Calculate the adjacency matrix given a spatial coordinate matrix

Description

Calculate the adjacency matrix given a spatial coordinate matrix with 2-dimension or 3-dimension or more.

Usage

AddAdj(
  pos,
  type = "fixed_distance",
  platform = c("Others", "Visium", "ST"),
  neighbors = 6,
  ...
)

Arguments

Details

When the type = "fixed_distance", then the spots within the Euclidean distance cutoffs from one spot are regarded as the neighbors of this spot. When the type = "fixed_number", the K-nearest spots are regarded as the neighbors of each spot.

Value

return a sparse matrix, representing the adjacency matrix.

References

None

See Also

None

Examples

data(CosMx_subset)
pos <- as.matrix(CosMx_subset@meta.data[,c("x", "y")])
Adj_sp <- AddAdj(pos)

Find clusters for SRT data

Description

Identify clusters of spots by a shared nearest neighbor (SNN) modularity optimization based on coFAST's embeddings.

Usage

AddCluster(
  seu,
  reduction = "cofast",
  cluster.name = "cofast.cluster",
  res = 0.8,
  K = NULL,
  res.start = 0.2,
  res.end = 2,
  step = 0.02
)

Arguments

Details

None

Value

return a revised Seurat object with a new column in meta.data named cluster.name.

References

None

See Also

None

Examples

library(Seurat)
data(pbmc3k_subset)
pbmc3k_subset <- AddCluster(pbmc3k_subset, reduction='ncfm')
head(pbmc3k_subset)

Add the spatial coordinates to the reduction slot

Description

Calculate the adjacency matrix given a spatial coordinate matrix with 2-dimension or 3-dimension or more.

Usage

Addcoord2embed(seu, coord.name, assay = "RNA")

Arguments

Value

return a revised Seurat object with a slot 'Spatial' in the reductions slot.

References

None

See Also

None

Examples

data(CosMx_subset)
library(Seurat)
Addcoord2embed(CosMx_subset, coord.name = c("x", "y"))

Calculate the aggregation score for specific clusters

Description

Calculate the adjacency matrix given a spatial coordinate matrix with 2-dimension or 3-dimension or more.

Usage

AggregationScore(seu, reduction.name = "cofast", random.seed = 1)

Arguments

Value

return a data.frame with two columns: the first column is the number of spots in each category (cluster/cell type); the second column is the corresponding aggregation score.

References

None

See Also

None

Examples

library(Seurat)
data(CosMx_subset)
CosMx_subset <- Addcoord2embed(CosMx_subset, coord.name = c("x", "y"))
Idents(CosMx_subset) <- 'cell_type'

dat.sp.score <- AggregationScore(CosMx_subset, reduction.name = 'Spatial')
print(dat.sp.score)

A CosMix spatial transcriptomics data

Description

This is a toy CosMix spatial transcriptomics data.

Examples

library(Seurat)
data(CosMx_subset)
head(CosMx_subset)

Cell-feature coembedding for scRNA-seq data

Description

Cell-feature coembedding for scRNA-seq data based on FAST model.

Usage

NCFM(
  object,
  assay = NULL,
  slot = "data",
  nfeatures = 2000,
  q = 10,
  reduction.name = "ncfm",
  weighted = FALSE,
  var.features = NULL
)

Arguments

Value

return a revised Seurat object with a new reduction slot reduction.name obtained by NCFM co-embedding method, where reduction.name is default as 'ncfm'.

Examples

data(pbmc3k_subset)
pbmc3k_subset <- NCFM(pbmc3k_subset)

Cell-feature coembedding for SRT data

Description

Run cell-feature coembedding for SRT data based on FAST model.

Usage

coFAST(
  object,
  Adj_sp,
  assay = NULL,
  slot = "data",
  nfeatures = 2000,
  q = 10,
  reduction.name = "cofast",
  var.features = NULL,
  ...
)

Arguments

Value

return a revised Seurat object with a new reduction slot reduction.name obtained by coFAST co-embedding, where default reduction.name is 'cofast'.

Examples

library(Seurat)
data(CosMx_subset)
pos <- as.matrix(CosMx_subset@meta.data[,c("x", "y")])
Adj_sp <- AddAdj(pos)
# Here, we set maxIter = 3 for cofast computation and demonstration.
CosMx_subset <- coFAST(CosMx_subset, Adj_sp = Adj_sp, maxIter=3)

Coembedding dimensional reduction plot

Description

Graph output of a dimensional reduction technique on a 2D scatter plot where each point is a cell or feature and it's positioned based on the coembeddings determined by the reduction technique. By default, cells and their signature features are colored by their identity class (can be changed with the group.by parameter).

Usage

coembed_plot(
  seu,
  reduction,
  gene_txtdata = NULL,
  cell_label = NULL,
  xy_name = reduction,
  dims = c(1, 2),
  cols = NULL,
  shape_cg = c(1, 5),
  pt_size = 1,
  pt_text_size = 5,
  base_size = 16,
  base_family = "serif",
  legend.point.size = 5,
  legend.key.size = 1.5,
  alpha = 0.3
)

Arguments

Details

None

Value

return a ggplot object

References

None

See Also

coembedding_umap

Examples

library(Seurat)
data(pbmc3k_subset)
data(top5_signatures)
coembed_plot(pbmc3k_subset, reduction = "UMAPsig",
 gene_txtdata = top5_signatures,  pt_text_size = 3, alpha=0.3)

Calculate UMAP projections for coembedding of cells and features

Description

Calculate UMAP projections for coembedding of cells and features

Usage

coembedding_umap(
  seu,
  reduction,
  reduction.name,
  gene.set = NULL,
  slot = "data",
  assay = "RNA",
  seed = 1
)

Arguments

Details

None

Value

return a revised Seurat object by adding a new reduction component named 'reduction.name'.

