| Title: | Dynamic Network Biomarker Analysis for Critical Transitions |
|---|---|
| Description: | A comprehensive toolkit for detecting critical transitions and identifying dynamic network biomarkers (DNB) in biological systems. Critical transitions, characterized by sudden shifts between distinct states, are prevalent in complex biological processes including disease progression, cellular differentiation, and developmental transitions. This package implements seven complementary DNB methodologies: (1) conventional DNB (cDNB) based on the original DNB theory (Chen et al. 2012 <doi:10.1038/srep00342>); (2) topological DNB (tDNB), a novel approach utilizing network topology and scale-free properties; (3) landscape DNB (LDNB) for quantifying state transitions (Liu et al. 2019 <doi:10.1093/nsr/nwy162>); (4) local DNB (LcDNB) leveraging protein-protein interaction networks; (5) module-based DNB (MDNB) for modular analysis (Li et al. 2022 <doi:10.1016/j.xinn.2022.100364>); (6) time-series network module biomarker (TSNMB) for temporal dynamics (Zhong et al. 2022 <doi:10.1093/jmcb/mjac052>); and (7) time-series leading edge (TSLE) analysis (Liu et al. 2020 <doi:10.1093/bioinformatics/btz758>). Core computational routines are implemented in C++ via 'Rcpp' for optimal performance. Compatible with bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data. Includes curated protein-protein interaction networks for human and mouse from the STRING database. |
| Authors: | Zaoqu Liu [aut, cre] (ORCID: <https://orcid.org/0000-0002-0452-742X>), Chuhan Zhang [ctb] (MDNB implementation) |
| Maintainer: | Zaoqu Liu <[email protected]> |
| License: | GPL (>= 3) + file LICENSE |
| Version: | 2.0.0 |
| Built: | 2026-05-22 08:42:04 UTC |
| Source: | https://github.com/SolvingLab/BioTransition |
Performing conventional dynamic network biomarker analysis based on the original DNB theory proposed by Chen et al. (2012).
cDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.size = 10, max.size = 2000, AddModuleSize = FALSE )cDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.size = 10, max.size = 2000, AddModuleSize = FALSE )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
variation.method |
specifies the method for calculating gene variation. sd or cv. |
min.size |
Minimum gene number of gene modules. |
max.size |
Maximum gene number of gene modules. |
AddModuleSize |
Whether to consider gene module size when calculating DNB score. |
A list containing:
A data.frame with composite index (CI) values for each state, including V_in (mean variation), R_in (mean correlation within DNB), and R_out (mean correlation with non-DNB genes)
Character vector of genes in the identified DNB module
List of data.frames with CI scores for all candidate modules in each state
List of gene modules detected in each state
Data.frame summarizing the best candidate module per state
List of correlation matrices for each state
List of gene variation values for each state
Zaoqu Liu; E-mail: [email protected]
Chen L, Liu R, Liu ZP, Li M, Aihara K. (2012). Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports, 2:342. doi:10.1038/srep00342
tDNB for topological DNB,
LcDNB for local DNB with PPI,
MDNB for module-based DNB
# Create example data set.seed(42) n_genes <- 100 n_samples <- 15 expr <- matrix( rnorm(n_genes * n_samples, mean = 10, sd = 2), nrow = n_genes, ncol = n_samples ) rownames(expr) <- paste0("Gene", seq_len(n_genes)) colnames(expr) <- paste0("Sample", seq_len(n_samples)) state <- data.frame( sample_id = colnames(expr), state = rep(c("A", "B", "C"), each = 5) ) result <- cDNB( expr = expr, state = state, state.levels = c("A", "B", "C"), min.size = 5, max.size = 50 ) result$DNB.score# Create example data set.seed(42) n_genes <- 100 n_samples <- 15 expr <- matrix( rnorm(n_genes * n_samples, mean = 10, sd = 2), nrow = n_genes, ncol = n_samples ) rownames(expr) <- paste0("Gene", seq_len(n_genes)) colnames(expr) <- paste0("Sample", seq_len(n_samples)) state <- data.frame( sample_id = colnames(expr), state = rep(c("A", "B", "C"), each = 5) ) result <- cDNB( expr = expr, state = state, state.levels = c("A", "B", "C"), min.size = 5, max.size = 50 ) result$DNB.score
Performs local conventional dynamic network biomarker (LcDNB) analysis using protein-protein interaction (PPI) networks.
LcDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.first.neighbor.size = 3, min.second.neighbor.size = 1, ppi = ppi_h, min.combined.score = 900, percent = TRUE, top.n = 30, top.p = 0.05, AddModuleSize = FALSE )LcDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.first.neighbor.size = 3, min.second.neighbor.size = 1, ppi = ppi_h, min.combined.score = 900, percent = TRUE, top.n = 30, top.p = 0.05, AddModuleSize = FALSE )
expr |
A numeric matrix with genes in rows and samples in columns. |
state |
A data.frame with sample IDs and state labels (2 columns). |
state.levels |
Character vector specifying the order of states. |
cor.method |
Correlation method: "pearson", "spearman", or "kendall". |
p.adjust.method |
P-value adjustment method. Default: "BH". |
variation.method |
Method for variation: "sd" or "cv". Default: "sd". |
min.first.neighbor.size |
Minimum first-order neighbors. Default: 3. |
min.second.neighbor.size |
Minimum second-order neighbors. Default: 1. |
ppi |
PPI network data.frame with G1, G2, combined_score columns. |
min.combined.score |
Minimum STRING score. Default: 900. |
percent |
Use percentage (TRUE) or absolute number (FALSE). |
top.n |
Number of top genes when percent=FALSE. Default: 30. |
top.p |
Proportion when percent=TRUE. Default: 0.05. |
AddModuleSize |
Weight by module size. Default: FALSE. |
A list containing DNB.score, DNB.genes, CI_all, Gene_module, Candidate, Cor, V, PPI.used, first.order.genes, second.order.genes.
Zaoqu Liu
# See vignette for detailed examples# See vignette for detailed examples
Performs landscape dynamic network biomarker (LDNB) analysis for detecting critical transitions. This method uses sample-specific perturbation networks (SSPN) to identify tipping points in biological state transitions.
LDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 990, min.first.neighbor.size = 10, min.second.neighbor.size = 1, use.PCC.P.type = "FDR", use.PCC.P.cutoff = 0.05, percent = FALSE, top.n = 30, top.p = 0.05, nCores = max(1, parallel::detectCores() - 2) )LDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 990, min.first.neighbor.size = 10, min.second.neighbor.size = 1, use.PCC.P.type = "FDR", use.PCC.P.cutoff = 0.05, percent = FALSE, top.n = 30, top.p = 0.05, nCores = max(1, parallel::detectCores() - 2) )
expr |
A numeric matrix or data.frame with genes in rows and samples in columns. Row names should be gene symbols. |
state |
A data.frame with exactly two columns: sample identifiers and
state labels. Must include a |
state.levels |
A character vector specifying the order of states. The
first level should be |
cor.method |
Character string specifying correlation method. One of
|
p.adjust.method |
Character string specifying p-value adjustment method.
Default: |
ppi |
A data.frame containing protein-protein interactions with columns
|
min.combined.score |
Numeric. Minimum STRING combined score. Default: 990. |
min.first.neighbor.size |
Integer. Minimum first-order neighbors. Default: 10. |
min.second.neighbor.size |
Integer. Minimum second-order neighbors. Default: 1. |
use.PCC.P.type |
Character. Type of p-value for filtering: |
use.PCC.P.cutoff |
Numeric. P-value cutoff. Default: 0.05. |
percent |
Logical. Use percentage ( |
top.n |
Integer. Number of top genes when |
top.p |
Numeric. Proportion when |
nCores |
Integer. Number of CPU cores for parallel computation. |
LDNB analysis requires a reference state (labeled as "ref" in the
state column) to construct background networks. The algorithm:
Constructs sample-specific perturbation networks (SSPN)
Calculates local landscape indices for each gene
Aggregates to global indices per sample and state
Identifies critical state and DNB genes
A list containing:
Data.frame with global indices per state
Character vector of identified DNB genes
Data.frame of genes ranked by landscape index
Data.frame with global index per sample
List of local indices per sample
Sample-specific perturbation network results
Zaoqu Liu [email protected]
Liu R, et al. (2019). Single-sample landscape entropy reveals the imminent phase transition during disease progression. National Science Review, 7(7):775-785. doi:10.1093/nsr/nwy162
# LDNB requires a reference state # See vignette for detailed examples# LDNB requires a reference state # See vignette for detailed examples
Performing module-based dynamic network biomarker analysis using PPI network
MDNB( expr, state, state.levels, cor.method = "pearson", ppi = ppi_h, min.combined.score = 900, PCC.min = 0.02, PCC.module = 0.02, numeber.module.QI = 10 )MDNB( expr, state, state.levels, cor.method = "pearson", ppi = ppi_h, min.combined.score = 900, PCC.min = 0.02, PCC.module = 0.02, numeber.module.QI = 10 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A dataframe with two columns: sample names and group information. |
state.levels |
A vector for state sequence (e.g., cell groups, time points, treatment conditions). |
cor.method |
Specifies the method for correlation analysis. |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
PCC.min |
Remove genes if their highest correlation with any other gene is less than this value. |
PCC.module |
A gene must maintain at least this minimum correlation with the seed gene in every group to be included in the module. |
numeber.module.QI |
Number of gene modules selected for QI calculation. |
A list containing:
DNB.score: The DNB score values (QI values) calculated for different groups.
