Florian, Langford, Paul R., Ottenhoff, Tom H. M., Hibberd, Martin L., French, Neil, Wright, Victoria J., Dockrell, Hazel M., Coin, Lachlan J., Wilkinson, Robert J., Levin, Michael. 2019 "Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification" biorxiv.org: . https://dx.doi.org/10.1101/58367410.1101/583674

• Gong_OD_4: Gong Z, Gu Y, Xiong K, Niu J, Zheng R, Su B, Fan L and Xie J (2021) The Evaluation and Validation of Blood-Derived Novel Biomarkers for Precise and Rapid Diagnosis of Tuberculosis in Areas With High-TB Burden. Front. Microbiol. 12:650567. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Heycken_FAIL_22: Heyckendorf J, Marwitz S, Reimann M, et al. Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model. Eur Respir J. 2021;2003492. https://dx.doi.org/10.1183/13993003.03492-202010.1183/13993003.03492-2020

• Hoang_OD_13: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Hoang_OD_20: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Hoang_OD_3: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Huang_13: Huang, Hai-Hui et al. 'Identification of 13 Blood-based Gene Expression Signatures to Accurately Distinguish Tuberculosis from Other Pulmonary Diseases and Healthy Controls'. 1 Jan. 2015 : S1837 - S1843.https://doi.org/10.3233/BME-15148610.3233/BME-151486

• Jacobsen_3: Jacobsen, Marc, Dirk Repsilber, Andrea Gutschmidt, Albert Neher, Knut Feldmann, Hans J. Mollenkopf, Andreas Ziegler, and Stefan H. E. Kaufmann. 2007. "Candidate Biomarkers for Discrimination between Infection and Disease Caused by Mycobacterium Tuberculosis." Journal of Molecular Medicine 85 (6): 613-21. https://dx.doi.org/10.1007/s00109-007-0157-610.1007/s00109-007-0157-6

• Jenum_8: Jenum, S., Dhanasekaran, S., Lodha, R. et al. Approaching a diagnostic point-of-care test for pediatric tuberculosis through evaluation of immune biomarkers across the clinical disease spectrum. Sci Rep 6, 18520 (2016). https://doi.org/10.1038/srep1852010.1038/srep18520

• Kaforou_27: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaforou_OD_44: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaforou_OD_53: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaul_3: Kaul S, Nair V, Birla S, et al. Latent Tuberculosis Infection Diagnosis among Household Contacts in a High Tuberculosis-Burden Area: a Comparison between Transcript Signature and Interferon Gamma Release Assay. Microbiol Spectr. 2022;10(2):e0244521. http://dx.doi.org/10.1128/spectrum.02445-2110.1128/spectrum.02445-21

• Kulkarni_HIV_2: Kulkarni V, Queiroz ATL, Sangle S, et al. A Two-Gene Signature for Tuberculosis Diagnosis in Persons With Advanced HIV. Front Immunol. 2021;12:631165. Published 2021 Feb 22. https://dx.doi.org/10.3389/fimmu.2021.63116510.3389/fimmu.2021.631165

• Kwan_186: Kwan PKW, Periaswamy B, De Sessions PF, et al. A blood RNA transcript signature for TB exposure in household contacts. BMC Infect Dis. 2020;20(1):403. http://dx.doi.org/10.1186/s12879-020-05116-110.1186/s12879-020-05116-1

• LauxdaCosta_OD_3: Laux da Costa L, Delcroix M, Dalla Costa ER, et al. A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases. Tuberculosis (Edinb). 2015;95(4):421-425. https://dx.doi.org/10.1016/j.tube.2015.04.00810.1016/j.tube.2015.04.008

• Lee_4: Lee, Shih-Wei, Lawrence Shih-Hsin Wu, Guan-Mau Huang, Kai-Yao Huang, Tzong-Yi Lee, and Julia Tzu-Ya Weng. 2016. "Gene Expression Profiling Identifies Candidate Biomarkers for Active and Latent Tuberculosis." BMC Bioinformatics 17 Suppl 1 (January): 3. https://dx.doi.org/10.1186/s12859-015-0848-x10.1186/s12859-015-0848-x

• Leong_24: Leong, Samantha, Yue Zhao, Noyal M. Joseph, Natasha S. Hochberg, Sonali Sarkar, Jane Pleskunas, David Hom, et al. 2018. "Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India." Tuberculosis (109): 41-51. https://doi.org/10.1016/j.tube.2018.01.00210.1016/j.tube.2018.01.002

• PREDICT29: Leong, S., Zhao, Y., Ribeiro-Rodrigues, R., Jones-López, E. C., Acuña-Villaorduña, C., Rodrigues, P. M., Palaci, M., Alland, D., Dietze, R., Ellner, J. J., Johnson, W. E., Salgame, P., Cross-validation of existing signatures and derivation of a novel 29-gene transcriptomic signature predictive of progression to TB in a Brazilian cohort of household contacts of pulmonary TB. Tuberculosis (Edinb). 2020 Jan;120:101898. https://dx.doi.org/10.1016/j.tube.2020.10189810.1016/j.tube.2020.101898

• Li_3: Li M, Qiu Y, Guo M, Qu R, Tian F, Wang G, Wang Y, Ma J, Liu S, Takiff H, Tang Y, Gao Q.2023.Evaluation of the Cepheid 3-gene host response blood test for tuberculosis diagnosis and treatment response monitoring in a primary-level clinic in rural China. J Clin Microbiol61:e00911-23. http://dx.doi.org/10.1128/jcm.00911-23

