ISS_clustMarker: Find Cluster marker of ISS data.
In mashranga/MolDia: Single cell In-Situ sequencing (ISS) data analysis

Description Usage Arguments Value Examples

Find Cluster marker of ISS data and plot significant gene cluster by barplot and heatmap.

1 2	ISS_clustMarker(data, topgene = 5, test.use = "bimod", marker.sig = 0.005, only.pos = TRUE, main = "")

`data`	Input data in class MolDiaISS. Output of readISS.
`topgene`	Desired number of top gene ineach cluster to show in summary result.
`test.use`	Denotes which test to use. Seurat currently implements "bimod" (likelihood-ratio test for single cell gene expression, McDavid et al., Bioinformatics, 2013, default), "roc" (standard AUC classifier), "t" (Students t-test), and "tobit" (Tobit-test for differential gene expression, as in Trapnell et al., Nature Biotech, 2014). For details see FindAllMarkers
`marker.sig`	Lower value will identify less significant marker. Default is 0.005
`only.pos`	Only return positive markers (TRUE by default)
`main`	Title of the plot.

A list of cluster with putative ranked markers and associated statistics in slot cluster.marker of main data. Also clusterwise figure in barplot and heatmap of desired top genes.

## 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 = 0.1)
## Get cluster marker
neuron_group_clust_marker <- ISS_clustMarker(data = neuron_group_clust, topgene =5,
                                        test.use="bimod", main = "")
 
## Plot cluster                                                                              
ISS_map (data=neuron_group_clust_marker, what = "cluster", label.topgene = 4)

## Plot tSNE 
neuron_group_clust_marker <- ISS_tsne(data = neuron_group_clust_marker, pc = 0.7)
ISS_map (data=neuron_group_clust_marker, what = "tsneAll", label.topgene = 4)