--- title: "Secreted protein signaling activity for distinct cell states from scRNA-Seq data" output: rmarkdown::html_vignette description: > Secreted protein signaling activity for distinct cell states from scRNA-Seq data. vignette: > %\VignetteIndexEntry{Secreted protein signaling activity for distinct cell states from scRNA-Seq data} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- --- output: github_document --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` This tutorial demonstrates how to infer secreted protein signaling activity in various cell states. For this demonstration, we employ an ovarian cancer scRNA-Seq study. Using the SecAct framework, we will estimate the signaling activity of secreted proteins for different CD4+ T cell states. ## Load scRNA-Seq data The scRNA-Seq data of CD4+ T cells is stored in a Seurat object. Three cell states are annotated in this dataset, i.e., naive, central memory, and helper 1-like. ``` r library(SecAct) library(Seurat) # load scRNA-Seq data dataPath <- file.path(system.file(package="SecAct"), "extdata/") Seurat_obj <- readRDS(paste0(dataPath,"OV_scRNAseq_CD4.rds")) # cell count table(Seurat_obj@meta.data[,"Annotation"]) ## CD4_central_memory CD4_naive CD4_Th1_like ## 402 28 358 # show UMAP plot Seurat::DimPlot(Seurat_obj, reduction = "umap", group.by = "Annotation") ```

## Infer secreted protein activity After loading scRNA-Seq data data, user can input `Seurat_obj` into `SecAct.activity.inference` to infer the signaling activities of secreted proteins for various cell states. Also, you need to claim which column in the meta matrix of Seurat object has cell state information. In this case, the column name is "Annotation" and thus assign `cellType_meta = "Annotation"`. User can find the SecAct results in `Seurat_obj @misc $SecAct_output $SecretedProteinActivity`. ``` r # infer activity; ~2 mins Seurat_obj <- SecAct.activity.inference.scRNAseq(Seurat_obj, cellType_meta="Annotation") # SecretedProteinActivity$zscore stores activity act <- Seurat_obj @misc $SecAct_output $SecretedProteinActivity$zscore # show activity head(act) ``` The inferred protein activity is a relative measure. A positive value indicates high activity in the corresponding cell states, while a negative value signifies low activity. ## Visualize activity User can visualize any secreted proteins of interest using `SecAct.heatmap.plot`. Here, we select the top active secreted proteins (SPs) for each cell state. ``` r # select top secreted proteins n <- 10 SPs.1 <- names(sort(act[,1],decreasing=T))[1:n] SPs.2 <- names(sort(act[,2],decreasing=T))[1:n] SPs.3 <- names(sort(act[,3],decreasing=T))[1:n] SPs <- c(SPs.1, SPs.2, SPs.3) # user can assign any secreted proteins to SPs # subset act matrix fg.mat <- act[SPs,] # visualize in heatmap SecAct.heatmap.plot(fg.mat) ```