• Long_RES_10: Long NP, Phat NK, Yen NTH, et al. A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction. Tuberculosis (Edinb). 2021;131:102138. http://dx.doi.org/10.1016/j.tube.2021.10213810.1016/j.tube.2021.102138

• Maertzdorf_15: Maertzdorf J, McEwen G, Weiner J 3rd, et al. Concise gene signature for point-of-care classification of tuberculosis. EMBO Mol Med. 2016;8(2):86-95. https://dx.doi.org/10.15252/emmm.20150579010.15252/emmm.201505790

• DIAG4: Maertzdorf, Jeroen, Gayle McEwen, January Weiner 3rd, Song Tian, Eric Lader, Ulrich Schriek, Harriet Mayanja-Kizza, Martin Ota, John Kenneth, and Stefan He Kaufmann. 2016. "Concise Gene Signature for Point-of-Care Classification of Tuberculosis." EMBO Molecular Medicine 8 (2): 86-95. https://dx.doi.org/10.15252/emmm.20150579010.15252/emmm.201505790

• Maertzdorf_OD_100: Maertzdorf, Jeroen, January Weiner 3rd, Hans-Joachim Mollenkopf, TBornot TB Network, Torsten Bauer, Antje Prasse, Joachim Müller-Quernheim, and Stefan H. E. Kaufmann. 2012. "Common Patterns and Disease-Related Signatures in Tuberculosis and Sarcoidosis." Proceedings of the National Academy of Sciences of the United States of America 109 (20): 7853-58. https://dx.doi.org/10.1073/pnas.112107210910.1073/pnas.1121072109

• Natarajan_7: Natarajan S, Ranganathan M, Hanna LE, Tripathy S. Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach. Genes (Basel). 2022;13(4):616. Published 2022 Mar 29. http://dx.doi.org/10.3390/genes1304061610.3390/genes13040616

• RISK6: Penn-Nicholson, A. et al. RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response. Sci. Rep. 10, (2020). https://dx.doi.org/10.1038/s41598-020-65043-810.1038/s41598-020-65043-8

• Qian_OD_17: Qian, Zhongqing et al. "Expression of nuclear factor, erythroid 2-like 2-mediated genes differentiates tuberculosis." Tuberculosis (Edinburgh, Scotland) vol. 99 (2016): 56-62. https://doi.org/10.1016/j.tube.2016.04.00810.1016/j.tube.2016.04.008

• Rajan_HIV_5: Rajan, Jayant V., Semitala, Fred C., Kamya, Moses R., Yoon, Christina., Mehta, Tejas., Cattamanchi, Adithya., Seielstad, Mark., Montalvo, Lani., Andama, Alfred., Katende, Jane., Asege, Lucy., Nakaye, Martha., Mwebe, Sandra. 2018 "A Novel, 5-Transcript, Whole-blood Gene-expression Signature for Tuberculosis Screening Among People Living With Human Immunodeficiency Virus" Clinical Infectious Diseases: 1-7. https://doi.org/10.1093/cid/ciy83510.1093/cid/ciy835

• Roe_3: Roe, Jennifer, Venturini, Cristina, Gupta, Rishi K., Gurry, Celine, Chain, Benjamin M., Sun, Yuxin, Southern, Jo, Jackson, Charlotte, Lipman, Marc, C., Miller, Robert F., Martineau, Adrian R., Abubakar, Ibrahim, Noursadeghi, Mahdad. 2019 "T1 Blood transcriptomic stratification of short-term risk in contacts of tuberculosis": . https://doi.org/10.1093/cid/ciz25210.1093/cid/ciz252

• Roe_OD_4: Roe, Jennifer K., Niclas Thomas, Eliza Gil, Katharine Best, Evdokia Tsaliki, Stephen Morris-Jones, Sian Stafford, et al. 2016. "Blood Transcriptomic Diagnosis of Pulmonary and Extrapulmonary Tuberculosis." JCI Insight 1 (16): e87238. https://dx.doi.org/10.1172/jci.insight.8723810.1172/jci.insight.87238

• Sambarey_HIV_10: Sambarey, Awanti, Abhinandan Devaprasad, Abhilash Mohan, Asma Ahmed, Soumya Nayak, Soumya Swaminathan, George D'Souza, et al. 2017. "Unbiased Identification of Blood-Based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks." EBioMedicine 15 (February): 112-26. https://dx.doi.org/10.1016/j.ebiom.2016.12.00910.1016/j.ebiom.2016.12.009

• Singhania_OD_20: Singhania, Akul, Raman Verma, Christine M. Graham, Jo Lee, Trang Tran, Matthew Richardson, Patrick Lecine, et al. 2018. "A Modular Transcriptional Signature Identifies Phenotypic Heterogeneity of Human Tuberculosis Infection." Nature Communications 9 (1): 2308. https://dx.doi.org/10.1038/s41467-018-04579-w10.1038/s41467-018-04579-w

• Sivakumaran_11: Sivakumaran D, Ritz C, Gjøen JE, et al. Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults. Front Immunol. 2021;11:626049. Published 2021 Feb 4. https://dx.doi.org/10.3389/fimmu.2020.62604910.3389/fimmu.2020.626049