DNB.genes: Genes from the module with highest CI in the critical state.
CI_all: Module contribution index (CI) details for each state.
Gene_module: Generated co-expression gene modules.
QIplot: Line chart showing QI changes across states.
metadata: Additional information about the analysis.
Zaoqu Liu, Chuhan Zhang; Email: [email protected]
Li L, Xu Y, Yan L, et al. Dynamic network biomarker factors orchestrate cell-fate determination at tipping points during hESC differentiation. Innovation (Camb). 2022 Dec 20;4(1):100364. doi: 10.1016/j.xinn.2022.100364
Protein-protein interaction network for human (Homo sapiens) from STRING database v12.0. This dataset contains interactions with confidence scores, suitable for network-based DNB analyses including LcDNB, MDNB, LDNB, TSNMB, and TSLE.
ppi_hppi_h
A data frame with protein-protein interactions:
Gene 1 symbol (character). First interacting gene.
Gene 2 symbol (character). Second interacting gene.
Interaction confidence score (numeric, 0-999). Higher scores indicate stronger evidence for interaction. Typical cutoffs: 400 (medium), 700 (high), 900 (highest).
Data Source: STRING database v12.0 (https://string-db.org/)
Species: Homo sapiens (NCBI Taxonomy ID: 9606)
Recommended Score Cutoffs:
400-600: Medium confidence (broad coverage)
700-800: High confidence (balanced)
900+: Highest confidence (most reliable)
Usage in DNB Analysis: Use this PPI network for human single-cell or bulk RNA-seq data with PPI-based DNB methods: LcDNB, MDNB, LDNB, TSNMB, TSLE.
STRING database v12.0 https://string-db.org/ https://stringdb-downloads.org/
Szklarczyk D, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638-D646.
ppi_m for mouse PPI network
# Load human PPI data("ppi_h") # Explore the network dim(ppi_h) head(ppi_h) # Check gene coverage all_genes <- unique(c(ppi_h$G1, ppi_h$G2)) length(all_genes) # Filter by confidence high_conf <- ppi_h[ppi_h$combined_score >= 700, ] nrow(high_conf)# Load human PPI data("ppi_h") # Explore the network dim(ppi_h) head(ppi_h) # Check gene coverage all_genes <- unique(c(ppi_h$G1, ppi_h$G2)) length(all_genes) # Filter by confidence high_conf <- ppi_h[ppi_h$combined_score >= 700, ] nrow(high_conf)
Protein-protein interaction network for mouse (Mus musculus) from STRING database v12.0. This dataset contains interactions with confidence scores, suitable for network-based DNB analyses including LcDNB, MDNB, LDNB, TSNMB, and TSLE.
ppi_mppi_m
A data frame with 12,684,354 protein-protein interactions:
Gene 1 symbol (character). First interacting gene.
Gene 2 symbol (character). Second interacting gene.
Interaction confidence score (numeric, 0-999). Higher scores indicate stronger evidence for interaction. Typical cutoffs: 400 (medium), 700 (high), 900 (highest).
