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)

Add FAST model settings for a PRECASTObj object

Description

Add FAST model settings for a PRECASTObj object

Usage

AddParSettingFAST(PRECASTObj, ...)

Arguments

Value

Return a revised PRECASTObj object with slot parameterList changed.

References

None

A Seurat object including spatial transcriptomics dataset from CosMx platform

Description

This data is a subset of SCLC CosMx spatial transcriptomics dataset.

Usage

  
  data(CosMx_subset)  

Format

A Seurat object, including count matrix, sptial coordinates, and manual annotation.

Source

The data is from the CosMx SRT sequencing platform.

References

None

Examples

  
  # Show some examples of how to use the dataset.  
  data(CosMx_subset)  
  library(Seurat)
  CosMx_subset

Run FAST model for a PRECASTObj object

Description

Run FAST model for a PRECASTObj object

Usage

FAST(PRECASTObj, q = 15, fit.model = c("poisson", "gaussian"))

Arguments

Value

Return a revised PRECASTObj object with slot PRECASTObj@resList added by a FAST compoonent.

References

None

(Varitional) ICM-EM algorithm for implementing FAST model

Description

(Varitional) ICM-EM algorithm for implementing FAST model

Usage

FAST_run(
  XList,
  AdjList,
  q = 15,
  fit.model = c("gaussian", "poisson"),
  AList = NULL,
  maxIter = 25,
  epsLogLik = 1e-05,
  verbose = TRUE,
  seed = 1,
  error_heter = TRUE,
  Psi_diag = FALSE,
  Vint_zero = FALSE
)

Arguments

Details

None

Value

return a list including the following components: (1) hV: an M-length list consisting of spatial embeddings in FAST; (2) nu: the estimated intercept vector; (3) Psi: the estimated covariance matrix; (4) W: the estimated shared loading matrix; (5) Lam: the estimated covariance matrix of error term; (6): ELBO: the ELBO value when algorithm convergence; (7) ELBO_seq: the ELBO values for all itrations.

References

None

See Also

FAST_structure, FAST, model_set_FAST

Fit FAST model for single-section SRT data

Description

Fit FAST model for single-section SRT data.

Usage

FAST_single(
  seu,
  Adj_sp,
  q = 15,
  fit.model = c("poisson", "gaussian"),
  slot = "data",
  assay = NULL,
  reduction.name = "fast",
  verbose = TRUE,
  ...
)

Arguments

Value

return a list including the parameters set in the arguments.

See Also

FAST_run

(Varitional) ICM-EM algorithm for implementing FAST model with structurized parameters

Description

(Varitional) ICM-EM algorithm for implementing FAST model with structurized parameters

Usage

FAST_structure(
  XList,
  AdjList,
  q = 15,
  fit.model = c("poisson", "gaussian"),
  parameterList = NULL
)

Arguments

Details

None

Value

return a list including the following components: (1) hV: an M-length list consisting of spatial embeddings in FAST; (2) nu: the estimated intercept vector; (3) Psi: the estimated covariance matrix; (4) W: the estimated shared loading matrix; (5) Lam: the estimated covariance matrix of error term; (6): ELBO: the ELBO value when algorithm convergence; (7) ELBO_seq: the ELBO values for all itrations.

References

None

See Also

FAST_run, FAST, model_set_FAST

Integrate multiple SRT data into a Seurat object

Description

Integrate multiple SRT data based on the PRECASTObj object by FAST and other model fitting.

Usage

IntegrateSRTData(
  PRECASTObj,
  seulist_HK,
  Method = c("iSC-MEB", "HarmonyLouvain"),
  seuList_raw = NULL,
  covariates_use = NULL,
  Tm = NULL,
  subsample_rate = 1,
  verbose = TRUE
)

Arguments

Details

If seuList_raw is not equal NULL or PRECASTObj@seuList is not NULL, this function will remove the unwanted variations for all genes in seuList_raw object. Otherwise, only the the unwanted variation of genes in PRECASTObj@seulist will be removed. The former requires a big memory to be run, while the latter not. To speed up the computation when the number of spots is very large, we also provide a subsampling schema controlled by the arugment subsample_rate. When the total number of spots is larger than 80,000, this function will automatically draws 50,000 spots to calculate the paramters in the spatial linear model for removing unwanted variations.

Value

Return a Seurat object by integrating all SRT data batches into a SRT data, where the column "batch" in the meta.data represents the batch ID, and the column "cluster" represents the clusters. The embeddings are put in seu@reductions slot and Idents(seu) is set to cluster label. Note that only the normalized expression is valid in the data slot while count is invalid.