• Sloot_HIV_2: Sloot, Rosa, Maarten F. Schim van der Loeff, Erik W. van Zwet, Mariëlle C. Haks, Sytze T. Keizer, Maarten Scholing, Tom H. M. Ottenhoff, Martien W. Borgdorff, and Simone A. Joosten. 2015. "Biomarkers Can Identify Pulmonary Tuberculosis in HIV-Infected Drug Users Months Prior to Clinical Diagnosis." EBioMedicine 2 (2): 172-79. https://dx.doi.org/10.1016/j.ebiom.2014.12.00110.1016/j.ebiom.2014.12.001

• Suliman_4: Suliman, Sara, Ethan Thompson, Jayne Sutherland, January Weiner Rd, Martin O. C. Ota, Smitha Shankar, Adam Penn-Nicholson, et al. 2018. "Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis." American Journal of Respiratory and Critical Care Medicine, April. https://doi.org/10.1164/rccm.201711-2340OC. https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• Suliman_RISK_2: Suliman, S. et al. Four-gene pan-African blood signature predicts progression to tuberculosis. Am. J. Respir. Crit. Care Med. 197, 1198-1208 (2018). https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• RISK4: Suliman, Sara, Ethan Thompson, Jayne Sutherland, January Weiner Rd, Martin O. C. Ota, Smitha Shankar, Adam Penn-Nicholson, et al. 2018. "Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis." American Journal of Respiratory and Critical Care Medicine, April. https://doi.org/10.1164/rccm.201711-2340OC. https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• DIAG3: Sweeney, Timothy E., Lindsay Braviak, Cristina M. Tato, and Purvesh Khatri. 2016. "Genome-Wide Expression for Diagnosis of Pulmonary Tuberculosis: A Multicohort Analysis." The Lancet. Respiratory Medicine 4 (3): 213-24. https://dx.doi.org/10.1016/S2213-2600(16)00048-510.1016/S2213-2600(16)00048-5

• TB12: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• EarlyRESP-TB25: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• TREAT-TB27: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• DISEASE: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• FAILURE: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• RESPONSE5: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• Tornheim_71: Tornheim, Jeffrey A., Anil K. Madugundu, Mandar Paradkar, Kiyoshi F. Fukutani, Artur TL Queiroz, Nikhil Gupte, Akshay N. Gupte et al. 2020. "Transcriptomic Profiles of Confirmed Pediatric Tuberculosis Patients and Household Contacts Identifies Active Tuberculosis, Infection, and Treatment Response Among Indian Children." The Journal of Infectious Diseases 221(10): 1647-1658. https://doi.org/10.1093/infdis/jiz63910.1093/infdis/jiz639

• Tornheim_RES_25: Tornheim, Jeffrey A., Anil K. Madugundu, Mandar Paradkar, Kiyoshi F. Fukutani, Artur TL Queiroz, Nikhil Gupte, Akshay N. Gupte et al. 2020. "Transcriptomic Profiles of Confirmed Pediatric Tuberculosis Patients and Household Contacts Identifies Active Tuberculosis, Infection, and Treatment Response Among Indian Children." The Journal of Infectious Diseases 221(10): 1647-1658. https://doi.org/10.1093/infdis/jiz63910.1093/infdis/jiz639

• Vargas_18: Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol. 2023;19(7):e1010770. http://dx.doi.org/10.1371/journal.pcbi.101077010.1371/journal.pcbi.1010770

• Vargas_42: Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol. 2023;19(7):e1010770. http://dx.doi.org/10.1371/journal.pcbi.101077010.1371/journal.pcbi.1010770

• Verhagen_10: Verhagen, L.M., Zomer, A., Maes, M. et al. A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children. BMC Genomics 14, 74 (2013). https://doi.org/10.1186/1471-2164-14-7410.1186/1471-2164-14-74

• Walter_51: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• Walter_PNA_119: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• Walter_PNA_47: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• ACS_COR: Zak, Daniel E., Adam Penn-Nicholson, Thomas J. Scriba, Ethan Thompson, Sara Suliman, Lynn M. Amon, Hassan Mahomed, et al. 2016. "A Blood RNA Signature for Tuberculosis Disease Risk: A Prospective Cohort Study." The Lancet 387 (10035): 231222. https://dx.doi.org/10.1016/S0140-6736(15)01316-110.1016/S0140-6736(15)01316-1

• Zhao_NANO_6: Kaipilyawar, Vaishnavi, et al. Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform. Clinical Infectious Diseases 75.6 (2022): 1022-1030. http://dx.doi.org/10.1093/cid/ciac010

Examples

data("TBcommon")

Description

A set of Tuberculosis gene signatures compiled from various publications. This set of signatures uses gene symbols. Attempts have been made to use updated gene symbols and remove symbols that did not match the most recent annotation. Additional sets for Entrez IDs and Ensembl IDs are forthcoming.

Usage

TBsignatures

Format

list

Details

Signature names are composed of the last name of the primary author, followed by a possible context for the signature, and ending with either the number of gene transcripts or genes in the signature, with respect to however it was described in the signature's original publication.

Possible signature contexts:

• <blank>: TB vs LTBI or Healthy Controls

• OD: Other diseases

• HIV: Human Immunodeficiency Virus

• PNA: Pneumonia

• RISK: Risk of developing active TB

• RES: Response to TB treatment

• FAIL: Failure of TB treatment

Note that in some cases signatures will be positive identifiers of TB whereas others are negative identifiers; this should be taken into account when creating ROC curves and computing any AUC estimates.

