AWS的Lambda使用尝试
之前给博客尝试用了CloudFront，今天来试试Lambda。 先给虚拟机装个conda wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39\_4.12.0-Linux-x86\_64.sh -O conda_install.sh chmod +x conda_install.sh ./
对富集结果进行贝叶斯网络推断
安装补充包 conda activate clusterprofiler ~/dev/xray/xray -c ~/etc/xui2.json & wget -e “https_proxy=http://127.0.0.1:20809“ https://github.com/noriakis/CBNplot/archive/ref
生存数据计算C指数并进行比较
安装补充包 conda activate ggsurvplot conda install -c bioconda bioconductor-survcomp -y 计算C指数1234library(survcomp)C_index1 <- concordance.index(x=test_data$GS, surv.time=test_data$time, surv.event=test
蛋白互作网络PPI分析后进行hub基因提取
导出基因1234x <- readRDS('../../fig.1/C_ref_A_fiig.1_A/x.rds')x <- unique(c(x$RRA_up,x$RRA_down))x <- as.data.frame(x)write.table(x = x, file = 'input.txt', row.names = F, quo
R语言：数据集分割
安装补充包 conda activate ggsurvplot conda install -c conda-forge r-catools -y 划分训练集和验证集123456library("caTools")dat <- readRDS('../data/dat.rds')set.seed(123)split = sample.split(su
COX-Ridge：外部数据集验证
建模1234567891011121314library(glmnet)library(survival)df1 <- readRDS('../../fig.3/B_ref_A_fiig.3_C/new_df1.rds')y <- data.matrix(df1[9:10])x <- data.matrix(df1[1:8])Ridge <- glmne
Seurat绘制3D-umap
之前在Rstudio-server上安装了seurat，现在可以试试绘制3D-umap了 安装补充包 export R_LIBS_SITE=”” .libPaths(‘/home/rstudio/miniconda3/envs/r_4_1_3/lib/R/library’) install.packages(
timeROC绘图记录
多变量123456789101112131415161718192021222324252627282930313233343536373839library(survival)library(car)library(rms)library(pROC)library(timeROC)library(ggDCA)library(survivalROC)library(pec)require(ggsc