问题描述
我想知道是否可以为 const wsProxy = createProxyMiddleware(
'ws://target.domain',{
changeOrigin: true,ws:true,onopen(proxySocket) {
console.info("ON open!");
proxySocket.addListener("data",(data)=>{
console.info("on data ",data.toString()); // Here I can see data that is coming as response from Remote Server
})
},onProxyReq(proxyReq,req,res) {
console.info("ON onProxyReq!")
},onProxyReqWs(proxyReq,socket,options,head) {
console.info("ON onProxyReqWs!")
socket.addListener("data",(data)=>{
console.info("on data22 ",data.toString());
})
},onProxyRes(proxyRes,res) {
console.info("ON onProxyRes !")
}
}
);
const app = express();
app.use(wsProxy);
const server = app.listen(3000);
server.on('upgrade',function (req,head) {
console.info("upgrade");
wsProxy.upgrade(req,head);
});
函数内的每个集群绘制一个 facet_grid 图。我要绘制的两个特征由特征 1 和特征 2 定义，方面应该是我的对象内定义为 FeatureScatter 内级别的集群。
pbmc.big$seurat_clusters解决方法
值得一提的是，您使用的数据可以从 this link 下载并像这样创建：
library(Seurat)
library(magrittr)
pbmc.big <- Read10X(data.dir = "../data/pbmc3k/filtered_gene_bc_matrices/hg19/")
pbmc.big <- CreateSeuratObject(counts = pbmc.big)
pbmc.big$percent.mito = PercentageFeatureSet(pbmc.big,pattern="^MT-")
运行聚类：
pbmc.big = pbmc.big %>%
SCTransform() %>%
RunPCA() %>%
RunTSNE(dims=1:15) %>%
FindNeighbors(dims=1:15) %>%
FindClusters(res=0.1)
你可以这样：
g = FeatureScatter(object = pbmc.big,feature1 = "MALAT1",feature2 = "percent.mito",plot.cor = TRUE)
g + facet_wrap(~colors)
缺少相关性，如果需要，一种方法是提取变量并绘制图：
library(ggpubr)
data.frame(cluster = Idents(pbmc.big),MALAT1 = FetchData(pbmc.big,"MALAT1"),percent.mito = FetchData(pbmc.big,"percent.mito")) %>%
ggplot(aes(x=MALAT1,y= percent.mito,col = cluster)) +
geom_point(size=1) +
facet_wrap(~cluster)+
stat_cor(method = "pearson")