Source

• Anderson_42: Anderson, Suzanne T., Myrsini Kaforou, Andrew J. Brent, Victoria J. Wright, Claire M. Banwell, George Chagaluka, Amelia C. Crampin, et al. 2014. "Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa." The New England Journal of Medicine 370 (18): 1712-23. https://dx.doi.org/10.1056/NEJMoa130365710.1056/NEJMoa1303657

• Anderson_OD_51: Anderson, Suzanne T., Myrsini Kaforou, Andrew J. Brent, Victoria J. Wright, Claire M. Banwell, George Chagaluka, Amelia C. Crampin, et al. 2014. "Diagnosis of Childhood Tuberculosis and Host RNA Expression in Africa." The New England Journal of Medicine 370 (18): 1712-23. https://dx.doi.org/10.1056/NEJMoa130365710.1056/NEJMoa1303657

• Berry_393: Berry, Matthew P. R., Christine M. Graham, Finlay W. McNab, Zhaohui Xu, Susannah A. A. Bloch, Tolu Oni, Katalin A. Wilkinson, et al. 2010. "An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis." Nature 466 (7309): 973-77. https://dx.doi.org/10.1038/nature0924710.1038/nature09247

• Berry_OD_86: Berry, Matthew P. R., Christine M. Graham, Finlay W. McNab, Zhaohui Xu, Susannah A. A. Bloch, Tolu Oni, Katalin A. Wilkinson, et al. 2010. "An Interferon-Inducible Neutrophil-Driven Blood Transcriptional Signature in Human Tuberculosis." Nature 466 (7309): 973-77. https://dx.doi.org/10.1038/nature0924710.1038/nature09247

• Blankley_380: Blankley, Simon, Christine M. Graham, Joe Levin, Jacob Turner, Matthew P. R. Berry, Chloe I. Bloom, Zhaohui Xu, et al. 2016. "A 380-Gene Meta-Signature of Active Tuberculosis Compared with Healthy Controls." The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology 47 (6): 1873-76. https://dx.doi.org/10.1183/13993003.02121-201510.1183/13993003.02121-2015

• Blankley_5: Blankley, Simon, Christine M. Graham, Joe Levin, Jacob Turner, Matthew P. R. Berry, Chloe I. Bloom, Zhaohui Xu, et al. 2016. "A 380-Gene Meta-Signature of Active Tuberculosis Compared with Healthy Controls." The European Respiratory Journal: Official Journal of the European Society for Clinical Respiratory Physiology 47 (6): 1873-76. https://dx.doi.org/10.1183/13993003.02121-201510.1183/13993003.02121-2015

• Bloom_OD_144: Bloom, Chloe I., Christine M. Graham, Matthew P. R. Berry, Fotini Rozakeas, Paul S. Redford, Yuanyuan Wang, Zhaohui Xu, et al. 2013. "Transcriptional Blood Signatures Distinguish Pulmonary Tuberculosis, Pulmonary Sarcoidosis, Pneumonias and Lung Cancers." PloS One 8 (8): e70630. https://dx.doi.org/10.1371/journal.pone.007063010.1371/journal.pone.0070630

• Bloom_RES_268: Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One. 2012;7(10):e46191. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Bloom_RES_558: Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PLoS One. 2012;7(10):e46191. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Chen_5: Chen L, Hua J, He X. Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis. Dis Markers. 2022;2022:2090560. http://dx.doi.org/10.1155/2022/209056010.1155/2022/2090560

• Chen_HIV_4: Chen Y, Wang Q, Lin S, et al. Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV. Front Cell Infect Microbiol. 2021;11:585919. Published 2021 Mar 19. https://dx.doi.org/10.3389/fcimb.2021.58591910.3389/fcimb.2021.585919

• Chendi_HIV_2: Chendi BH, Tveiten H, Snyders CI, et al. CCL1 and IL-2Ra differentiate Tuberculosis disease from latent infection Irrespective of HIV infection in low TB burden countries. J Infect. 2021;S0163-4453(21)00379-0. https://dx.doi.org/10.1016/j.jinf.2021.07.03610.1016/j.jinf.2021.07.036

• Darboe_RISK_11: Darboe, F. et al. Diagnostic performance of an optimized transcriptomic signature of risk of tuberculosis in cryopreserved peripheral blood mononuclear cells. Tuberculosis 108, 124-126 (2018). https://dx.doi.org/ 10.1016/j.tube.2017.11.001 10.1016/j.tube.2017.11.001

• Dawany_HIV_251: Dawany, N. et al. Identification of a 251 gene expression signature that can accurately detect M. tuberculosis in patients with and without HIV co-infection. PLoS One 9, (2014). https://dx.doi.org/10.1371/journal.pone.008992510.1371/journal.pone.0089925

• Duffy_23: Duffy FJ, Olson GS, Gold ES, Jahn A, Aderem A, Aitchison J, Rothchild AC, Diercks AH, Nemeth J. A contained Mycobacterium tuberculosis mouse infection model predicts active disease and containment in humans. The Journal of Infectious Diseases. 2021 Mar 10. https://dx.doi.org/10.1093/infdis/jiab13010.1093/infdis/jiab130

• Esmail_203: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Esmail_82: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Esmail_893: Esmail, Hanif, Rachel P. Lai, Maia Lesosky, Katalin A. Wilkinson, Christine M. Graham, Stuart Horswell, Anna K. Coussens, Clifton E. Barry 3rd, Anne O'Garra, and Robert J. Wilkinson. 2018. "Complement Pathway Gene Activation and Rising Circulating Immune Complexes Characterize Early Disease in HIV-Associated Tuberculosis." Proceedings of the National Academy of Sciences of the United States of America 115 (5): E964-73. https://dx.doi.org/10.1073/pnas.171185311510.1073/pnas.1711853115

