Package 'EMTscore'

Description

This function calculates epithelial-mesenchymal transition (EMT) scores for one or more single-cell objects using multiple scoring methods. Supported methods include "Seurat", "nnPCA", "AUCell", "GSVA", "ssGSEA", "SCSE", and "JASMINE".

Usage

add_EMT_score(
  objects,
  gmt_file,
  emt_name = "EMT_Score",
  method = c("Seurat", "nnPCA", "AUCell", "GSVA", "ssGSEA", "SCSE", "JASMINE"),
  nnPCA_dim = 1
)

Arguments

Value

A named list of Seurat objects with a new metadata column containing EMT scores.

Examples

library(ExperimentHub)
eh <- ExperimentHub()
A549_TGFB1 <- eh[["EH10293"]]
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
objects <- list(A549_TGFB1 = A549_TGFB1)
res <- add_EMT_score(objects, gmt_file,
  emt_name = "EMT_Score",
  method = "AUCell"
)

Description

Calculate EMT (epithelial-mesenchymal transition) scores for bulk RNA-seq expression data. The function accepts a single expression matrix (rows = genes, columns = samples) or a named list of such matrices, and computes EMT scores for each sample using one of the supported scoring methods.

Usage

add_EMT_score_Bulk(
  expr_mat_list,
  gmt_file = NULL,
  emt_name,
  method = c("nnPCA", "AUCell", "ssGSEA", "SCSE", "GSVA", "JASMINE"),
  dimension
)

Arguments

Details

Supported methods:

• "nnPCA" - non-negative sparse PCA (via nsprcomp)

• "AUCell" - AUCell AUC scoring

• "ssGSEA" - single-sample GSEA

• "GSVA"

• "SCSE" - Single-Cell Signature Explorer (adapted)

• "JASMINE" - JASMINE scoring

- The function expects gene names as rownames(mat) and sample names as colnames(mat). It will coerce inputs to numeric matrices internally. - If none of the GMT genes are present in a matrix a warning is issued and that matrix returns NULL in the result list. - The scoring helpers (Execute_nnPCA, Execute_AUCell, Execute_ssGSEA, Execute_SCSE, Execute_JAS) are assumed available in the package namespace (or the environment) and must return a data.frame with sample rows and the score column name given in score_names.

Value

If a single matrix is provided, a data.frame with rownames = sample names and one column named after emt_name containing EMT scores. If a list of matrices is provided, a named list of such data.frames (one per input matrix) is returned.

Examples

# single matrix
data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
res <- add_EMT_score_Bulk(
  expr_mat_list = list(s = geneExp), gmt_file,
  emt_name = "EMT", method = "AUCell", dimension = 1
)

# multiple matrices (named list)
exprs <- list(TCGA = geneExp, GTEX = geneExp)
res_list <- add_EMT_score_Bulk(
  expr_mat_list = exprs, gmt_file,
  emt_name = "EMT", method = "nnPCA", dimension = 1
)

Description

Computes EMT-related scores for multiple gene sets defined in a GMT file.

Usage

add_EMT_score_multiple(
  objects,
  gmt_file,
  emt_names = NULL,
  method = c("Seurat", "nnPCA", "AUCell", "ssGSEA", "GSVA", "SCSE", "JASMINE"),
  nnPCA_dim = 1,
  cores = 1
)

Arguments

Value

A named list of Seurat objects with multiple EMT score columns added.

Examples

library(ExperimentHub)
eh <- ExperimentHub()
A549_TNF <- eh[["EH10291"]]
A549_EGF <- eh[["EH10292"]]
A549_TGFB1 <- eh[["EH10293"]]
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
objects <- list(A549_TGFB1 = A549_TGFB1, A549_EGF = A549_EGF, A549_TNF = A549_TNF)
EMscore_result <- add_EMT_score_multiple(objects, gmt_file,
  emt_names = c("Escore", "Mscore"),
  method = "nnPCA", nnPCA_dim = 1, cores = 2
)

Description

Arrange multiple ggplot objects with subtitles and common legend

Usage

Arrange_plots(
  plots_list,
  ncol_per_row = 2,
  subtitles = NULL,
  fig_title = NULL,
  common_legend = TRUE,
  legend_position = "bottom"
)

Arguments

Value

Combined ggplot object.

