# #——getPackage eval = FALSE --------------------------------------------------- # 如果(!requireNamespace(“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“dcanr ") ## ---- eval = FALSE ------------------------------------------------------------ # BiocManager::安装(“DavisLaboratory / dcanr ") ## ---- 加载消息= FALSE ------------------------------------------------------ 库(dcanr) # #——回声= TRUE,消息= FALSE,警告= FALSE ---------------------------------- 图书馆(dcanr) dcMethods () ## ----------------------------------------------------------------------------- # 数据加载数据(sim102) #得到可用条件getConditionNames (sim102) #表达数据和条件的UME6混战simdata < - getSimData (sim102 cond.name = UME6,全= FALSE) emat < - simdata美元emat ume6_kd <——simdata条件打印美元(emat[1:5, 1:5])基因和406个样本头(ume6_kd) # 149 #注意:二进制编码与1和2的条件 ## ----------------------------------------------------------------------------- # 应用z分数与斯皮尔曼相关系数方法z_scores < - dcScore (emat, ume6_kd特区。方法= ' zscore ' cor.method =“枪兵”)打印(z_scores [1:5, 1:5 ]) ## ----------------------------------------------------------------------------- # 执行统计检验:自动选择z检验raw_p < - dct (z_scores、emat ume6_kd)打印(raw_p [1:5, 1:5 ]) ## ----------------------------------------------------------------------------- # 调整假定值(从dct不应该修改原始假定值)adj_p < - dcAdjust (f = p.adjust raw_p,方法=“罗斯福”)打印(adj_p [1:5, 1:5 ]) ## ---- 消息= FALSE,警告= FALSE, fig.wide = TRUE ------------------------------ 库(igraph) #得到微分网络dcnet < - dcNetwork (z_scores,Adj_p) plot(dcnet，顶点。标签= ")#转换为邻接矩阵adjmat < - as_adj (dcnet稀疏= FALSE)打印(adjmat[1:5, 1:5]) #皈依data.frame edgedf < - as_data_frame (dcnet, =‘边缘’)打印头(edgedf )) ## ---- sessionInfo,回声= FALSE -------------------------------------------------- sessionInfo ()