• Estevez_133: Estévez O, Anibarro L, Garet E, et al. An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile. Front Immunol. 2020;11:1470. Published 2020 Jul 14. https://dx.doi.org/10.3389/fimmu.2020.0147010.3389/fimmu.2020.01470

• Estevez_259: Estévez O, Anibarro L, Garet E, et al. An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile. Front Immunol. 2020;11:1470. Published 2020 Jul 14. https://dx.doi.org/10.3389/fimmu.2020.0147010.3389/fimmu.2020.01470

• Francisco_OD_2: Francisco NM, Fang YM, Ding L, et al. Diagnostic accuracy of a selected signature gene set that discriminates active pulmonary tuberculosis and other pulmonary diseases. J Infect. 2017;75(6):499-510. http://dx.doi.org/10.1016/j.jinf.2017.09.01210.1016/j.jinf.2017.09.012

• Gjoen_10: Gjøen, J.E., Jenum, S., Sivakumaran, D. et al. 'Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children.' Sci Rep 7, 5839 (2017). https://doi.org/10.1038/s41598-017-05057-x10.1038/s41598-017-05057-x

• Gjoen_7: Gjøen, J.E., Jenum, S., Sivakumaran, D. et al. 'Novel transcriptional signatures for sputum-independent diagnostics of tuberculosis in children.' Sci Rep 7, 5839 (2017). https://doi.org/10.1038/s41598-017-05057-x10.1038/s41598-017-05057-x

• Gliddon_2_OD_4: Gliddon HD, Kaforou M, Alikian M, et al. Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification. Front Immunol. 2021;12:637164. Published 2021 Mar 2. https://dx.doi.org/10.3389/fimmu.2021.63716410.3389/fimmu.2021.637164

• Gliddon_HIV_3: Gliddon HD, Kaforou M, Alikian M, et al. Identification of Reduced Host Transcriptomic Signatures for Tuberculosis Disease and Digital PCR-Based Validation and Quantification. Front Immunol. 2021;12:637164. Published 2021 Mar 2. https://dx.doi.org/10.3389/fimmu.2021.63716410.3389/fimmu.2021.637164

• Gliddon_OD_3: Gliddon, Harriet D., Kaforou, Myrsini, Alikian, Mary, Habgood-Coote, Dominic, Zhou, Chenxi, Oni, Tolu, Anderson, Suzanne T., Brent, Andrew J., Crampin, Amelia C., Eley, Brian, Kern, Florian, Langford, Paul R., Ottenhoff, Tom H. M., Hibberd, Martin L., French, Neil, Wright, Victoria J., Dockrell, Hazel M., Coin, Lachlan J., Wilkinson, Robert J., Levin, Michael. 2019 "Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification" biorxiv.org:https://dx.doi.org/10.1101/58367410.1101/583674

• Gliddon_OD_4: Gliddon, Harriet D., Kaforou, Myrsini, Alikian, Mary, Habgood-Coote, Dominic, Zhou, Chenxi, Oni, Tolu, Anderson, Suzanne T., Brent, Andrew J., Crampin, Amelia C., Eley, Brian, Kern, Florian, Langford, Paul R., Ottenhoff, Tom H. M., Hibberd, Martin L., French, Neil, Wright, Victoria J., Dockrell, Hazel M., Coin, Lachlan J., Wilkinson, Robert J., Levin, Michael. 2019 "Identification of reduced host transcriptomic signatures for tuberculosis and digital PCR-based validation and quantification" biorxiv.org:https://dx.doi.org/10.1101/58367410.1101/583674

• Gong_OD_4: Gong Z, Gu Y, Xiong K, Niu J, Zheng R, Su B, Fan L and Xie J (2021) The Evaluation and Validation of Blood-Derived Novel Biomarkers for Precise and Rapid Diagnosis of Tuberculosis in Areas With High-TB Burden. Front. Microbiol. 12:650567. https://dx.doi.org/10.3389/fmicb.2021.65056710.3389/fmicb.2021.650567

• Heycken_FAIL_22: Heyckendorf J, Marwitz S, Reimann M, et al. Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model. Eur Respir J. 2021;2003492. https://dx.doi.org/10.1183/13993003.03492-202010.1183/13993003.03492-2020

• Hoang_OD_13: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Hoang_OD_20: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Hoang_OD_3: Hoang, Long & Jain, Pooja & Pillay, Timesh & Tolosa-Wright, Mica & Niazi, Umar & Takwoingi, Yemisi & Halliday, Alice & Berrocal-Almanza, Luis & Deeks, Jonathan & Beverley, Peter & Kon, Onn & Lalvani, Ajit. (2021). Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study. The Lancet Infectious Diseases. https://dx.doi.org/10.1016/S1473-3099(20)30928-210.1016/S1473-3099(20)30928-2

• Huang_13: Huang, Hai-Hui et al. 'Identification of 13 Blood-based Gene Expression Signatures to Accurately Distinguish Tuberculosis from Other Pulmonary Diseases and Healthy Controls'. 1 Jan. 2015 : S1837 - S1843.https://doi.org/10.3233/BME-15148610.3233/BME-151486

