chuiqin
commented
4 months ago 在Seurat 5.0版本的条件下，我测试了VISION，发现可以正常运行：
> library(irGSEA)
> library(tidyverse)
> library(SeuratData)
> data("pbmc3k.final")
> pbmc3k.final3 <- irGSEA.score(object = pbmc3k.final, assay = "RNA",
+ slot = "data",ncores = 4,
+ msigdb = T, species = "Homo sapiens", category = "H", geneid = "symbol",
+ method = c("VISION"), kcdf = 'Gaussian')
Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Updating matrix keys for DimReduc ‘pca’
Updating matrix keys for DimReduc ‘umap’
Warning: Assay RNA changing from Assay to Assay
Warning: Graph RNA_nn changing from Graph to Graph
Warning: Graph RNA_snn changing from Graph to Graph
Warning: DimReduc pca changing from DimReduc to DimReduc
Warning: DimReduc umap changing from DimReduc to DimReduc
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in RNA
Updating slots in RNA_nn
Setting default assay of RNA_nn to RNA
Updating slots in RNA_snn
Setting default assay of RNA_snn to RNA
Updating slots in pca
Updating slots in umap
Setting umap DimReduc to global
Setting assay used for NormalizeData.RNA to RNA
Setting assay used for FindVariableFeatures.RNA to RNA
Setting assay used for ScaleData.RNA to RNA
Setting assay used for RunPCA.RNA to RNA
Setting assay used for JackStraw.RNA.pca to RNA
No assay information could be found for ScoreJackStraw
Setting assay used for FindNeighbors.RNA.pca to RNA
No assay information could be found for FindClusters
Setting assay used for RunUMAP.RNA.pca to RNA
Validating object structure for Assay ‘RNA’
Validating object structure for Graph ‘RNA_nn’
Validating object structure for Graph ‘RNA_snn’
Validating object structure for DimReduc ‘pca’
Validating object structure for DimReduc ‘umap’
Object representation is consistent with the most current Seurat version
Error in irGSEA.score(object = pbmc3k.final, assay = "RNA", slot = "data", :
VISION only support slot (counts).
> pbmc3k.final3 <- irGSEA.score(object = pbmc3k.final, assay = "RNA",
+ slot = "counts",ncores = 4,
+ msigdb = T, species = "Homo sapiens", category = "H", geneid = "symbol",
+ method = c("VISION"), kcdf = 'Gaussian')
Validating object structure
Updating object slots
Ensuring keys are in the proper structure
Updating matrix keys for DimReduc ‘pca’
Updating matrix keys for DimReduc ‘umap’
Warning: Assay RNA changing from Assay to Assay
Warning: Graph RNA_nn changing from Graph to Graph
Warning: Graph RNA_snn changing from Graph to Graph
Warning: DimReduc pca changing from DimReduc to DimReduc
Warning: DimReduc umap changing from DimReduc to DimReduc
Ensuring keys are in the proper structure
Ensuring feature names don't have underscores or pipes
Updating slots in RNA
Updating slots in RNA_nn
Setting default assay of RNA_nn to RNA
Updating slots in RNA_snn
Setting default assay of RNA_snn to RNA
Updating slots in pca
Updating slots in umap
Setting umap DimReduc to global
Setting assay used for NormalizeData.RNA to RNA
Setting assay used for FindVariableFeatures.RNA to RNA
Setting assay used for ScaleData.RNA to RNA
Setting assay used for RunPCA.RNA to RNA
Setting assay used for JackStraw.RNA.pca to RNA
No assay information could be found for ScoreJackStraw
Setting assay used for FindNeighbors.RNA.pca to RNA
No assay information could be found for FindClusters
Setting assay used for RunUMAP.RNA.pca to RNA
Validating object structure for Assay ‘RNA’
Validating object structure for Graph ‘RNA_nn’
Validating object structure for Graph ‘RNA_snn’
Validating object structure for DimReduc ‘pca’
Validating object structure for DimReduc ‘umap’
Object representation is consistent with the most current Seurat version
Calculate VISION scores
Importing counts from obj[["RNA"]]@counts ...
Normalizing to counts per 10,000...
Importing Meta Data from obj@meta.data ...
Importing latent space from Embeddings(obj, "pca") using first 50 components
Using 13656/13714 genes detected in 0.10% of cells for signature analysis.
See the `sig_gene_threshold` input to change this behavior.
Adding Visualization: Seurat_pca
Adding Visualization: Seurat_umap
Beginning Analysis
Clustering cells...
Using latent space to cluster cells...
completed
Projecting data into 2 dimensions...
Evaluating signature scores on cells...
|===============================================================| 100%, Elapsed 00:00
Evaluating signature-gene importance...
|===============================================================| 100%, Elapsed 00:00
Creating 5 background signature groups with the following parameters:
sigSize sigBalance
1 21 1
2 59 1
3 88 1
4 129 1
5 170 1
signatures per group: 3000
Computing KNN Cell Graph in the Latent Space...
Evaluating local consistency of signatures in latent space...
|===============================================================| 100%, Elapsed 00:00
|===============================================================| 100%, Elapsed 00:07
|===============================================================| 100%, Elapsed 00:05
|===============================================================| 100%, Elapsed 00:00
Clustering signatures...
fitting ...
|==============================================================================| 100%
Computing differential signature tests...
|===============================================================| 100%, Elapsed 00:00
|===============================================================| 100%, Elapsed 00:01
Computing correlations between signatures and latent space components...
|===============================================================| 100%, Elapsed 00:01
Analysis Complete!