Description

Sample-level annotation for the cell lines in geneExp, mapping each sample name to its data source and epithelial/mesenchymal cell-type label. Used to demonstrate the plotting helpers.

Usage

data(cell_annotation_file)

Format

A data.frame with 118 rows and 3 columns: name (sample identifier, matching the columns of geneExp), source (data source, e.g. CCLE), and celltype_annotation (E/M state label).

Source

Sample-level annotation accompanying the cell-line expression matrix deposited at Zenodo, doi:10.5281/zenodo.19487376, subset to the samples retained in geneExp. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(cell_annotation_file)

Description

This function computes signature scores using Spearman correlation between a predefined gene signature and expression data. It supports both bulk expression matrices and Seurat objects.

Usage

compute_Signature_score(data, signature_file, score_name)

Arguments

Value

A numeric matrix (for bulk input) or a Seurat object with a new metadata column (for Seurat input).

Examples

data(geneExp)
signature_file <- system.file("extdata", "stemsig.tsv", package = "EMTscore")
compute_Signature_score(geneExp, signature_file, "Stemness_Score")

Description

This function calculates one or more gene signature scores for single-cell datasets stored as Seurat or SingleCellExperiment (SCE) objects. Each score is computed using Spearman correlation between gene expression profiles and signature weights, followed by min-max normalization. The resulting scores are added to the Seurat object metadata.

Usage

compute_Signature_score_SingleCell(objects, signature_files, score_names)

Arguments

Details

Each signature score is calculated by computing the Spearman correlation between the normalized expression of genes and their corresponding signature weights. The resulting scores are then scaled to a 0-1 range and stored in the Seurat object metadata under the specified names.

Value

A named list of Seurat objects, each containing new metadata columns corresponding to the computed signature scores.

Examples

obj <- system.file("extdata", "example_seurat_obj.rds", package = "EMTscore")
obj1 <- readRDS(obj)
files <- c(test = obj1)
signature_file1 <- system.file("extdata", "stemsig.tsv", package = "EMTscore")
signature_file2 <- system.file("extdata", "cellular_senescence_sig.tsv", package = "EMTscore")
signature_files <- c(signature_file1, signature_file2)
result <- compute_Signature_score_SingleCell(files, signature_files,
  score_names = c("Stemness_Score", "Senescence_Score")
)

Description

This function calculates pairwise correlations between all numeric columns of two input dataframes that contain sample-level scores (e.g., pathway, signature, or module scores). The correlation is computed only for samples (rows) shared by both dataframes.

Usage

correlate_sample_scores(score_mat1, score_mat2, method = "pearson")

Arguments

Details

This function is designed for comparing pathway, EMT, or signature scores across samples. It finds the intersection of sample names (row names) between the two dataframes and performs correlation analysis between every pair of numeric score columns.

Value

A dataframe containing:

Pathway_in_score_mat1

Column name from score_mat1

Pathway_in_score_mat2

Column name from score_mat2

Correlation

Correlation coefficient

P_value

P-value of the correlation test

Valid_n

Number of non-missing paired samples used in the test

Examples

set.seed(1)
ids <- paste0("sample", seq_len(20))
score_mat1 <- data.frame(PathwayA = rnorm(20), PathwayB = rnorm(20),
  row.names = ids)
score_mat2 <- data.frame(EMT = rnorm(20), row.names = ids)
correlate_sample_scores(score_mat1, score_mat2, method = "pearson")

Description

A data frame combining cell annotations with computed scores, ready to be passed to the plotting helpers.

Usage

data(data_for_plot)

Format

A data frame (rows = cells/samples) of annotations and scores.

Source

Precomputed example object built with data_prepare by merging cell_annotation_file with nnPCA scores computed from the bundled geneExp matrix. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(data_for_plot)

Description

Merge cell annotation and score result, and set rownames based on a user-specified column.

