# tSNE my.obj <- run.pc.tsne( my.obj, dims = 1 : 10)
#Flowjo tutorial video Pc
# This is optional and might not be good in some cases # 683 #my.obj <- (my.obj, dims = 1:10,top.pos = 20, top.neg = 20) # (optional) # 211 # second round PC #my.obj <- run.pca(my.obj, method = "gene.model", gene.list = "main") # 2 round PCA (optional) # This is to find top genes in the first 10 PCs and re-run PCA for better clustering. Example for batch alignment using iba function: # my.obj <- iba(my.obj,dims = 1:30, k = 10,ba.method = "CPCA", method = "gene.model", gene.list = # run PCA in case no batch alignment is necessary my.obj <- run.pca( my.obj, method = "gene.model ", gene.list = my.obj gene.model, data.type = "main ") # If you run PCA (run.pca) there would be no batch alignment but if you run CPCA (using iba function) this would perform batch alignment and PCA after batch alignment. # Spatial # Spatial data dimentions (rows,columns):0,0 # iCellR object # # scATAC-seq # ATAC raw data dimensions (rows,columns):0,0 ATAC main data dimensions (rows,columns):0,0 ATAC columns names. Imputed data dimensions (rows,columns):0,0 # scVDJ-seq # VDJ data dimentions (rows,columns):0,0 # CITE-seq # ADT raw data dimensions (rows,columns):0,0 ADT main data dimensions (rows,columns):0,0 ADT columns names. # QC stats performed:FALSE, PCA performed:FALSE Clustering performed:FALSE, Number of clusters:0 tSNE performed:FALSE, UMAP performed:FALSE, DiffMap performed:FALSE Main data dimensions (rows,columns): 0,0 Normalization factors. Columns names: WT_AAACATACAACCAC.1,WT_AAACATTGAGCTAC.1,WT_AAACATTGATCAGC.1. SeqGeq DE tutorialįor citing iCellR use this PMID: 34353854 If you are using FlowJo or SeqGeq, they have made plugins for iCellR and other single cell tools: (list of all plugins) and (iCellR plugin).
#Flowjo tutorial video manual
#Flowjo tutorial video code
Tutorial: example 2 code and results (based on CPCA batch alignment and KNetL map ).Tutorial: example 1 code and results (based on KNetL map ).More databases added for cell type prediction (ImmGen and MCA). News (April 2020): see our imputation/coverage correction ( CC) and batch alignment (CCCA and CPCA) methods. KNetL map is capable of zooming and shows a lot more details compared to tSNE and UMAP. News (May 2020): see our dimensionality reduction called KNetL map (pronounced like "nettle"). News (July 2020): See iCellR version 1.5.5 with new cell cycle analysis for G0, G1S, G2M, M, G1M and S phase, Pseudotime Abstract KNetL map (PAK map) and gene-gene correlations. tirosh, mean, sum, gsva, ssgsea, zscore and plage).
Use the i.score function for scoring (scoring cells based on gene signatures) methods (i.e. News (April 2021): Use iCellR version 1.6.4 for scATAC-seq and Spatial Transcriptomics (ST). ICellR is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).
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