GSEAlens 中的 Lens（透镜）象征着这个包如同一个放大镜，帮助研究者深入探索 GSEA 富集分析中的关键通路。
GSEAlens 提供了一个基于 Web 界面的交互式平台，用于展示通路的介绍和描述，并集成了 AI 辅助的通路富集结果导出功能。通过封装工作流程和标准化输入格式，这个 R 包简化了 GSEA 富集分析的结果查看和探索过程。
使用airway包作为示例运行，这个R包当中包含两组每组4个重复
library(GSEAlens)
# 先检查是否已安装 airway
if (!requireNamespace("airway", quietly = TRUE)) {
# 如果未安装，先检查并安装 BiocManager
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
# 通过 BiocManager 安装 airway
BiocManager::install("airway")
}
library("airway")
data(airway)
expression_data <- airway首先参考标准流程，使用 edgeR 包组装 DGEList 对象，设计无截距矩阵并重命名行名， 之后制作用于拟合的比对，过滤低表达基因，保留蛋白质编码 RNA，使用 symbol 作为行名。
注意：GSEAlens 基于无截距设计的 limma-voom 流程 (~0+group)，以确保列名直接对应组别名称。
library(edgeR)
library(limma)
group_level <- airway@colData@listData[["dex"]]
design <- model.matrix(~0+group_level)
colnames(design) <- levels(group_level)
compare_end <- combn(levels(group_level), 2, simplify = FALSE)
contrast_strings <- sapply(compare_end, function(x) paste(x[2], x[1], sep = " - "))
contrast_matrix <- makeContrasts(contrasts = contrast_strings, levels = design)
genes_df <- data.frame(
gene_id = airway@rowRanges@elementMetadata@listData$gene_id,
symbol = airway@rowRanges@elementMetadata@listData$symbol,
gene_biotype = airway@rowRanges@elementMetadata@listData$gene_biotype
)
genes_df$Length <- airway@rowRanges@elementMetadata@listData$gene_seq_end -
airway@rowRanges@elementMetadata@listData$gene_seq_start + 1
gsea_limma_voom_data <- edgeR::DGEList(
counts = assay(airway, "counts"),
genes = genes_df,
norm.factors = NULL,
group = group_level,
remove.zeros = T
)## Removing 30208 rows with all zero countstotal_counts <- edgeR::cpm(gsea_limma_voom_data) |> rowSums()
dup_symbols <- gsea_limma_voom_data$genes$symbol[duplicated(gsea_limma_voom_data$genes$symbol)]
keep <- rep(TRUE, nrow(gsea_limma_voom_data))
for (gene in dup_symbols) {
idx <- which(gsea_limma_voom_data$genes$symbol == gene)
best_idx <- idx[which.max(total_counts[idx])]
remove_idx <- idx[idx != best_idx]
keep[remove_idx] <- FALSE
}
gsea_limma_voom_data <- gsea_limma_voom_data[keep, ]
rownames(gsea_limma_voom_data) <- gsea_limma_voom_data$genes$symbol
keep_biotype <- gsea_limma_voom_data$genes$gene_biotype == "protein_coding"
gsea_limma_voom_data <- gsea_limma_voom_data[keep_biotype,]
gsea_limma_voom_data <- edgeR::normLibSizes(gsea_limma_voom_data, method = "TMM")
isexpr <- rowSums(edgeR::cpm(gsea_limma_voom_data) > 1) >= 3
gsea_limma_voom_data <- gsea_limma_voom_data[isexpr,]之后进行limma-voom拟合，获取fit对象
参考DESeq2包的说明文档，进行数据清洗与处理。 首先去除非编码RNA
library("DESeq2")
dds_se <- DESeqDataSet(expression_data, design = ~ cell + dex)
gene_biotypes <- rowData(dds_se)$gene_biotype
keep_protein_coding <- gene_biotypes == "protein_coding"
dds_se <- dds_se[keep_protein_coding, ]