• Jacobsen_3: Jacobsen, Marc, Dirk Repsilber, Andrea Gutschmidt, Albert Neher, Knut Feldmann, Hans J. Mollenkopf, Andreas Ziegler, and Stefan H. E. Kaufmann. 2007. "Candidate Biomarkers for Discrimination between Infection and Disease Caused by Mycobacterium Tuberculosis." Journal of Molecular Medicine 85 (6): 613-21. https://dx.doi.org/10.1007/s00109-007-0157-610.1007/s00109-007-0157-6

• Jenum_8: Jenum, S., Dhanasekaran, S., Lodha, R. et al. Approaching a diagnostic point-of-care test for pediatric tuberculosis through evaluation of immune biomarkers across the clinical disease spectrum. Sci Rep 6, 18520 (2016). https://doi.org/10.1038/srep1852010.1038/srep18520

• Kaforou_27: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaforou_OD_44: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaforou_OD_53: Kaforou, Myrsini, Victoria J. Wright, Tolu Oni, Neil French, Suzanne T. Anderson, Nonzwakazi Bangani, Claire M. Banwell, et al. 2013. "Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study." PLoS Medicine 10 (10): e1001538. https://dx.doi.org/10.1371/journal.pmed.100153810.1371/journal.pmed.1001538

• Kaul_3: Kaul S, Nair V, Birla S, et al. Latent Tuberculosis Infection Diagnosis among Household Contacts in a High Tuberculosis-Burden Area: a Comparison between Transcript Signature and Interferon Gamma Release Assay. Microbiol Spectr. 2022;10(2):e0244521. http://dx.doi.org/10.1128/spectrum.02445-2110.1128/spectrum.02445-21

• Kulkarni_HIV_2: Kulkarni V, Queiroz ATL, Sangle S, et al. A Two-Gene Signature for Tuberculosis Diagnosis in Persons With Advanced HIV. Front Immunol. 2021;12:631165. Published 2021 Feb 22. https://dx.doi.org/10.3389/fimmu.2021.63116510.3389/fimmu.2021.631165

• Kwan_186: Kwan PKW, Periaswamy B, De Sessions PF, et al. A blood RNA transcript signature for TB exposure in household contacts. BMC Infect Dis. 2020;20(1):403. http://dx.doi.org/10.1186/s12879-020-05116-110.1186/s12879-020-05116-1

• LauxdaCosta_OD_3: Laux da Costa L, Delcroix M, Dalla Costa ER, et al. A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases. Tuberculosis (Edinb). 2015;95(4):421-425. https://dx.doi.org/10.1016/j.tube.2015.04.00810.1016/j.tube.2015.04.008

• Lee_4: Lee, Shih-Wei, Lawrence Shih-Hsin Wu, Guan-Mau Huang, Kai-Yao Huang, Tzong-Yi Lee, and Julia Tzu-Ya Weng. 2016. "Gene Expression Profiling Identifies Candidate Biomarkers for Active and Latent Tuberculosis." BMC Bioinformatics 17 Suppl 1 (January): 3. https://dx.doi.org/10.1186/s12859-015-0848-x10.1186/s12859-015-0848-x

• Leong_24: Leong, Samantha, Yue Zhao, Noyal M. Joseph, Natasha S. Hochberg, Sonali Sarkar, Jane Pleskunas, David Hom, et al. 2018. "Existing blood transcriptional classifiers accurately discriminate active tuberculosis from latent infection in individuals from south India." Tuberculosis (109): 41-51. https://doi.org/10.1016/j.tube.2018.01.00210.1016/j.tube.2018.01.002

• Leong_RISK_29: Leong, S., Zhao, Y., Ribeiro-Rodrigues, R., Jones-López, E. C., Acuña-Villaorduña, C., Rodrigues, P. M., Palaci, M., Alland, D., Dietze, R., Ellner, J. J., Johnson, W. E., Salgame, P., Cross-validation of existing signatures and derivation of a novel 29-gene transcriptomic signature predictive of progression to TB in a Brazilian cohort of household contacts of pulmonary TB. Tuberculosis (Edinb). 2020 Jan;120:101898. https://dx.doi.org/10.1016/j.tube.2020.10189810.1016/j.tube.2020.101898

• Li_3: Li M, Qiu Y, Guo M, Qu R, Tian F, Wang G, Wang Y, Ma J, Liu S, Takiff H, Tang Y, Gao Q.2023.Evaluation of the Cepheid 3-gene host response blood test for tuberculosis diagnosis and treatment response monitoring in a primary-level clinic in rural China. J Clin Microbiol61:e00911-23. http://dx.doi.org/10.1128/jcm.00911-23

• Long_RES_10: Long NP, Phat NK, Yen NTH, et al. A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction. Tuberculosis (Edinb). 2021;131:102138. http://dx.doi.org/10.1016/j.tube.2021.10213810.1016/j.tube.2021.102138

• Maertzdorf_15: Maertzdorf J, McEwen G, Weiner J 3rd, et al. Concise gene signature for point-of-care classification of tuberculosis. EMBO Mol Med. 2016;8(2):86-95. https://dx.doi.org/10.15252/emmm.20150579010.15252/emmm.201505790

• Maertzdorf_4: Maertzdorf, Jeroen, Gayle McEwen, January Weiner 3rd, Song Tian, Eric Lader, Ulrich Schriek, Harriet Mayanja-Kizza, Martin Ota, John Kenneth, and Stefan He Kaufmann. 2016. "Concise Gene Signature for Point-of-Care Classification of Tuberculosis." EMBO Molecular Medicine 8 (2): 86-95. https://dx.doi.org/10.15252/emmm.20150579010.15252/emmm.201505790