Usage

data_prepare(cell_annotation_file, score_result, merge_colname)

Arguments

Value

A data.frame ready for plotting.

Examples

data(cell_annotation_file)
data(nnPCA_Result_multiple)
data_for_plot <- data_prepare(cell_annotation_file, nnPCA_Result_multiple, merge_colname = "name")

Description

Read a GMT file and compute EMT scores for a gene expression matrix.

Usage

Execute_AUCell(exprMatrix, Genesets, score_names)

Arguments

Value

Data frame of samples with AUCell scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_AUCell(geneExp, gmt_file, score_names = "score")

Description

This function computes AUCell scores for each gene set in a GMT file using a given gene expression matrix (genes as rows, samples as columns). It runs in parallel across multiple cores and returns a combined data frame of AUCell scores for all pathways.

Usage

Execute_AUCell_parallel(exprMatrix, gmt_file, cores)

Arguments

Value

A data frame containing AUCell scores for each pathway across samples.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_AUCell_parallel(geneExp, gmt_file, cores = 1)

Description

Scatter plot of E vs M scores with mean +/- SD per cell type.

Usage

Execute_E_M_plot(
  data_for_plot,
  E_colname,
  M_colname,
  celltype_colname,
  colors = c("#F87189", "#CE9031", "#A48CF5", "#97A430", "#39A7D0", "#E57D5F", "#84C7B9",
    "#E1AF64", "#C26CCF", "#B0BF43", "#57C3E8", "#F29D9E", "#92AAE6")
)

Arguments

Value

A ggplot object.

Examples

data(data_for_plot)
plot <- Execute_E_M_plot(
  data_for_plot,
  E_colname = "Panchy_et_al_E_signature",
  M_colname = "Panchy_et_al_M_signature",
  celltype_colname = "celltype_annotation",
  colors = c(
    "#F87189", "#CE9031", "#A48CF5", "#97A430", "#39A7D0", "#E57D5F",
    "#84C7B9", "#E1AF64", "#C26CCF", "#B0BF43", "#57C3E8", "#F29D9E", "#92AAE6"
  )
)

Description

Read a GMT file and compute EMT scores for a gene expression matrix.

Usage

Execute_GSVA(exprMatrix, Genesets, score_names)

Arguments

Value

Data frame of samples with GSVA scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_GSVA(geneExp, gmt_file, score_names = "score")

Description

This function calculates GSVA scores for gene sets defined in a GMT file using the GSVA package. Parallel processing is used to speed up computation across pathways.

Usage

Execute_GSVA_parallel(exprMatrix, gmt_file, cores)

Arguments

Value

A data frame containing GSVA scores for each pathway across samples. Each row corresponds to a pathway with an additional 'Pathway' column.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_GSVA_parallel(geneExp, gmt_file, cores = 1)

Description

Read a GMT file and compute JASMINE scores for a gene expression matrix.

Usage

Execute_JAS(exprMatrix, Genesets, score_names)

Arguments

Value

Data frame of samples with JASMINE scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_JAS(geneExp, gmt_file, score_names = "score")

Description

This function calculates JASMINE scores for gene sets defined in a GMT file. Each pathway score combines normalized rank mean and odds ratio of gene expression across samples. Parallel processing is used to speed up computation across gene sets.

Usage

Execute_JASMINE_parallel(exprMatrix, gmt_file, cores)

Arguments

Value

A data frame containing JASMINE scores for each pathway across samples.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_JASMINE_parallel(geneExp, gmt_file, cores = 1)

Description

Scatter plot of two M scores with mean +/- SD per cell type.

Usage

Execute_M_dimension_plot(
  data_for_plot,
  M1_colname,
  M2_colname,
  celltype_colname,
  colors = c("#F87189", "#CE9031", "#A48CF5", "#97A430", "#39A7D0", "#E57D5F", "#84C7B9",
    "#E1AF64", "#C26CCF", "#B0BF43", "#57C3E8", "#F29D9E", "#92AAE6")
)

Arguments

Value

A ggplot object.

