ISS_cluster: Cluster ISS data based on different methods.

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

Description

Cluster ISS data by different methods used in SEURAT, MONOCLE, BackSPIN.

Usage

ISS_cluster(data, method = "seurat", DEGmethod = "seurat", pc = 1,
  cluster_id = NULL, resolution = 0.3, algorithm = 1,
  do.norm = TRUE, do.scale = TRUE, k.param = 30)

Arguments

data	Input data in class MolDiaISS. Output of readISS.
method	Method of clustering. SEURAT, backspin, monocle.
DEGmethod	Methods to find DE genes.NULL means use all genes.
pc	Desired percent of variance (0 to 1) to be explained by PCA. Default is 1 (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.
do.norm	Do normalization
do.scale	Do scalling
k.param	Defines k for the k-nearest neighbor algorithm

Examples

## Reading data
left_hypo <- 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 = left_hypo, gene = mark_gene, gene.target = 2,
                            at.least.gene = 2, 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 (data = neuron_group, method = "seurat",
                                    pc = 0.9, resolution = 0.3)

## Re-cluster specific cluster
re_clust  <- ISS_cluster (data = neuron_group_clust, method = "seurat",
                                 pc = 0.9, cluster_id = 3, resolution = 1)

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