Description Usage Arguments Examples
View source: R/6.1_ISS_cluster.R
Cluster ISS data by different methods used in SEURAT, MONOCLE, BackSPIN.
1 2 3 | 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)
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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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## 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)
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