Data Source: STRING database v12.0 (https://string-db.org/)
Species: Mus musculus (NCBI Taxonomy ID: 10090)
Statistics:
Total interactions: 12,684,354
Unique genes: 21,645
Score range: 150-999
Recommended Score Cutoffs:
400-600: Medium confidence (broad coverage)
700-800: High confidence (balanced)
900+: Highest confidence (most reliable)
Usage in DNB Analysis: Use this PPI network for mouse single-cell or bulk RNA-seq data with PPI-based DNB methods: LcDNB, MDNB, LDNB, TSNMB, TSLE.
STRING database v12.0 https://string-db.org/ https://stringdb-downloads.org/
Szklarczyk D, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638-D646.
ppi_h for human PPI network
# Load mouse PPI data("ppi_m") # Explore the network dim(ppi_m) head(ppi_m) # Check gene coverage all_genes <- unique(c(ppi_m$G1, ppi_m$G2)) length(all_genes) # 21,645 genes # Filter by confidence high_conf <- ppi_m[ppi_m$combined_score >= 700, ] nrow(high_conf) ## Not run: # Use in DNB analysis result <- MDNB( expr = mouse_expr, state = sample_groups, state.levels = c("Control", "Treatment"), ppi = ppi_m, min.combined.score = 700 ) ## End(Not run)# Load mouse PPI data("ppi_m") # Explore the network dim(ppi_m) head(ppi_m) # Check gene coverage all_genes <- unique(c(ppi_m$G1, ppi_m$G2)) length(all_genes) # 21,645 genes # Filter by confidence high_conf <- ppi_m[ppi_m$combined_score >= 700, ] nrow(high_conf) ## Not run: # Use in DNB analysis result <- MDNB( expr = mouse_expr, state = sample_groups, state.levels = c("Control", "Treatment"), ppi = ppi_m, min.combined.score = 700 ) ## End(Not run)
Performing single-sample landscape entropy analysis.
SLE( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 1, percent = TRUE, top.n = 30, top.p = 0.05, nCores = parallel::detectCores() - 10 )SLE( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 1, percent = TRUE, top.n = 30, top.p = 0.05, nCores = parallel::detectCores() - 10 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
min.first.neighbor.size |
Minimum size of first order genes of a specific center gene. |
percent |
Whether to use Percent to determine the number of DNB genes. |
top.n |
Only percent = FALSE takes effect. Center genes with top (number) DNB score were defined as DNB genes. |
top.p |
Only percent = TRUE takes effect. Center genes with top (percent) DNB score were defined as DNB genes. |
nCores |
The number of cores will be used. |
Zaoqu Liu; E-mail: [email protected]
Performing single-sample network module biomarker analysis.
sNMB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05, nCores = parallel::detectCores() - 10 )sNMB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05, nCores = parallel::detectCores() - 10 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
min.first.neighbor.size |
Minimum size of first order genes of a specific center gene. |
percent |
Whether to use Percent to determine the number of DNB genes. |
top.n |
Only percent = FALSE takes effect. Center genes with top (number) DNB score were defined as DNB genes. |
top.p |
Only percent = TRUE takes effect. Center genes with top (percent) DNB score were defined as DNB genes. |
nCores |
The number of cores will be used. |
Zaoqu Liu; E-mail: [email protected]
Performing sample-specific perturbation network analysis based on PPI network.
SSPN1( expr, ref.samples, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, nCores = parallel::detectCores() - 10 )SSPN1( expr, ref.samples, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, nCores = parallel::detectCores() - 10 )
expr |
A expression dataframe with gene rows and sample columns. |
ref.samples |
Samples for constructing the background reference network; Column ID or names. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
nCores |
The number of cores will be used. |
Zaoqu Liu; E-mail: [email protected]
Performing sample-specific perturbation network analysis based on customized network.
SSPN2( expr, ref.samples, net, cor.method = "pearson", p.adjust.method = "BH", nCores = parallel::detectCores() - 10 )SSPN2( expr, ref.samples, net, cor.method = "pearson", p.adjust.method = "BH", nCores = parallel::detectCores() - 10 )
expr |
A expression dataframe with gene rows and sample columns. |
ref.samples |
Samples for constructing the background reference network; Column ID or names. |
net |
A customized network with two columns. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
nCores |
The number of cores will be used. |
Zaoqu Liu; E-mail: [email protected]
Performing topological dynamic network biomarker analysis using scale-free network topology. This is a novel method developed by Liu Z. that leverages topological overlap matrix (TOM) for more robust DNB detection.
tDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.size = 10, max.size = 2000, AddModuleSize = FALSE, power.vec = c(seq_len(10), seq(from = 12, to = 20, by = 2)), network.type = "signed", RsquaredCut = 0.85 )tDNB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", variation.method = "sd", min.size = 10, max.size = 2000, AddModuleSize = FALSE, power.vec = c(seq_len(10), seq(from = 12, to = 20, by = 2)), network.type = "signed", RsquaredCut = 0.85 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
variation.method |
specifies the method for calculating gene variation. sd or cv. |
min.size |
Minimum gene number of gene modules. |
max.size |
Maximum gene number of gene modules. |
AddModuleSize |
Whether to consider gene module size when calculating DNB score. |
power.vec |
a vector of soft thresholding powers for which the scale free topology fit indices are to be calculated. |
network.type |
network type. Allowed values are (unique abbreviations of) "unsigned" or "signed". |
RsquaredCut |
desired minimum scale free topology fitting index R^2. |
A list containing:
A data.frame with composite index (CI) values for each state, including V_in (mean variation), R_in (mean correlation within DNB), and R_out (mean correlation with non-DNB genes)
Character vector of genes in the identified DNB module
List of data.frames with CI scores for all candidate modules in each state
List of gene modules detected in each state
Data.frame summarizing the best candidate module per state
List of correlation matrices for each state
List of topological overlap matrices for each state
List of gene variation values for each state
List of scale-free network fitting results
Zaoqu Liu; E-mail: [email protected]
# Create example data set.seed(42) n_genes <- 100 n_samples <- 15 expr <- matrix( rnorm(n_genes * n_samples, mean = 10, sd = 2), nrow = n_genes, ncol = n_samples ) rownames(expr) <- paste0("Gene", seq_len(n_genes)) colnames(expr) <- paste0("Sample", seq_len(n_samples)) state <- data.frame( sample_id = colnames(expr), state = rep(c("A", "B", "C"), each = 5) ) result <- tDNB( expr = expr, state = state, state.levels = c("A", "B", "C"), min.size = 5, max.size = 50 ) result$DNB.score# Create example data set.seed(42) n_genes <- 100 n_samples <- 15 expr <- matrix( rnorm(n_genes * n_samples, mean = 10, sd = 2), nrow = n_genes, ncol = n_samples ) rownames(expr) <- paste0("Gene", seq_len(n_genes)) colnames(expr) <- paste0("Sample", seq_len(n_samples)) state <- data.frame( sample_id = colnames(expr), state = rep(c("A", "B", "C"), each = 5) ) result <- tDNB( expr = expr, state = state, state.levels = c("A", "B", "C"), min.size = 5, max.size = 50 ) result$DNB.score
Performing time-series landscape entropy analysis.
TSLE( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05 )TSLE( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
min.first.neighbor.size |
Minimum size of first order genes of a specific center gene. |
percent |
Whether to use Percent to determine the number of DNB genes. |
top.n |
Only percent = FALSE takes effect. Center genes with top (number) DNB score were defined as DNB genes. |
top.p |
Only percent = TRUE takes effect. Center genes with top (percent) DNB score were defined as DNB genes. |
Zaoqu Liu; E-mail: [email protected]
Performing time-series network module biomarker analysis.
TSNMB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05 )TSNMB( expr, state, state.levels, cor.method = "pearson", p.adjust.method = "BH", ppi = ppi_h, min.combined.score = 900, min.first.neighbor.size = 3, percent = TRUE, top.n = 30, top.p = 0.05 )
expr |
A expression dataframe with gene rows and sample columns. |
state |
A time-series dataframe with two columns, the first is the sample names and the second is the group or time point information. |
state.levels |
A vector for state sequence. |
cor.method |
specifies the method for correlation analysis. |
p.adjust.method |
correction method, a character string. Can be abbreviated. c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") |
ppi |
Protein-protein interaction network; background network. |
min.combined.score |
Minimum combined score for determining protein-protein interaction. |
min.first.neighbor.size |
Minimum size of first order genes of a specific center gene. |
percent |
Whether to use Percent to determine the number of DNB genes. |
top.n |
Only percent = FALSE takes effect. Center genes with top (number) DNB score were defined as DNB genes. |
top.p |
Only percent = TRUE takes effect. Center genes with top (percent) DNB score were defined as DNB genes. |
Zaoqu Liu; E-mail: [email protected]