dds_se## class: DESeqDataSet
## dim: 22810 8
## metadata(2): '' version
## assays(1): counts
## rownames(22810): ENSG00000000003 ENSG00000000005 ... ENSG00000273482
## ENSG00000273490
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample之后去除低表达基因，使用symbol作为行名
smallestGroupSize <- 3
keep <- rowSums(counts(dds_se) >= 10) >= smallestGroupSize
dds_se <- dds_se[keep,]
dds_se## class: DESeqDataSet
## dim: 13256 8
## metadata(2): '' version
## assays(1): counts
## rownames(13256): ENSG00000000003 ENSG00000000419 ... ENSG00000272047
## ENSG00000272325
## rowData names(10): gene_id gene_name ... seq_coord_system symbol
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSamplerownames(dds_se) <- dds_se@rowRanges@elementMetadata@listData[["gene_name"]]
total_counts <- rowSums(SummarizedExperiment::assay(dds_se))
keep <- rep(TRUE, nrow(dds_se))
dup_genes <- rownames(dds_se)[duplicated(rownames(dds_se))]
for (gene in dup_genes) {
idx <- which(rownames(dds_se) == gene)
best_idx <- idx[which.max(total_counts[idx])]
remove_idx <- idx[idx != best_idx]
keep[remove_idx] <- FALSE
}
dds_se <- dds_se[keep, ]运行DESeq2处理
## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing过滤低表达基因，保留仅蛋白质编码RNA，并将矩阵行名修订为symbol
DDS_rawdata <- expression_data
gene_biotypes_epd <- rowData(DDS_rawdata)$gene_biotype
keep_protein_coding_epd <- gene_biotypes == "protein_coding"
DDS_rawdata <- DDS_rawdata[keep_protein_coding, ]
smallestGroupSize <- 3
keep_epd <- rowSums(assay(DDS_rawdata, "counts") >= 10) >= smallestGroupSize
DDS_rawdata <- DDS_rawdata[keep_epd,]
rownames(DDS_rawdata) <- DDS_rawdata@rowRanges@elementMetadata@listData[["gene_name"]]
total_counts <- rowSums(SummarizedExperiment::assay(DDS_rawdata))
keep_name <- rep(TRUE, nrow(DDS_rawdata))
dup_genes <- rownames(DDS_rawdata)[duplicated(rownames(DDS_rawdata))]
for (gene in dup_genes) {
idx <- which(rownames(DDS_rawdata) == gene) # 该基因的所有行索引
best_idx <- idx[which.max(total_counts[idx])] # 表达量最高的行
remove_idx <- idx[idx != best_idx] # 要剔除的行（同一基因名下非最高表达）
keep_name[remove_idx] <- FALSE
}
DDS_rawdata <- DDS_rawdata[keep_name, ]矩阵信息获取
cts <- assay(DDS_rawdata,"counts")
coldata <- DDS_rawdata@colData %>% as.data.frame.array()
coldata <- coldata[,c("cell","dex")]
coldata$cell <- factor(coldata$cell)
coldata$dex <- factor(coldata$dex)组装为DDS对象并进行处理
## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing使用build_gsea_pathways函数构建用于GSEA富集分析的基因集对象
## [build_gsea_pathways] Species: Homo sapiens (Human)##
## Selected 5 collection(s). Batch Tag: [Mix5_HS_C5_GO_BP]##
## [build_gsea_pathways] Done! Built 16333 pathways x 19676 genes for Homo sapiens (Human)通过setup_gsea_env函数，组装用于计算分析的GSEAEnv对象，不同的终端使用同一个函数，仅纳入数据不同，对于limma-voom流程，由于fit对象当中不包含原始的基因读数，因此必须额外纳入过滤后用于生成fit对象的DGEList，本例中为多步过滤的gsea_limma_voom_data。
limma-voom 流程对象纳入分析。
