Perform basic 'pagoda2' processing, i.e. adjust variance, calculate pca reduction, make knn graph, identify clusters with multilevel, and generate largeVis and tSNE embeddings.
basicP2proc( cd, n.cores = 1, n.odgenes = 3000, nPcs = 100, k = 30, perplexity = 50, log.scale = TRUE, trim = 10, keep.genes = NULL, min.cells.per.gene = 0, min.transcripts.per.cell = 100, get.largevis = TRUE, get.tsne = TRUE, make.geneknn = TRUE )
cd
: count matrix whereby rows are genes, columns are cells.n.cores
: numeric Number of cores to use (default=1)n.odgenes
: numeric Number of top overdispersed genes to use (dfault=3e3)nPcs
: numeric Number of PCs to use (default=100)k
: numeric Default number of neighbors to use in kNN graph (default=30)perplexity
: numeric Perplexity to use in generating tSNE and largeVis embeddings (default=50)log.scale
: boolean Whether to use log scale normalization (default=TRUE)trim
: numeric Number of cells to trim in winsorization (default=10)keep.genes
: optional set of genes to keep from being filtered out (even at low counts) (default=NULL)min.cells.per.gene
: numeric Minimal number of cells required for gene to be kept (unless listed in keep.genes) (default=0)min.transcripts.per.cell
: numeric Minimumal number of molecules/reads for a cell to be admitted (default=100)get.largevis
: boolean Whether to caluclate largeVis embedding (default=TRUE)get.tsne
: boolean Whether to calculate tSNE embedding (default=TRUE)make.geneknn
: boolean Whether pre-calculate gene kNN (for gene search) (default=TRUE)a new 'Pagoda2' object