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

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

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

Arguments

Examples

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

Embedding alignment and clustering based on the embeddings from FAST

Description

Embedding alignment and clustering using the Harmony and Louvain based on the ebmeddings from FAST as well as determining the number of clusters.

Usage

RunHarmonyLouvain(PRECASTObj, resolution = 0.5)

Arguments

Value

Return a revised PRECASTObj object with slot PRECASTObj@resList added by a Harmony compoonent (including the aligned embeddings and embeddings of batch effects) and a Louvain component (including the clusters).

Fit an iSC-MEB model using the embeddings from FAST

Description

Fit an iSC-MEB model using the embeddings from FAST and the number of clusters obtained by Louvain.

Usage

RuniSCMEB(PRECASTObj, ...)

Arguments

Value

Return a revised PRECASTObj object with an added component iSCMEB in the slot PRECASTObj@resList (including the aligned embeddings, clusters and posterior probability matrix of clusters).

Select housekeeping genes

Description

Select housekeeping genes for preparation of removing unwanted variations in expression matrices

Usage

SelectHKgenes(seuList, species = c("Human", "Mouse"), HK.number = 200)

Arguments

Value

Return a string vector of the selected gene names.

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

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

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)

Calcuate the the adjusted McFadden's pseudo R-square

Description

Calcuate the the adjusted McFadden's pseudo R-square between the embeddings and the labels

Usage

get_r2_mcfadden(embeds, y)

Arguments

Details

None

Value

return the adjusted McFadden's pseudo R-square.

References

McFadden, D. (1987). Regression-based specification tests for the multinomial logit model. Journal of econometrics, 34(1-2), 63-82.

Fit an iSC-MEB model using specified multi-section embeddings

Description

Integrate multiple SRT data based on the PRECASTObj by FAST and iSC-MEB model fitting.

Usage

iscmeb_run(
  VList,
  AdjList,
  K,
  beta_grid = seq(0, 5, by = 0.2),
  maxIter = 25,
  epsLogLik = 1e-05,
  verbose = TRUE,
  int.model = "EEE",
  init.start = 1,
  Sigma_equal = FALSE,
  Sigma_diag = TRUE,
  seed = 1
)

Arguments

Value

returns a iSCMEBResObj object which contains all model results.

Set parameters for FAST model

Description

Prepare parameters setup for FAST model fitting.

Usage

model_set_FAST(
  maxIter = 30,
  epsLogLik = 1e-05,
  error_heter = TRUE,
  Psi_diag = FALSE,
  verbose = TRUE,
  seed = 1
)

Arguments

Value

return a Seurat object with new reduction (named reduction.name) added to the 'reductions' slot.

Examples

model_set_FAST(maxIter = 30, epsLogLik = 1e-5,
  error_heter=TRUE, Psi_diag=FALSE, verbose=TRUE, seed=2023)

A Seurat object including scRNA-seq PBMC dataset

Description

This data is a subset of PBMC3k scRNA-seq data in SeuratData package.

Usage

  
  data(pbmc3k_subset)  

Format

A Seurat object, including count matrix, and manual annotation.

Source

The data is from the scRNA-seq sequencing platform.

References

None

Examples

  
  # Show  examples of how to use the dataset.  
  data(pbmc3k_subset)  
  library(Seurat)
  pbmc3k_subset

Calculate the cell-feature distance matrix

Description

Calculate the cell-feature distance matrix based on coembeddings.

Usage

pdistance(object, reduction = "fast", 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.

Examples

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

A data.frame object including top five signature genes in scRNA-seq PBMC dataset

Description

This data is a data.frame object that includes top five signature genes in scRNA-seq PBMC dataset

Usage

  
  data(top5_signatures)  

Format

A data.frame object, including signature genes, distance, and manual annotation.

Source

None

References

None

Examples

  
  # Show  examples of how to use the dataset.  
  data(top5_signatures)  
  head(top5_signatures)

Transfer gene names from one fortmat to the other format

Description

Transfer gene names from one fortmat to the other format for two species: human and mouse.

Usage

transferGeneNames(
  genelist,
  now_name = "ensembl",
  to_name = "symbol",
  species = c("Human", "Mouse")
)

Arguments

Value

Return a string vector of transferred gene names. The gene names not matched in the database will not change.

Examples

## Not run: 
geneNames <- c("ENSG00000171885", "ENSG00000115756")
transferGeneNames(geneNames, now_name = "ensembl", to_name="symbol", species="Human")

## End(Not run)