## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(mc.cores=2) ## ----echo=FALSE, eval=TRUE, results='hide'------------------------------------ library(SingleCellSignalR) data(example_dataset, package = "SingleCellSignalR") data = example_dataset genes = data$genes rownames(data) = genes data = data[,-1] ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # clust <- clustering(data = data, n.cluster = 4, n = 10, method = "simlr",write = FALSE,pdf=FALSE) ## ----echo=FALSE, eval=TRUE---------------------------------------------------- clust <- clustering(data = data,n.cluster = 4, n = 10, method = "kmeans",write = FALSE,pdf=FALSE) ## ----eval=TRUE, results='hide'------------------------------------------------ clust.ana <- cluster_analysis(data = data, genes = rownames(data), cluster = clust$cluster, write = FALSE) ## ----eval=TRUE---------------------------------------------------------------- signal <- cell_signaling(data = data, genes = rownames(data), cluster = clust$cluster, write = FALSE) ## ----eval=TRUE---------------------------------------------------------------- inter.net <- inter_network(data = data, signal = signal, genes = genes, cluster = clust$cluster, write = FALSE) ## ----eval=TRUE---------------------------------------------------------------- visualize_interactions(signal = signal) ## ----eval=TRUE---------------------------------------------------------------- visualize_interactions(signal = signal,show.in=c(1,4)) ## ----echo=FALSE, eval=TRUE, results='hide'------------------------------------ data(example_dataset, package = "SingleCellSignalR") data = example_dataset genes = data$genes rownames(data) = genes data = data[,-1] ## ----eval=TRUE, results='hide'------------------------------------------------ class = cell_classifier(data=data, genes=rownames(data), markers = markers(c("immune")), tsne=clust$`t-SNE`,plot.details=TRUE,write = FALSE) ## ----eval=TRUE, results='hide'------------------------------------------------ # Define the cluster vector and the cluster names cluster <- class$cluster c.names <- class$c.names # Remove undefined cells data <- data[,cluster!=(max(cluster))] tsne <- clust$`t-SNE`[cluster!=(max(cluster)),] c.names <- c.names[-max(cluster)] cluster <- cluster[cluster!=(max(cluster))] ## ----eval=TRUE---------------------------------------------------------------- clust.ana <- cluster_analysis(data = data, genes = rownames(data), cluster = cluster, c.names = c.names, write = FALSE) ## ----eval=TRUE---------------------------------------------------------------- signal <- cell_signaling(data = data, genes = genes, cluster = cluster, c.names = c.names, write = FALSE) inter.net <- inter_network(data = data, signal = signal, genes = genes, cluster = cluster, write = FALSE) ## ----eval=TRUE---------------------------------------------------------------- signal[[6]] ## ----eval=FALSE--------------------------------------------------------------- # intra = intra_network(goi = "ASGR1",data = data,genes = rownames(data),cluster = cluster, coi = "Macrophages", c.names = c.names, signal = signal,write=FALSE) ## ----eval=TRUE---------------------------------------------------------------- visualize_interactions(signal) ## ----eval=TRUE---------------------------------------------------------------- visualize_interactions(signal, show.in=c(1,6))