gseadata_limmavoom <- GSEAlens::setup_gsea_env(fit = fit,pathway_obj = gsea_pathwaysets,expr_data = gsea_limma_voom_data)##
## Starting GSEAlens engine...## Detected input type: [Limma-Voom]## GseaEnv object built successfully!## Contains 1 contrast groups## Contains 16333 pathwaysDESeq2 的 SummarizedExperiment 对象纳入分析。
##
## Starting GSEAlens engine...## Detected input type: [DESeq2]## [DESeq2] target_factor not specified, automatically inferred as: 'dex'## GseaEnv object built successfully!## Contains 1 contrast groups## Contains 16333 pathwaysDESeq2 的 Count matrix 流程的对象纳入分析。
##
## Starting GSEAlens engine...## Detected input type: [DESeq2]## [DESeq2] target_factor not specified, automatically inferred as: 'dex'## GseaEnv object built successfully!## Contains 1 contrast groups## Contains 16333 pathways所有对象均使用batch_calc_gsea这个函数处理，没有任何区别。
并行计算提示：可根据电脑性能调整 workers 选项，设置计算使用的核心数量。 比对数量越多，建议设置越高的核心数以提升计算效率。
# limma-voom 流程
gsea_res_limmavoom <- GSEAlens::batch_calc_gsea(gseadata_limmavoom,
custom_series_name = "limmavoom_data",
workers = 4, # 根据比对数量和电脑性能调整
force = TRUE)
# DESeq2 SummarizedExperiment 流程
gsea_res_se <- GSEAlens::batch_calc_gsea(gseadata_se,
custom_series_name = "dds_se_data",
workers = 4,
force = TRUE)
# DESeq2 Count matrix 流程
gsea_res_dds <- GSEAlens::batch_calc_gsea(gseadata_dds,
custom_series_name = "dds_data",
workers = 4,
force = TRUE)运行batch_calc_gsea之后，在对应目录下会生成一个RDS文件，读入该文件，或者使用import_gsea_capsule读取，import_gsea_capsule函数可以在读取的时候，自动将相关文件整理到使用的RMD文件或者R脚本的文件夹中，并且给出数据的检视情况。
gsea_res <- import_gsea_capsule("/path/to/your/files/")
# 或直接读取 RDS 文件
# gsea_res <- readRDS("/path/of/your/file/")数据载入后，就可以使用launch_gsea_app进行交互式查看
setup_gsea_env 函数返回的 GseaEnv 对象包含以下组件： | 组件 | 说明 | |——|——| | backend_info | 后端类型信息（limma-voom 或 DESeq2） | | contrast_registry | 对比组注册表，包含所有成对比较信息 | | de_store | 差异分析结果存储 | | expr_bundle | 表达数据封装（原始计数、标准化矩阵、样本元数据） | | geneset | 基因集信息（TERM2GENE、元数据字典、物种） |
batch_calc_gsea 函数返回的 GseaRes 对象包含以下组件： | 组件 | 说明 | |——|——| | metadata | 计算元数据（运行时间、使用的核心数、参数设置） | | backend_info | 后端类型信息 | | contrast_registry | 对比组注册表 | | de_store | 差异分析结果存储 | | expr_bundle | 表达数据封装 | | geneset_info | 基因集信息 | | results | GSEA 结果列表，每个对比组对应一个条目 |
extract_gsea_task 函数返回的 GseaTask 对象用于单对比分析：
| 组件 | 说明 |
|---|---|
gsea_res |
GSEA result 对象 |
meta |
元信息（对比组信息、基因集名称、表达数据） |
## R version 4.6.0 RC (2026-04-17 r89917)
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## attached base packages:
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## other attached packages:
## [1] DESeq2_1.52.0 edgeR_4.10.0
## [3] limma_3.68.2 airway_1.32.0
## [5] SummarizedExperiment_1.42.0 Biobase_2.72.0
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