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Finish calculate VISION scores
> packageVersion(pkg = "Seurat")
[1] ‘5.0.3’
> packageVersion(pkg = "VISION")
[1] ‘3.0.1’
> packageVersion(pkg = "irGSEA")
[1] ‘3.2.5’你是否可以运行这段代码，判断一下是否正确安装了VISION以及irGSEA
library(Seurat)
library(irGSEA)
library(tidyverse)
library(Seurat)
library(SeuratData)
data("pbmc3k.final")
pbmc3k.final3 <- irGSEA.score(object = pbmc3k.final, assay = "RNA",
slot = "counts",ncores = 4,
msigdb = T, species = "Homo sapiens", category = "H", geneid = "symbol",
method = c("VISION"), kcdf = 'Gaussian')
Pigeongeon
rm(list = ls())# 删除所有已定义对象 getwd() setwd("/mnt/data/home/tycloud")
library(Seurat) library(SeuratData) library(RcppML) library(irGSEA) library(doMC) registerDoMC(cores = 20)
Sys.setenv(RETICULATE_PYTHON = "/mnt/data/tool/miniconda3/envs/irGSEA/bin/python") reticulate::py_config()
读取gsva.py文件
gsva_path <- "/mnt/data/home/tycloud/R/library/4.3.3/irGSEA/python/gsva.py" gsva_content <- readLines(gsva_path)
定位到保存结果的部分并修改
modified_content <- gsub("acts.to_csv\('./matrix.py.result.csv'\)", "pd.DataFrame(acts[0]).to_csv('./matrix.py.result.csv')", gsva_content)
保存
writeLines(modified_content, gsva_path)
load("Scissor/HCC_endothelium.Rdata") # 加载保存的Seurat对象
【检查Seurat对象版本】【HCC_endothelium 是 Seurat 版本 5.0.1，运行失败，而我的其他Seurat对象是3.2.1，都成功运行了VISION】
check_seurat_version <- function(object_name) { object <- get(object_name) if (inherits(object, "Seurat")) { version <- object@version cat(paste0(object_name, " 是 Seurat 版本 ", version, "\n")) } else { cat(paste0(object_name, " 不是 Seurat 对象\n")) } }
检查每个对象
check_seurat_version("HCC_endothelium")
HCC_endothelium 是 Seurat 版本 5.0.1
Validating object structure Updating object slots Ensuring keys are in the proper structure Updating matrix keys for DimReduc 'umap' Updating matrix keys for DimReduc 'pca' Updating matrix keys for DimReduc 'harmony' Warning: Assay RNA changing from Assay to Assay Warning: DimReduc umap changing from DimReduc to DimReduc Warning: Adding a dimensional reduction (umap) without the associated assay being present Ensuring keys are in the proper structure Warning: Adding a dimensional reduction (umap) without the associated assay being present Ensuring feature names don't have underscores or pipes Warning: Adding a dimensional reduction (umap) without the associated assay being present Updating slots in RNA Updating slots in RNA_nn Setting default assay of RNA_nn to RNA Updating slots in RNA_snn Setting default assay of RNA_snn to RNA Updating slots in umap Setting umap DimReduc to global Warning: Adding a dimensional reduction (umap) without the associated assay being present Updating slots in pca Updating slots in harmony No assay information could be found for FindIntegrationAnchors Setting assay used for NormalizeData.RNA to RNA Setting assay used for FindVariableFeatures.RNA to RNA Setting assay used for ScaleData.RNA to RNA Setting assay used for RunPCA.RNA to RNA Setting assay used for Seurat..ProjectDim.RNA.harmony to RNA Setting assay used for FindNeighbors.RNA.pca to RNA Validating object structure for Assay 'RNA' Validating object structure for Graph 'RNA_nn' Validating object structure for Graph 'RNA_snn' Validating object structure for DimReduc 'umap' Validating object structure for DimReduc 'pca' Validating object structure for DimReduc 'harmony' Object representation is consistent with the most current Seurat version Calculate VISION scores Importing counts from obj[["RNA"]]@counts ... Normalizing to counts per 10,000... Importing Meta Data from obj@meta.data ... Importing latent space from Embeddings(obj, "pca") using first 50 components
Using 17514/66590 genes detected in 0.10% of cells for signature analysis. See the
sig_gene_thresholdinput to change this behavior.Dropping 'CellName' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision Dropping 'PatientID' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision Dropping 'Sub_Cluster' from meta data as it is of type 'character' and has more than 20 unique values. If you want to include this meta data variable, convert it to a factor before providing the data frame to Vision Adding Visualization: Seurat_umap Adding Visualization: Seurat_pca Adding Visualization: Seurat_harmony Beginning Analysis
Clustering cells... Using latent space to cluster cells... completed
Projecting data into 2 dimensions...
Evaluating signature scores on cells...
|=========================================================================================================================| 100%, Elapsed 00:01 Evaluating signature-gene importance...
|=========================================================================================================================| 100%, Elapsed 00:31 Creating 5 background signature groups with the following parameters: sigSize sigBalance 1 37 1 2 92 1 3 144 1 4 175 1 5 192 1 signatures per group: 3000 Computing KNN Cell Graph in the Latent Space...
Evaluating local consistency of signatures in latent space...
|=========================================================================================================================| 100%, Elapsed 00:02 |=========================================================================================================================| 100%, Elapsed 05:36 |=========================================================================================================================| 100%, Elapsed 06:04 |=============================================================================================================== | 89%, ETA 00:01 Error: $ operator is invalid for atomic vectors