References

None

See Also

None

Examples

library(Seurat)
data(pbmc3k_subset)
data(top5_signatures)

pbmc3k_subset <- coembedding_umap(
  pbmc3k_subset, reduction = "ncfm", reduction.name = "UMAPsig",
  gene.set = top5_signatures$gene
)

Determine the dimension of low dimensional embedding

Description

This function estimate the dimension of low dimensional embedding for a given cell by gene expression matrix. For more details, see Franklin et al. (1995) and Crawford et al. (2010).

Usage

diagnostic.cor.eigs(object, ...)

## Default S3 method:
diagnostic.cor.eigs(
  object,
  q_max = 50,
  plot = TRUE,
  n.sims = 10,
  parallel = TRUE,
  ncores = 10,
  seed = 1,
  ...
)

## S3 method for class 'Seurat'
diagnostic.cor.eigs(
  object,
  assay = NULL,
  slot = "data",
  nfeatures = 2000,
  q_max = 50,
  seed = 1,
  ...
)

Arguments

Value

A data.frame with attribute 'q_est' and 'plot', which is the estimated dimension of low dimensional embedding. In addition, this data.frame containing the following components:

• q - The index of eigen values.

• eig_value - The eigen values on observed data.

• eig_sim - The mean value of eigen values of n.sims simulated data.

• q_est - The selected dimension in attr(obj, 'q_est').

• plot - The plot saved in attr(obj, 'plot').

References

1. Franklin, S. B., Gibson, D. J., Robertson, P. A., Pohlmann, J. T., & Fralish, J. S. (1995). Parallel analysis: a method for determining significant principal components. Journal of Vegetation Science, 6(1), 99-106.

2. Crawford, A. V., Green, S. B., Levy, R., Lo, W. J., Scott, L., Svetina, D., & Thompson, M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors.Educational and Psychological Measurement, 70(6), 885-901.

Examples

n <- 100
p <- 50
d <- 15
object <- matrix(rnorm(n*d), n, d) %*% matrix(rnorm(d*p), d, p)
diagnostic.cor.eigs(object, n.sims=2)

Find the signature genes for each group of cell/spots

Description

Find the signature genes for each group of cell/spots based on coembedding distance and expression ratio.

Usage

find.signature.genes(
  seu,
  distce.assay = "distce",
  ident = NULL,
  expr.prop.cutoff = 0.1,
  assay = NULL,
  genes.use = NULL
)

Arguments

Details

In each data.frame object of the returned value, the row.names are gene names, and these genes are sorted by decreasing order of 'distance'. User can define the signature genes as top n genes in distance and that the 'expr.prop' larger than a cutoff. We set the cutoff as 0.1.

Value

return a list with each component a data.frame object having two columns: 'distance' and 'expr.prop'.

References

None

See Also

None

Examples

library(Seurat)
data(pbmc3k_subset)
pbmc3k_subset <- pdistance(pbmc3k_subset, reduction='ncfm')
df_list_rna <- find.signature.genes(pbmc3k_subset)

Obtain the top signature genes and related information

Description

Obtain the top signature genes and related information.

Usage

get.top.signature.dat(df.list, ntop = 5, expr.prop.cutoff = 0.1)

Arguments

Details

Using this funciton, we obtain the top signature genes and organize them into a data.frame. The 'row.names' are gene names. The colname 'distance' means the distance between gene (i.e., VPREB3) and cells with the specific cell type (i.e., B cell), which is calculated based on the coembedding of genes and cells in the coembedding space. The distance is smaller, the association between gene and the cell type is stronger. The colname 'expr.prop' represents the expression proportion of the gene (i.e., VPREB3) within the cell type (i.e., B cell). The colname 'label' means the cell types and colname 'gene' denotes the gene name. By the data.frame object, we know 'VPREB3' is the one of the top signature gene of B cell.

Value

return a 'data.frame' object with four columns: 'distance','expr.prop', 'label' and 'gene'.

References

None

See Also

None

Examples

library(Seurat)
data(pbmc3k_subset)
pbmc3k_subset <- pdistance(pbmc3k_subset, reduction='ncfm')
df_list_rna <- find.signature.genes(pbmc3k_subset)
dat.sig <- get.top.signature.dat(df_list_rna, ntop=5)
head(dat.sig)

A toy single-cell RNA-seq data

Description

This a toy single-cell RNA-seq data, the subset of PBMC3K.

Examples

library(Seurat)
data(pbmc3k_subset)
head(pbmc3k_subset)

Calculate the cell-feature distance matrix

Description

Calculate the cell-feature distance matrix based on coembeddings.

Usage

pdistance(object, reduction = "cofast", assay.name = "distce", eta = 1e-10)

Arguments

Details

This function calculate the distance matrix between cells/spots and features, and then put the distance matrix in a new generated assay. This distance matrix will be used in the siganture gene identification.

Value

return a revised Seurat object with a assay slot 'assay.name'.

Examples

data(pbmc3k_subset)
pbmc3k_subset <- NCFM(pbmc3k_subset)
pbmc3k_subset <- pdistance(pbmc3k_subset, "ncfm")

A dataframe including top five signature genes

Description

A dataframe including top five signature genes for each cell type of PBMC3k.

Examples

library(Seurat)
data(top5_signatures)
head(top5_signatures)