• Maertzdorf_OD_100: Maertzdorf, Jeroen, January Weiner 3rd, Hans-Joachim Mollenkopf, TBornot TB Network, Torsten Bauer, Antje Prasse, Joachim Müller-Quernheim, and Stefan H. E. Kaufmann. 2012. "Common Patterns and Disease-Related Signatures in Tuberculosis and Sarcoidosis." Proceedings of the National Academy of Sciences of the United States of America 109 (20): 7853-58. https://dx.doi.org/10.1073/pnas.112107210910.1073/pnas.1121072109

• Natarajan_7: Natarajan S, Ranganathan M, Hanna LE, Tripathy S. Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach. Genes (Basel). 2022;13(4):616. Published 2022 Mar 29. http://dx.doi.org/10.3390/genes1304061610.3390/genes13040616

• PennNich_RISK_6: Penn-Nicholson, A. et al. RISK6, a 6-gene transcriptomic signature of TB disease risk, diagnosis and treatment response. Sci. Rep. 10, (2020). https://dx.doi.org/10.1038/s41598-020-65043-810.1038/s41598-020-65043-8

• Qian_OD_17: Qian, Zhongqing et al. "Expression of nuclear factor, erythroid 2-like 2-mediated genes differentiates tuberculosis." Tuberculosis (Edinburgh, Scotland) vol. 99 (2016): 56-62. https://doi.org/10.1016/j.tube.2016.04.00810.1016/j.tube.2016.04.008

• Rajan_HIV_5: Rajan, Jayant V., Semitala, Fred C., Kamya, Moses R., Yoon, Christina., Mehta, Tejas., Cattamanchi, Adithya., Seielstad, Mark., Montalvo, Lani., Andama, Alfred., Katende, Jane., Asege, Lucy., Nakaye, Martha., Mwebe, Sandra. 2018 "A Novel, 5-Transcript, Whole-blood Gene-expression Signature for Tuberculosis Screening Among People Living With Human Immunodeficiency Virus" Clinical Infectious Diseases: 1-7. https://doi.org/10.1093/cid/ciy83510.1093/cid/ciy835

• Roe_3: Roe, Jennifer, Venturini, Cristina, Gupta, Rishi K., Gurry, Celine, Chain, Benjamin M., Sun, Yuxin, Southern, Jo, Jackson, Charlotte, Lipman, Marc, C., Miller, Robert F., Martineau, Adrian R., Abubakar, Ibrahim, Noursadeghi, Mahdad. 2019 "T1 Blood transcriptomic stratification of short-term risk in contacts of tuberculosis": . https://doi.org/10.1093/cid/ciz25210.1093/cid/ciz252

• Roe_OD_4: Roe, Jennifer K., Niclas Thomas, Eliza Gil, Katharine Best, Evdokia Tsaliki, Stephen Morris-Jones, Sian Stafford, et al. 2016. "Blood Transcriptomic Diagnosis of Pulmonary and Extrapulmonary Tuberculosis." JCI Insight 1 (16): e87238. https://dx.doi.org/10.1172/jci.insight.8723810.1172/jci.insight.87238

• Sambarey_HIV_10: Sambarey, Awanti, Abhinandan Devaprasad, Abhilash Mohan, Asma Ahmed, Soumya Nayak, Soumya Swaminathan, George D'Souza, et al. 2017. "Unbiased Identification of Blood-Based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks." EBioMedicine 15 (February): 112-26. https://dx.doi.org/10.1016/j.ebiom.2016.12.00910.1016/j.ebiom.2016.12.009

• Singhania_OD_20: Singhania, Akul, Raman Verma, Christine M. Graham, Jo Lee, Trang Tran, Matthew Richardson, Patrick Lecine, et al. 2018. "A Modular Transcriptional Signature Identifies Phenotypic Heterogeneity of Human Tuberculosis Infection." Nature Communications 9 (1): 2308. https://dx.doi.org/10.1038/s41467-018-04579-w10.1038/s41467-018-04579-w

• Sivakumaran_11: Sivakumaran D, Ritz C, Gjøen JE, et al. Host Blood RNA Transcript and Protein Signatures for Sputum-Independent Diagnostics of Tuberculosis in Adults. Front Immunol. 2021;11:626049. Published 2021 Feb 4. https://dx.doi.org/10.3389/fimmu.2020.62604910.3389/fimmu.2020.626049

• Sloot_HIV_2: Sloot, Rosa, Maarten F. Schim van der Loeff, Erik W. van Zwet, Mariëlle C. Haks, Sytze T. Keizer, Maarten Scholing, Tom H. M. Ottenhoff, Martien W. Borgdorff, and Simone A. Joosten. 2015. "Biomarkers Can Identify Pulmonary Tuberculosis in HIV-Infected Drug Users Months Prior to Clinical Diagnosis." EBioMedicine 2 (2): 172-79. https://dx.doi.org/10.1016/j.ebiom.2014.12.00110.1016/j.ebiom.2014.12.001

