| Title: | Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling |
|---|---|
| Description: | Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms. |
| Authors: | Junwei Han [aut, cre, ctb], Yinchun Su [aut], Siyuan Li [aut] |
| Maintainer: | Junwei Han <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.1 |
| Built: | 2024-11-12 04:11:12 UTC |
| Source: | https://github.com/cran/iPRISM |
Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.
iPRISM
# if (!"devtools" %in% as.data.frame(installed.packages())$Package) # install.packages("devtools") # devtools::install_github("hanjunwei-lab/iPRISM")# if (!"devtools" %in% as.data.frame(installed.packages())$Package) # install.packages("devtools") # devtools::install_github("hanjunwei-lab/iPRISM")
This function generates a correlation plot between two datasets, displaying correlation coefficients as a heatmap and significant correlations as scatter points.
cor_plot( data1, data2, sig.name1 = "value1", sig.name2 = "value2", cutoff.pvalue = 0.05, color = c("#62CCC9", "#FF9999") )cor_plot( data1, data2, sig.name1 = "value1", sig.name2 = "value2", cutoff.pvalue = 0.05, color = c("#62CCC9", "#FF9999") )
data1 |
A data frame or matrix representing the first dataset. |
data2 |
A data frame or matrix representing the second dataset. |
sig.name1 |
A character string specifying the name of the first dataset (default: "value1"). |
sig.name2 |
A character string specifying the name of the second dataset (default: "value2"). |
cutoff.pvalue |
The significance threshold for correlation (default: 0.05). |
color |
A vector of two colors for the heatmap gradient (default: c("#62CCC9", "#FF9999")). |
The function computes correlation coefficients between corresponding columns in the two datasets and identifies significant correlations based on p-values.
A ggplot object displaying the correlation heatmap and scatter points.
# Read all data into memory data(data.path, package = "iPRISM") data(data.cell, package = "iPRISM") # Draw the plot cor_plot(data1 = data.path,data2 = data.cell,sig.name1 = "path",sig.name2 = "cell")# Read all data into memory data(data.path, package = "iPRISM") data(data.cell, package = "iPRISM") # Draw the plot cor_plot(data1 = data.path,data2 = data.cell,sig.name1 = "path",sig.name2 = "cell")
The 'data_sig' represents the sample feature matrix, where rows correspond to samples, and columns correspond to features.
data_sigdata_sig
An object of class matrix (inherits from array) with 121 rows and 31 columns.
library(iPRISM) data(data_sig, package = "iPRISM") plot(data_sig)library(iPRISM) data(data_sig, package = "iPRISM") plot(data_sig)
The 'data.cell' represents the second type of feature matrix used for calculating correlations (in this case, cell abundances), where rows correspond to samples, and columns correspond to features.
data.celldata.cell
An object of class matrix (inherits from array) with 121 rows and 21 columns.
library(iPRISM) data(data.cell, package = "iPRISM") dim(data.cell)library(iPRISM) data(data.cell, package = "iPRISM") dim(data.cell)
The 'data.path' represents the first type of feature matrix used for calculating correlations (in this case, pathway expression levels), where rows correspond to samples, and columns correspond to features.
data.pathdata.path
An object of class matrix (inherits from array) with 121 rows and 17 columns.
library(iPRISM) data(data.path, package = "iPRISM") plot(data.path)library(iPRISM) data(data.path, package = "iPRISM") plot(data.path)
Calculates the enrichment score (ES) for a given set of labels and correlation vector.
ESscore(labels.list, correl.vector = NULL)ESscore(labels.list, correl.vector = NULL)
labels.list |
A binary vector indicating membership in a gene set (1 for inclusion, 0 for exclusion). |
correl.vector |
A vector of correlation values (e.g., gene expression correlations). |
The enrichment score (ES) for the given labels and correlation vector.
Calculates the weighted enrichment score (ES) for a given set of labels and correlation vector.
ESscore_weighted(labels.list, correl.vector = NULL)ESscore_weighted(labels.list, correl.vector = NULL)
labels.list |
A binary vector indicating membership in a gene set (1 for inclusion, 0 for exclusion). |
correl.vector |
A vector of correlation values (e.g., gene expression correlations). |
The weighted enrichment score (ES) for the given labels and correlation vector.
This gene list includes genes from tumor microenvironment (TME). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.
genelist_cpgenelist_cp
An object of class numeric of length 15867.
library(iPRISM) data(genelist_cp, package = "iPRISM")library(iPRISM) data(genelist_cp, package = "iPRISM")
This gene list includes genes from human leukocyte antigen (HLA). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.
genelist_hlagenelist_hla
An object of class numeric of length 15867.
library(iPRISM) data(genelist_hla, package = "iPRISM")library(iPRISM) data(genelist_hla, package = "iPRISM")
This gene list includes genes from immune checkpoint inhibitors (ICI). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.
genelist_immgenelist_imm
An object of class numeric of length 15867.
