# Data preprocessing This function is to prepare data for the `ConNetGNN` function. ```r Preprocessing(data, parallel.cores = 1, verbose = TRUE) ``` ## Arguments - `data`: The input data should be a data frame or a matrix where the rows are genes and the columns are cells. The `seurat` object are also accepted. - `parallel.cores`: Number of processors to use when doing the calculations in parallel (default: `2`). If `parallel.cores=0`, then it will use all available core processors unless we set this argument with a smaller number. - `verbose`: Gives information about each step. Default: `TRUE`. ## Returns A list: - **orig_dara**: User-submitted raw data, rows are highly variable genes and columns are cells. - **cell_features**: Cell feature matrix. - **gene_features**: Gene feature matrix. - **ltmg_matrix**: Gene regulatory signal matrix for LTMG. - **cell_adj**: The adjacency matrix of the cell correlation network. - **gene_adj**: The adjacency matrix of the gene correlation network. ## Details Preprocessing The function is able to interface with the `seurat` framework. The process of `seurat` data processing refers to `Examples`. The input data should be containing hypervariable genes and log-transformed. Left-truncated mixed Gaussian (LTMG) modeling to calculate gene regulatory signal matrix. Positively correlated gene-gene and cell-cell are used as the initial gene correlation matrix and cell correlation matrix. ## Examples ```r # Load dependent packages. # require(coop) # Seurat data processing. # require(Seurat) # Load the PBMC dataset (Case data for seurat) # pbmc.data <- Read10X(data.dir = "../data/pbmc3k/filtered_gene_bc_matrices/hg19/") # Our recommended data filtering is that only genes expressed as non-zero in more than # 1% of cells, and cells expressed as non-zero in more than 1% of genes are kept. # In addition, users can also filter mitochondrial genes according to their own needs. # pbmc <- CreateSeuratObject(counts = pbmc.data, project = "case", # min.cells = 3, min.features = 200) # pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-") # pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5) # Normalizing the data. # pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize") # Identification of highly variable features. # pbmc <- FindVariableFeatures(pbmc, selection.method = 'vst', nfeatures = 2000) # Run Preprocessing. # Prep_data <- Preprocessing(pbmc) # Users can also directly input data # in data frame or matrix format # containing highly variable genes. data("Hv_exp") Hv_exp <- Hv_exp[,1:20] Hv_exp <- Hv_exp[which(rowSums(Hv_exp) > 0),] Prep_data <- Preprocessing(Hv_exp[1:10,]) ```

Preprocessing function