Examples

data(cell_annotation_file)
data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
nnPCA_Mscore <- Execute_nnPCA(geneExp, gmt_file,
  dimension = 2, score_names = c("M1_score", "M2_score"))
data_for_plot <- data_prepare(cell_annotation_file, nnPCA_Mscore, merge_colname = "name")
plot2 <- Execute_M_dimension_plot(
  data_for_plot,
  M1_colname = "M1_score",
  M2_colname = "M2_score",
  celltype_colname = "celltype_annotation",
  colors = c(
    "#F87189", "#CE9031", "#A48CF5", "#97A430", "#39A7D0", "#E57D5F",
    "#84C7B9", "#E1AF64", "#C26CCF", "#B0BF43", "#57C3E8", "#F29D9E", "#92AAE6"
  )
)

Description

Execute nnPCA Scoring from GMT File

Usage

Execute_nnPCA(exprMatrix, Genesets, dimension, score_names)

Arguments

Details

Read a GMT file and compute EMT scores for a gene expression matrix.

Value

Data frame of samples with nnPCA scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_nnPCA(geneExp, gmt_file, dimension = 1, score_names = "score")

Description

This function reads a GMT gene set file and performs non-negative sparse PCA (nnPCA) for each gene set in parallel, returning a combined data frame of pathway scores.

Usage

Execute_nnPCA_parallel(exprMatrix, Genesets, dimension, cores)

Arguments

Value

A data frame containing the first nnPCA component scores for each pathway.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_nnPCA_parallel(geneExp, gmt_file, dimension = 1, cores = 1)

Description

Read a GMT file and compute EMT scores for a gene expression matrix.

Usage

Execute_SCSE(exprMatrix, Genesets, score_names)

Arguments

Value

Data frame of samples with SCSE scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_SCSE(geneExp, gmt_file, score_names = "score")

Description

This function computes SCSE (Single Cell Signature Explorer) scores for each gene set in a GMT file using a given gene expression matrix (genes as rows, samples as columns). It runs in parallel across multiple cores and returns a combined data frame of SCSE scores for all pathways.

Usage

Execute_SCSE_parallel(exprMatrix, gmt_file, cores)

Arguments

Value

A data frame containing SCSE scores for each pathway across samples.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_SCSE_parallel(geneExp, gmt_file, cores = 1)

Description

Read a GMT file and compute EMT scores for a gene expression matrix.

Usage

Execute_ssGSEA(exprMatrix, Genesets, score_names)

Arguments

Value

Data frame of samples with ssGSEA scores.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
result <- Execute_ssGSEA(geneExp, gmt_file, score_names = "score")

Description

This function calculates ssGSEA scores for gene sets defined in a GMT file using the ssGSEA package. Parallel processing is used to speed up computation across pathways.

Usage

Execute_ssGSEA_parallel(exprMatrix, gmt_file, cores)

Arguments

Value

A data frame containing ssGSEA scores for each pathway across samples. Each row corresponds to a pathway with an additional 'Pathway' column.

Examples

data(geneExp)
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
result <- Execute_ssGSEA_parallel(geneExp, gmt_file, cores = 1)

Description

Removes (or keeps) gene sets from 'target_gmt' according to the proportion of their genes that appear anywhere in 'ref_gmt'. By default, only novel and non-redundant gene sets are kept (overlap < 'cutoff').

Usage

filter_gmt_by_reference(
  ref_gmt,
  target_gmt,
  output_gmt,
  cutoff = 0.5,
  keep_low_overlap = TRUE,
  min_genes = 5
)

Arguments

Value

Invisibly a 'data.frame' with one row per processed gene set and the columns:

Examples

ref_gmt <- system.file("extdata", "TianLab_collected_EMT_signatures.gmt",
  package = "EMTscore")
target_gmt <- system.file("extdata", "h.all.v2025.1.Hs.symbols.gmt",
  package = "EMTscore")
output_gmt <- tempfile(fileext = ".gmt")
# Keep only gene sets that share < 50 % genes with the TianLab EMT collection
filter_gmt_by_reference(
  ref_gmt,
  target_gmt,
  output_gmt,
  cutoff = 0.50,
  keep_low_overlap = TRUE
)

Description

A small log2-scale bulk gene expression matrix used to demonstrate the scoring functions in runnable examples. It is a subset of the CCLE / cell line panel distributed with the EMTscore study, restricted to the genes that appear in the bundled EMT signature gene sets (EM_signature.gmt and test.gmt) so that the examples run quickly without a network download.

