Mypackage is a comprehensive bioinformatic data analysis R package focusing on cancer genomics, expression profile analysis, and integrated analysis of clinical data. This package provides a series of efficient tool functions to help researchers quickly process, analyze, and visualize various bioinformatic data.
- MAF format data processing and analysis
- Correlation analysis between mutation and clinical data
- Mutation network construction and visualization
- Integrated analysis of mutation and expression data
- Differential gene expression analysis
- GSEA enrichment analysis and visualization
- Visualization of immune-related gene expression analysis
- Survival curve plotting
- COX forest plot analysis
- Correlation analysis between clinical and molecular features
- HRD (Homologous Recombination Deficiency) score calculation
- CNV (Copy Number Variation) and SV (Structural Variation) acquisition
- Multiple heatmap plotting functions
- Integrated visualization of pathways and mutations
- Immune infiltration visualization
# Install the devtools package (if not already installed)
install.packages("devtools")
# Install mypackage from local directory
devtools::install_local("d:/R/libs/mypackage")
or install.packages("./Mypackage_1.3.1.tar.gz", repos = NULL, type = "source")
# Install mypackage online
remotes::install_github("ypmjx123/Mypackage")
# Load the package
library(Mypackage)Mypackage depends on the following R packages, which will be automatically installed during installation: -ggplot2: Data visualization -maftools: MAF file processing and analysis -survival & survminer: Survival analysis -limma: Differential expression analysis -dplyr: Data manipulation -tidyr: Data tidying -stringr: String manipulation -biomaRt: Gene annotation -pheatmap: Heatmap plotting
Mypackage includes several built-in datasets for users to learn and test functions:：
| Dataset Name | Description |
|---|---|
mutation_CRC |
Colorectal cancer mutation data |
clin_TCGA |
TCGA clinical data |
exp_raw |
Raw gene expression data |
Gene_group_CRC1 |
CRC-related gene set |
pathway_data |
Pathway-related data |
hotsgenes |
Hotspot gene list |
tumor_ploidy |
Tumor ploidy data |
Analyzes the correlation between mutation data and clinical characteristics.
# Example usage
result <- maf_cor(mutation_data = mutation_CRC, clin = clin_TCGA, top=20,corrplot_method = "circle")Constructs a mutation gene network.
# Example usage
network <- mut_network(SNV=mutation_CRC,top=20,pValue=0.01,customdata=NULL)Converts custom mutation format to MAF format.
# Example usage
maf_obj <- mutToMAF(root_dir=root_dir,clin=clindata,tumor_t=10,site_depth=100,hotspot_vaf=0.009,
non_hotspot_vaf=0.045,hotspotloss_vaf=0.095,non_hotspotloss_vaf=0.195)Differential expression analysis and visualization.
# Example usage
dif_results <- limma.dif.visual(exprdata=exp_CRC[,-1],
pdata=Gene_group_CRC1,datatype="TPM",
Type=c("Wild", "Mut"),diff_method="limma",
contrastfml= "Wild - Mut",
tumor="CRC TCGA",
P.Value=0.05,
logFC=0.5,tidyHeatmap=TRUE,
color= NULL,
ann_colors = list(regulate = c(Down = "#1B9E77", Up = "#D95F02"),
PREX2 = c(Wild = "#757575", Mut = "#FF4040")),
Regulate=c("Up","Down"),GO=TRUE,GO.plot="dotplot",split=TRUE,
KEGG=TRUE,KEGG.plot="dotplot",rel_heights= c(1.5, 0.5, 1))GSEA enrichment analysis and advanced visualization.
# Example usage
gsea_plot <- GSEAplot2(x = gseaResult)Survival curve plotting and comparison.
# Example usage
surv_plot <- ggsurvplotsggsurvplots(data = clin_TCGA, conf.int = FALSE,time_col = "PFS_MONTHS",
status_col = "PFS_STATUS", group_col = "Status", pvalue_table = TRUE,
palette = ggsci::pal_ucscgb()(4), risk.table = FALSE, title = NULL,
legend.labs = c("no KRAS or TP53", "TP53", "KRAS", "KRAS&TP53"),
xlab = "PFS_MONTHS", ylab = "Survival probability",
surv.median.line="hv",surv.scale="default",legend=FALSE)COX regression forest plot plotting.
# Example usage
cox = cox_forest(data=aa,
time_col = "OS_MONTHS",
status_col = "OS_STATUS",
Univariate=T,
univar_predictors=colnames(aa)[c(6:7,18:21,30,24,25,33)],
Multivariate=T,
multivar_predictors = colnames(aa)[c(6:7,18:21,30,24,25,33)],
show_plots = T,xticks1=NULL,#c(0,0.25,0.5,0.75,1.00,1.25,1.5,6.5,11),
xticks2=NULL,#c(0,0.25,0.5,0.75,1.00,2,2.5,6,15),
title_univar = "OS Univariate",
title_multivar = "OS Multivariate",
use_baseline_table = TRUE,all =T,forestplot=F,ci_pch=16,ci_col="red",ci_line="blue",zero_col="#e22e2a",
log2=T,footnote=NULL)Calculate the HRD (homologous recombination defect) score.
# Example usage
hrd_scores <- HRDscore(dirpath = "d:/R/libs/mypackage/inst/HRD/")Acquire and analyze CNV and SV data.
# Example usage
cnv_sv_data <- CNV_SV_get(data=data,Type=c("rearrangement/fusion","amplification"))Feature heat map drawing.