• Suliman_4: Suliman, Sara, Ethan Thompson, Jayne Sutherland, January Weiner Rd, Martin O. C. Ota, Smitha Shankar, Adam Penn-Nicholson, et al. 2018. "Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis." American Journal of Respiratory and Critical Care Medicine, April. https://doi.org/10.1164/rccm.201711-2340OC. https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• Suliman_RISK_2: Suliman, S. et al. Four-gene pan-African blood signature predicts progression to tuberculosis. Am. J. Respir. Crit. Care Med. 197, 1198-1208 (2018). https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• Suliman_RISK_4: Suliman, Sara, Ethan Thompson, Jayne Sutherland, January Weiner Rd, Martin O. C. Ota, Smitha Shankar, Adam Penn-Nicholson, et al. 2018. "Four-Gene Pan-African Blood Signature Predicts Progression to Tuberculosis." American Journal of Respiratory and Critical Care Medicine, April. https://doi.org/10.1164/rccm.201711-2340OC. https://dx.doi.org/10.1164/rccm.201711-2340OC10.1164/rccm.201711-2340OC

• Sweeney_OD_3: Sweeney, Timothy E., Lindsay Braviak, Cristina M. Tato, and Purvesh Khatri. 2016. "Genome-Wide Expression for Diagnosis of Pulmonary Tuberculosis: A Multicohort Analysis." The Lancet. Respiratory Medicine 4 (3): 213-24. https://dx.doi.org/10.1016/S2213-2600(16)00048-510.1016/S2213-2600(16)00048-5

• Tabone_OD_11: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• Tabone_RES_25: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• Tabone_RES_27: Tabone O, Verma R, Singhania A, et al. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med. 2021;218(10):e20210915. http://dx.doi.org/10.1084/jem.2021091510.1084/jem.20210915

• Thompson_9: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• Thompson_FAIL_13: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• Thompson_RES_5: Thompson, Ethan G., Ying Du, Stephanus T. Malherbe, Smitha Shankar, Jackie Braun, Joe Valvo, Katharina Ronacher, et al. 2017. "Host Blood RNA Signatures Predict the Outcome of Tuberculosis Treatment." Tuberculosis 107 (December): 48-58. https://dx.doi.org/10.1016/j.tube.2017.08.00410.1016/j.tube.2017.08.004

• Tornheim_71: Tornheim, Jeffrey A., Anil K. Madugundu, Mandar Paradkar, Kiyoshi F. Fukutani, Artur TL Queiroz, Nikhil Gupte, Akshay N. Gupte et al. 2020. "Transcriptomic Profiles of Confirmed Pediatric Tuberculosis Patients and Household Contacts Identifies Active Tuberculosis, Infection, and Treatment Response Among Indian Children." The Journal of Infectious Diseases 221(10): 1647-1658. https://doi.org/10.1093/infdis/jiz63910.1093/infdis/jiz639

• Tornheim_RES_25: Tornheim, Jeffrey A., Anil K. Madugundu, Mandar Paradkar, Kiyoshi F. Fukutani, Artur TL Queiroz, Nikhil Gupte, Akshay N. Gupte et al. 2020. "Transcriptomic Profiles of Confirmed Pediatric Tuberculosis Patients and Household Contacts Identifies Active Tuberculosis, Infection, and Treatment Response Among Indian Children." The Journal of Infectious Diseases 221(10): 1647-1658. https://doi.org/10.1093/infdis/jiz63910.1093/infdis/jiz639

• Vargas_18: Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol. 2023;19(7):e1010770. http://dx.doi.org/10.1371/journal.pcbi.101077010.1371/journal.pcbi.1010770

• Vargas_42: Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol. 2023;19(7):e1010770. http://dx.doi.org/10.1371/journal.pcbi.101077010.1371/journal.pcbi.1010770

• Verhagen_10: Verhagen, L.M., Zomer, A., Maes, M. et al. A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children. BMC Genomics 14, 74 (2013). https://doi.org/10.1186/1471-2164-14-7410.1186/1471-2164-14-74

• Walter_51: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• Walter_PNA_119: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• Walter_PNA_47: Walter, Nicholas D., Mikaela A. Miller, Joshua Vasquez, Marc Weiner, Adam Chapman, Melissa Engle, Michael Higgins, et al. 2016. "Blood Transcriptional Biomarkers for Active Tuberculosis among Patients in the United States: A Case-Control Study with Systematic Cross-Classifier Evaluation." Journal of Clinical Microbiology 54 (2): 274-82. https://dx.doi.org/10.1128/JCM.01990-1510.1128/JCM.01990-15

• Zak_RISK_16: Zak, Daniel E., Adam Penn-Nicholson, Thomas J. Scriba, Ethan Thompson, Sara Suliman, Lynn M. Amon, Hassan Mahomed, et al. 2016. "A Blood RNA Signature for Tuberculosis Disease Risk: A Prospective Cohort Study." The Lancet 387 (10035): 231222. https://dx.doi.org/10.1016/S0140-6736(15)01316-110.1016/S0140-6736(15)01316-1

• Zhao_NANO_6: Kaipilyawar, Vaishnavi, et al. Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform. Clinical Infectious Diseases 75.6 (2022): 1022-1030. http://dx.doi.org/10.1093/cid/ciac010

Examples

data("TBsignatures")

Description

Up/Down-regulated genes information for selected TB signatures.

Usage

TBsignaturesSplit

Format

list

Source

See ?TBsignatures for reference information.

Examples

data("TBsignaturesSplit")

Description

Use this function to run the TBSignatureProfiler application.

Usage

TBSPapp()

Value

The Shiny application will open.

Examples

# Upload data through the app
if (interactive()) {
  TBSPapp()
}