Usage

data(geneExp)

Format

A numeric matrix with 544 rows (genes, row names are HGNC symbols) and 120 columns (samples/cell lines, column names match the name column of cell_annotation_file). Values are log2-transformed normalized expression.

Source

Subset of the full expression matrix deposited at Zenodo, doi:10.5281/zenodo.19487376 (file geneExp.rda). The processing script that produced this subset is in system.file("script", "make_geneExp.R", package = "EMTscore").

Examples

data(geneExp)
dim(geneExp)

Description

A curated collection of Gene Ontology (GO) gene sets used as input for EMT scoring.

Usage

data(GO)

Format

A list of gene sets, each a character vector of gene symbols.

Source

EMT-related gene sets derived from the Gene Ontology (GO) database (http://geneontology.org). Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(GO)

Description

Hallmark gene sets from the Molecular Signatures Database (MSigDB), provided as example pathway input.

Usage

data(MSigDB_Hallmark)

Format

A list of Hallmark gene sets, each a character vector of gene symbols.

Source

Hallmark (H) gene set collection from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb). Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(MSigDB_Hallmark)

Description

This dataset contains EMT score computed using the nnPCA method.

Usage

data(nnPCA_Result_EMT)

Format

A data frame or matrix of nnPCA-based EMT score.

EMT score results generated using the nnPCA method.

Source

Precomputed example output produced by running Execute_nnPCA on the bundled geneExp matrix with the EM_signature.gmt gene set shipped in system.file("extdata", package = "EMTscore"). Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(nnPCA_Result_EMT)

Description

Example Escore and Mscore values computed with the nnPCA method across multiple gene sets.

Usage

data(nnPCA_Result_multiple)

Format

A data frame with nnPCA Escore and Mscore columns (rows = samples).

Source

Precomputed example output produced by running Execute_nnPCA (with dimension = 2) on the bundled geneExp matrix and the EM_signature.gmt gene set shipped in system.file("extdata", package = "EMTscore"). Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(nnPCA_Result_multiple)

Description

Epithelial (E) marker gene set curated from Panchy et al., provided as an example EMT signature.

Usage

data(Panchy_et_al_E_signature)

Format

A character vector of epithelial-state gene symbols.

Source

Epithelial marker gene list curated from Panchy et al. Gene symbols were extracted from the published EMT signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Panchy_et_al_E_signature)

Description

Mesenchymal (M) marker gene set curated from Panchy et al., provided as an example EMT signature.

Usage

data(Panchy_et_al_M_signature)

Format

A character vector of mesenchymal-state gene symbols.

Source

Mesenchymal marker gene list curated from Panchy et al. Gene symbols were extracted from the published EMT signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Panchy_et_al_M_signature)

Description

This function visualizes EMT (epithelial mesenchymal transition) scores across pseudotime for multiple Seurat objects. Each Seurat object is treated as a different condition or sample.

Usage

plot_EMT_from_objects(obj_list, col_name, emt_score_col)

Arguments

Value

A ggplot object showing smoothed EMT score trajectories along pseudotime for each condition.

Examples

library(ExperimentHub)
eh <- ExperimentHub()
A549_TGFB1 <- eh[["EH10293"]]
gmt_file <- system.file("extdata", "EM_signature.gmt", package = "EMTscore")
objs <- add_EMT_score(list(A549_TGFB1 = A549_TGFB1), gmt_file,
  emt_name = "EMT_Score", method = "AUCell"
)
p <- plot_EMT_from_objects(objs,
  col_name = "nCount_RNA",
  emt_score_col = "EMT_Score"
)

Description

present result of heat map of genes that contribute to M score of dimension 1 and disension 2

Usage

plot_heatmap_function(geneExp, geneList_M)

Arguments

Value

Figure represent EMT score for different methods and gene sets

Examples

data(geneExp)
data(Tan_et_al_cell_line_M_signature)
geneList_M <- Tan_et_al_cell_line_M_signature
p <- plot_heatmap_function(t(geneExp), geneList_M)

Description

This function clusters PCA results using Gaussian Mixture Model (GMM) or K-means, identifies the clusters with maximum Escore and Mscore, assigns labels "E", "M", and "EM1", "EM2", ... to other clusters, and returns a named vector of final cluster labels for each sample.

