Description Usage Arguments Value Examples
Any data in class MolDiaISS clusteded or not clustered used to reduce dimention to 2D by RCA-tsne.
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data |
Input data in class MolDiaISS. Output of readISS. |
pc |
Desired percent of variance to be explained by PCA. Default is 1 which means 100 percent variation explained. |
perplexity |
Numeric; Perplexity parameter. See Rtsne |
2D dataframe of points in slot @tsne.data.
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 32 33 34 35 36 37 38 39 40 | ## 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)
#### Dimention reduction by tSNE on non-clustered data
tsne_noclust <- ISS_tsne(data = neuron_group, pc = 0.7)
# Plot tSNE
result <- ISS_map(data = tsne_noclust, what = "tsneAll")
# Plot selected gene on tSNE plot
result <- ISS_map(data = tsne_noclust, what = "tsne", gene =tsne_noclust@gene[1:2] )
#### Dimention reduction by tSNE on clustered data
# Cluster data based on SEURAT pipeline
neuron_group_clust <- ISS_cluster(data = neuron_group, pc = 0.7, resolution = 0.3, method = "seurat")
# Dimention reduction by tSNE
tsne_clust <- ISS_tsne(data = neuron_group_clust, pc= 0.9, perplexity= 100)
Plot cluster on tSNE plot
result <- ISS_map(data = tsne_clust, what = "tsneAll")
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