## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, results = "markup", message = FALSE, warning = FALSE) ## ----------------------------------------------------------------------------- library(Sconify) # Markers from the user-generated excel file marker.file <- system.file('extdata', 'markers.csv', package = "Sconify") markers <- ParseMarkers(marker.file) # How to convert your excel sheet into vector of static and functional markers markers # Get the particular markers to be used as knn and knn statistics input input.markers <- markers[[1]] funct.markers <- markers[[2]] # Selection of the k. See "Finding Ideal K" vignette k <- 30 # The built-in scone functions wand.nn <- Fnn(cell.df = wand.combined, input.markers = input.markers, k = k) # Cell identity is in rows, k-nearest neighbors are columns # List of 2 includes the cell identity of each nn, # and the euclidean distance between # itself and the cell of interest # Indices str(wand.nn[[1]]) wand.nn[[1]][1:20, 1:10] # Distance str(wand.nn[[2]]) wand.nn[[2]][1:20, 1:10] ## ----------------------------------------------------------------------------- wand.scone <- SconeValues(nn.matrix = wand.nn, cell.data = wand.combined, scone.markers = funct.markers, unstim = "basal") wand.scone ## ----------------------------------------------------------------------------- # Constructs KNN list, computes KNN density estimation wand.knn.list <- MakeKnnList(cell.data = wand.combined, nn.matrix = wand.nn) wand.knn.list[[8]] # Finds the KNN density estimation for each cell, ordered by column, in the # original data matrix wand.knn.density <- GetKnnDe(nn.matrix = wand.nn) str(wand.knn.density)