library(iPRISM) data(genelist_imm, package = "iPRISM")library(iPRISM) data(genelist_imm, package = "iPRISM")
This function performs gene set enrichment analysis (GSEA) based on multiplex network data.
get_gsea_path( seed = seed, network = network, gamma = 0.7, pathlist = pathlist, gsea.weight = 1, gsea.nperm = 1000 )get_gsea_path( seed = seed, network = network, gamma = 0.7, pathlist = pathlist, gsea.weight = 1, gsea.nperm = 1000 )
seed |
A seed value (optional). |
network |
A network object (e.g., protein-protein interaction network). |
gamma |
A parameter for random walk restart (default: 0.7). |
pathlist |
A predefined list of gene sets (pathways). |
gsea.weight |
Weight for GSEA (default: 1). |
gsea.nperm |
Number of permutations for significance testing (default: 1000). |
The function constructs a multiplex network, performs random walk restart, and calculates gene scores. It then transforms the scores and applies GSEA using the provided gene sets.
A GSEA result object.
data(Seeds, package = "iPRISM") data(ppi, package = "iPRISM") data(path_list, package = "iPRISM") result <- get_gsea_path(seed = Seeds, network = ppi, pathlist = path_list[1:2], gsea.nperm = 100) print(result)data(Seeds, package = "iPRISM") data(ppi, package = "iPRISM") data(path_list, package = "iPRISM") result <- get_gsea_path(seed = Seeds, network = ppi, pathlist = path_list[1:2], gsea.nperm = 100) print(result)
This function fits a logistic regression model to the given data.
get_logiModel(data.sig, pred.value, levels = c("R", "N"), step = TRUE)get_logiModel(data.sig, pred.value, levels = c("R", "N"), step = TRUE)
data.sig |
A data frame where each row is a sample and each column is a pathway. |
pred.value |
A numeric vector representing the response variable. |
levels |
A character vector specifying the levels of the response variable (default: c("R", "N")). |
step |
Logical. If TRUE, perform stepwise model selection (default: TRUE). |
The function converts the response variable to a factor with specified levels and fits a logistic regression model using the glm function.
A fitted logistic regression model.
data(data_sig, package = "iPRISM") b <- get_logiModel(data.sig = data_sig, pred.value = pred_value, step = TRUE) summary(b)data(data_sig, package = "iPRISM") b <- get_logiModel(data.sig = data_sig, pred.value = pred_value, step = TRUE) summary(b)
This function performs gene set enrichment analysis using a gene list and a set of pathways.
gseafun(genelist, pathlist, nperm = 1000, weighted = 1)gseafun(genelist, pathlist, nperm = 1000, weighted = 1)
genelist |
A named vector of gene expression values. |
pathlist |
A list of gene sets (pathways) to test for enrichment. |
nperm |
Number of permutations for calculating p-values (default is 1000). |
weighted |
Logical indicating whether to use weighted enrichment scores (default is TRUE). |
A data frame with enrichment scores (ES), p-values, and adjusted p-values.
data(path_list, package = "iPRISM") data(genelist_imm, package = "iPRISM") res_gsea_imm <- gseafun(genelist = genelist_imm, pathlist = path_list[1:2], weighted = 1, nperm = 1000) print(res_gsea_imm)data(path_list, package = "iPRISM") data(genelist_imm, package = "iPRISM") res_gsea_imm <- gseafun(genelist = genelist_imm, pathlist = path_list[1:2], weighted = 1, nperm = 1000) print(res_gsea_imm)
The 'path_list' contains the gene list associated with pathways.
path_listpath_list
An object of class list of length 2656.
library(iPRISM) data(path_list, package = "iPRISM") length(path_list)library(iPRISM) data(path_list, package = "iPRISM") length(path_list)
An igraph object containing a protein-protein physical interaction network.
ppippi
An object of class igraph of length 15867.
library(iPRISM) data(ppi, package = "iPRISM") library(igraph) graph <- simplify(ppi) graph_comp <- components(graph)$membership == which.max(components(graph)$csize) graph <- induced_subgraph(graph, V(graph)[graph_comp]) plot(graph)library(iPRISM) data(ppi, package = "iPRISM") library(igraph) graph <- simplify(ppi) graph_comp <- components(graph)$membership == which.max(components(graph)$csize) graph <- induced_subgraph(graph, V(graph)[graph_comp]) plot(graph)
A named vector where each element corresponds to a sample name and represents the original class label.
A named vector where each element corresponds to a sample name and represents the original class label.
pred_value pred_valuepred_value pred_value
An object of class character of length 121.
An object of class character of length 121.
library(iPRISM) data(pred_value, package = "iPRISM") table(pred_value) library(iPRISM) data(pred_value, package = "iPRISM") table(pred_value)library(iPRISM) data(pred_value, package = "iPRISM") table(pred_value) library(iPRISM) data(pred_value, package = "iPRISM") table(pred_value)
A character vector with seed node names.
SeedsSeeds
An object of class character of length 3.
library(iPRISM) data(Seeds, package = "iPRISM")library(iPRISM) data(Seeds, package = "iPRISM")