# Example usage
heatmap <- sig_Heatmap(input = input, features = feas,ID ="SAMPLE_ID",show_plot=F,
condiction=condiction,id_condiction=colnames(condiction)[[1]],col_condiction=colnames(condiction)[[2]],
cols_group=c("#757575","#FF4040"),row_group=c("red","green"),
legend_show=TRUE,column_title_size=10,row_title_size=8,
heatmap_col=NULL,
#heatmap_col=c("#0505FA", "#FFFFFF", "#FA050D"),
group = "PREX2",row_title="Regulate", scale = TRUE,name="Expression")Immune infiltration visualization.
# Example usage
immu_plot <- immu_visual(im=NULL,exp=exp_CRC[,-1],
method = 'epic',
sample_group=Gene_group_CRC1,
tumor="CRC TCGA",heatmap=TRUE,
Type=c("Wild", "Mut"),
color=c("#757575", "#FF4040"),
geom_text=TRUE,
test = "wilcox.test")Here is an example of a complete analysis process that shows how to use mypackage for the full range of analysis from data processing to visualization:
# Load package and data
library(Mypackage)
data(mutation_CRC)
data(clin_TCGA)
data(exp_raw)
# 1. Mutagenesis data analysis
mut_cor_result <- maf_cor(mutation_data = mutation_CRC, clin = clin_TCGA, gene = "TP53")
print(mut_cor_result)
# 2. Expression difference analysi
dif_genes <- limma.dif.visual(exp_matrix = exp_raw, group_info = sample_group)
# 3. GSEA analyse
gsea_results <- GSEAplot2(gene_list = dif_genes$logFC, pathway_db = "KEGG")
# 4.survival analysis
survival_plot <- ggsurvplots(surv_data = clin_TCGA, gene_expr = exp_raw, gene = "TP53")
# 5. Comprehensive visualization
heatmap_plot <- sig_Heatmap(expr_matrix = exp_raw, group_info = sample_group, top_genes = 50)The project structure of Mypackage is organized as follows:
mypackage/
├── R/ # R Function source file
├── data/ # Built-in data set
├── inst/ # Additional installation files (HRD data, sample data, etc.)
├── man/ # Help document
├── vignettes/ # Long documents and tutorials
├── DESCRIPTION # Metadata and dependency information
├── NAMESPACE # Definition of namespaces
└── README.md # Project Description Document
Mypackage contains the following main functions:
| function name | characterization |
|---|---|
maf_cor |
Correlation analysis between mutation and clinical data |
mut_network |
Construction of mutation networks |
mutToMAF |
Convert to MAF format |
limma.dif.visual |
Differential expression analysis and visualization |
GSEAplot2 |
GSEA enrichment analysis |
ggsurvplots |
Survial curve mapping |
cox_forest |
COX forest diagram analysis |
HRDscore |
Calculation of the HRD score |
CNV_SV_get |
CNV and SV data acquisition (non-analysis) |
sig_Heatmap |
Feature heat map drawing |
immu_visual |
Immune infiltration visualization |
gene.mut_exp |
Analysis of gene mutation and expression integration |
path_mut_visual |
Visualization of pathway and mutation integration |
get_annotation |
Gene annotation acquisition |
exp_geneIDtoSYMBOL |
Express data ID conversion |
# View help for specific functions
doc <- help("maf_cor")
# View the overall help for packages
doc <- help(package = "Mypackage")# View all available vignettes
browseVignettes("Mypackage")
# Open a specific vignette
vignette("Mypackge")Mypackage depends on the following R packages:
- R (>= 4.4.3)
- ggplot2
- maftools
- survival
- survminer
- limma
- dplyr
- tidyr
- stringr
- biomaRt
- pheatmap
- gridExtra
- RColorBrewer
We welcome contributions from the community. If you want to contribute to my package, follow these steps:
- Fork warehouse
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Submit your changes (
git commit -m 'Add some AmazingFeature') - Pushed to branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Yao J, Sun Q, Wu H, et al. Decoding the molecular landscape: HER2 and PD-L1 in advanced gastric cancer. Front Immunol. 2025;16:1567308. doi:10.3389/fimmu.2025.1567308
- Qiu Q, Tan D, Chen Q, et al. Clinical implications of PD-L1 expression and pathway-related molecular subtypes in advanced Asian colorectal cancer patients. Am J Cancer Res. 2024;14(2):796-808. doi:10.62347/FSSF9938
- Ding W, Yang P, Zhao X, et al. Unraveling EGFR-TKI resistance in lung cancer with high PD-L1 or TMB in EGFR-sensitive mutations. Respir Res. 2024;25(1):40. doi:10.1186/s12931-023-02656-3
- Peng H, Ying J, Zang J, et al. Specific Mutations in APC, with Prognostic Implications in Metastatic Colorectal Cancer. Cancer Res Treat. 2023;55(4):1270-1280. doi:10.4143/crt.2023.415
- Jiang Y, Mai G, Zhao X, et al. Molecular characterization and prognostic implications of KRAS mutations in pancreatic cancer patients: insights from multi-cohort analysis. NPJ Precis Oncol. 2025;9(1):299. Published 2025 Aug 22. doi:10.1038/s41698-025-01087-1
- Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330-337. Published 2012 Jul 18. doi:10.1038/nature11252
This project uses the MIT license-see LICENSE for details
For questions or suggestions, please contact:
- Mailbox：[ypm_yy@outlook.com]
- Github：[https://github.com/ypmjx123/Mypackage.git]