leiden_resolution = "auto", #可以手动调参数 num_threads = 4 #4线程工作,加快速度 ) 接下来使用曾老师的方法去检查拷贝数变异的情况 6、读取数据加载R包 rm(list = ls()) options(stringsAsFactiors = F) library(phylogram) library(gridExtra) library(grid) require(dendextend) require(ggthemes) lib...
- k_obs_groups:默认1,肿瘤细胞聚类数目 - num_threads:线程数- denoise:默认FALSE,对CNV矩阵进行降噪 4.剖析inferCNV原理 以上就是整个inferCNV的分析流程,两个函数就搞定。inferCNV包的集成程度非常高,傻瓜式的使用对用户非常友好,但是函数run到底对我们的数据做了哪些处理?来吧,逐步拆解函数run。 - Removing ...
cluster_by_groups=T, # 根据细胞类型对肿瘤细胞执行单独的聚类,如 cell annotations 文件中定义的那样 denoise=T, # 去噪处理 num_threads=10, # 设置线程数, 多线程运行,加快计算速度 HMM=T) # 是否基于 HMM 预测 CNV,选择 F 会加快运算速度 需要注意的是:cutoff 为每个基因在参考细胞中的最小平均 read ...
final_scale_limits =NULL, final_center_val =NULL, debug =FALSE, num_threads =4, plot_steps =FALSE, resume_mode =TRUE, png_res =300, plot_probabilities =TRUE, save_rds =TRUE, save_final_rds =TRUE, diagnostics =FALSE, remove_genes_at_chr_ends =FALSE, prune_outliers =FALSE, mask_non...
num_threads =4, plot_steps =FALSE, resume_mode =TRUE, png_res =300, plot_probabilities =TRUE, save_rds =TRUE, save_final_rds =TRUE, diagnostics =FALSE, remove_genes_at_chr_ends =FALSE, prune_outliers =FALSE, mask_nonDE_genes =FALSE, mask_nonDE_pval =0.05, ...
10X转录组设置为0.1out_dir="result/ep_infercnv_output",# 结果输出目录cluster_by_groups=F,# clusterhclust_method="ward.D2",analysis_mode="subclusters",denoise=TRUE,HMM=TRUE,plot_steps=F,#关闭过程中的画图过程速度会增快output_format="pdf",num_threads=25#线程数,多线程运算建议结合内存大小合理选...
no_prelim_plot = T, HMM=FALSE, sd_amplifier = 1.3, analysis_mode = "samples", # analysis_mode ='subclusters' # 区分肿瘤亚型,慢 num_threads=3, png_res = 200) 解决办法:加上参数 write_expr_matrix = T
num_threads=2, BayesMaxPNormal=0 ) ``` Basic ouput from running inferCNV. ```{r, echo=FALSE} knitr::include_graphics("../example_output/infercnv.png") ``` HMM preditions ```{r, echo=FALSE} knitr::include_graphics("../example_output/infercnv.13_HMM_predHMMi6.hmm_mode-...
_res = png_res, useRaster = useRaster) 1: infercnv::run(infercnv_obj, cutoff = 0.1, out_dir = out_dir, cluster_by_groups = FALSE, k_obs_groups = 2, plot_steps = FALSE, denoise = TRUE, HMM = TRUE, analysis_mode = "subclusters", tumor_subcluster_pval = 0.05, num_threads =...
noise_filter=NA,sd_amplifier=1.5,noise_logistic=FALSE,outlier_method_bound="average_bound",outlier_lower_bound=NA,outlier_upper_bound=NA,final_scale_limits=NULL,final_center_val=NULL,debug=FALSE,num_threads=4,plot_steps=FALSE,resume_mode=TRUE,png_res=300,plot_probabilities=TRUE,save_rds=TRUE,...