Usage

predict_cluster_labels(
  pca_result,
  method = "GMM",
  n_clusters = 3,
  PC_name = c("Escore", "Mscore")
)

Arguments

Value

A named character vector of cluster labels for each sample.

Examples

data(nnPCA_Result_multiple)
labels_gmm <- predict_cluster_labels(nnPCA_Result_multiple,
  method = "GMM", n_clusters = 3, PC_name = c("Escore", "Mscore")
)
labels_km <- predict_cluster_labels(nnPCA_Result_multiple,
  method = "Kmeans", n_clusters = 3, PC_name = c("Escore", "Mscore")
)

Description

This function reads a GMT (Gene Matrix Transposed) file with GSEABase::getGmt() and extracts all unique genes across all gene sets.

Usage

read_gmt(fname)

Arguments

Value

A data frame with a single column gene, containing all unique genes.

Examples

gmt_file <- system.file("extdata", "test.gmt", package = "EMTscore")
genes <- read_gmt(gmt_file)

Description

Example EMT scores computed with the Single-Cell Signature Explorer (SCSE) method.

Usage

data(SCSE_Result_EMT)

Format

A data frame of SCSE-based EMT scores (rows = samples).

Source

Precomputed example output produced by running Execute_SCSE on the bundled geneExp matrix with the EM_signature.gmt gene set shipped in system.file("extdata", package = "EMTscore"). Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(SCSE_Result_EMT)

Description

Epithelial (E) signature gene set derived from cell-line data in Tan et al.

Usage

data(Tan_et_al_cell_line_E_signature)

Format

A character vector of epithelial signature gene symbols.

Source

Cell-line epithelial EMT signature curated from Tan et al. Gene symbols were extracted from the published signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Tan_et_al_cell_line_E_signature)

Description

Mesenchymal (M) signature gene set derived from cell-line data in Tan et al.

Usage

data(Tan_et_al_cell_line_M_signature)

Format

A character vector of mesenchymal signature gene symbols.

Source

Cell-line mesenchymal EMT signature curated from Tan et al. Gene symbols were extracted from the published signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Tan_et_al_cell_line_M_signature)

Description

Epithelial (E) signature gene set derived from tumor data in Tan et al.

Usage

data(Tan_et_al_tumor_E_signature)

Format

A character vector of epithelial signature gene symbols.

Source

Tumor epithelial EMT signature curated from Tan et al. Gene symbols were extracted from the published signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Tan_et_al_tumor_E_signature)

Description

Mesenchymal (M) signature gene set derived from tumor data in Tan et al.

Usage

data(Tan_et_al_tumor_M_signature)

Format

A character vector of mesenchymal signature gene symbols.

Source

Tumor mesenchymal EMT signature curated from Tan et al. Gene symbols were extracted from the published signature; no further processing was applied. Creation and provenance code: system.file("script", "make-data.R", package = "EMTscore").

Examples

data(Tan_et_al_tumor_M_signature)

Description

Convert multiple gene lists into a single GMT (Gene Matrix Transposed) formatted file. Each element of the input list represents one gene set, where the element name is the gene set name and the element value is a vector of gene symbols.

Usage

write_gmt(gene_sets, file)

Arguments

Value

No return value. The function writes a GMT file to the specified path.

Examples

gene_sets <- list(
  EMT_High = c("VIM", "ZEB1", "SNAI1"),
  EMT_Low  = c("CDH1", "OCLN", "DSP")
)
write_gmt(gene_sets, file = tempfile(fileext = ".gmt"))