Seurat_workflow: Create a Seurat object from score matrix, then,...

In Busydog1990/genepro: CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences

Create a Seurat object from score matrix, then, dimensionality reduction and classification of the score matrix.

Description

Create a Seurat object from score matrix, then, dimensionality reduction and classification of the score matrix.

Usage

Seurat_workflow(
  score_matrix,
  nfeatures = 2000,
  dims_SNN = 1:5,
  resolution = 0.1,
  dims_UMAP = 1:5,
  pca.reduction.name = "pca",
  pca.reduction.key = "PC_",
  umap.reduction.key = "UMAP_",
  ...
)

Arguments

score_matrix	Matrix. Score matrix of each site.
nfeatures	Number of features to select as top variable features.
dims_SNN	Dimensions of reduction to use as input with SNN.
resolution	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
dims_UMAP	Dimensions of reduction to use as input with UMAP.
pca.reduction.name	dimensional reduction name with PCA, Default:"pca".
pca.reduction.key	dimensional reduction key, specifies the string before the number for the dimension names with PCA. Default:"PC_".
umap.reduction.key	dimensional reduction key, specifies the string before the number for the dimension names with UMAP. Default:"UMAP_".
...	Other parameters of CreateSeuratObject, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, FindClusters and RunUMAP functions in Seurat package.

Value

score matrix of each site. Rownames of the repeated sequence name will be suffixed.

See Also

CreateSeuratObject,FindVariableFeatures,ScaleData, RunPCA,FindNeighbors,FindClusters,RunUMAP

Examples

require(ggseqlogo)
data(ggseqlogo_sample)
test_aa <- get_alignment_score(alignment = seqs_aa[[1]],type = "AA")
my_Seurat <- Seurat_workflow(test_aa)

Busydog1990/genepro documentation built on July 20, 2023, 6:03 a.m.