ISS_cluster_seruat: "ISS_cluster_seruat" Cluster ISS data by SEURAT.

In mashranga/MolDia: Single cell In-Situ sequencing (ISS) data analysis

Description

"ISS_cluster_seruat" Cluster ISS data by SEURAT.

Usage

ISS_cluster_seruat(data, pc = NULL, cluster_id = NULL,
  resolution = 0.3, algorithm = 1, DEGmethod = "seurat",
  k.param = 30)

Arguments

data	Input data in class MolDiaISS. Output of readISS.
pc	Desired percent of variance (0 to 1) to be explained by PCA. Default in NULL (All PC will use).
cluster_id	Re-cluster clustreded data. Numeric input. Default is NULL.
resolution	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities. Default is 0.3.
algorithm	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm). Default is 1.
DEGmethod	Methods to find DE genes.
k.param	Defines k for the k-nearest neighbor algorithm

Examples

## Reading data
data_3 <- readISS(file = system.file("extdata", "Hypocampus_left.csv", package="MolDia"),
                  cellid = "CellId", centX = "centroid_x", centY = "centroid_y")

## Arrange marker gene
data(marker_gene)
mark_gene <- list(genr = marker_gene$genr, neuron = c(marker_gene$genr_neuro,
                                                      marker_gene$genr_neuro_pyra1,
                                                      marker_gene$genr_neuro_pyra2,
                                                      marker_gene$genr_neuro_inter1,
                                                      marker_gene$genr_neuro_inter2,
                                                      marker_gene$genr_neuro_inter3,
                                                      marker_gene$genr_neuro_inter4,
                                                      marker_gene$genr_neuro_inter5,
                                                      marker_gene$genr_neuro_inter6),
                                            nonneuron = marker_gene$genr_nonneuro)

## Barplot of Neuronal marker gene and extract those cells only
neuron_group <- ISS_barplot(data = data_3, gene = mark_gene, gene.target = 2,
                            at.least.gene = 8, gene.show = 2)
## Data preprocessing
neuron_group <- ISS_preprocess(data = neuron_group, normalization.method = "LogNormalize",
                               do.scale = TRUE, do.center = TRUE)

## Cluster data based on SEURAT pipeline
neuron_group_clust  <- ISS_cluster_seruat (data = neuron_group, pc = 0.9, resolution = 0.4)

## Re-cluster specific cluster
# re_clust  <- ISS_cluster_seruat (data = neuron_group_clust, pc = 0.9,
#                                  cluster_id = 0, resolution = 0.5)

mashranga/MolDia documentation built on May 26, 2019, 9:36 a.m.