Package 'ASURAT' reference manual

Title:	Functional annotation-driven unsupervised clustering for single-cell data
Description:	ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
Authors:	Keita Iida [aut, cre] , Johannes Nicolaus Wibisana [ctb]
Maintainer:	Keita Iida <[email protected]>
License:	GPL-3 + file LICENSE
Version:	1.9.0
Built:	2024-07-05 05:44:47 UTC
Source:	https://github.com/bioc/ASURAT

Add metadata of variables and samples.

Description

This function adds metadata of variables and samples.

Usage

add_metadata(sce = NULL, mitochondria_symbol = NULL)
add_metadata(sce = NULL, mitochondria_symbol = NULL)

Arguments

`sce`	A SingleCellExperiment object.
`mitochondria_symbol`	A string representing for mitochondrial genes. This function computes percents of reads that map to the mitochondrial genes. Examples are '^MT-', '^mt-', etc.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")

Functional annotation-driven unsupervised clustering of SingleCell data.

Description

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

Perform bubble sorting, counting the number of steps.

Description

Perform bubble sorting, counting the number of steps.

Usage

bubble_sort(listdata)
bubble_sort(listdata)

Arguments

listdata

A list of vector and integer. For example, in R code, listdata = list(vec = c(1, 0, 1, ...), cnt = 0). The integer (cnt = 0) is the initial number of steps for bubble sorting.

Value

A List.

Examples

bubble_sort(list(vec = c(1, 1, 0), cnt = 0))

bubble_sort(list(vec = c(1, 1, 0), cnt = 0))

Cluster each functional gene set into three groups.

Description

This function clusters each functional gene set into strongly, variably, and weakly correlated gene sets.

Usage

cluster_genesets(sce = NULL, cormat = NULL, th_posi = NULL, th_nega = NULL)
cluster_genesets(sce = NULL, cormat = NULL, th_posi = NULL, th_nega = NULL)

Arguments

`sce`	A SingleCellExperiment object.
`cormat`	A correlation matrix of gene expressions.
`th_posi`	A threshold of positive correlation coefficient.
`th_nega`	A threshold of negative correlation coefficient.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
# The results are stored in `metadata(pbmcs$GO)$sign`.

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
# The results are stored in `metadata(pbmcs$GO)$sign`.

Compute separation indices for each cluster against the others.

Description

This function computes separation indices for each cluster versus the others.

Usage

compute_sepI_all(sce = NULL, labels = NULL, nrand_samples = NULL)
compute_sepI_all(sce = NULL, labels = NULL, nrand_samples = NULL)

Arguments

`sce`	A SingleCellExperiment object.
`labels`	A vector of labels of all the samples (cells).
`nrand_samples`	An integer for the number of samples used for random sampling, which samples at least one sample per cluster.

Value

A SingleCellExperiment object.

Examples

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_all(sce = pbmcs_eg$GO, labels = labels,
                                nrand_samples = 10)
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_all(sce = pbmcs_eg$GO, labels = labels,
                                nrand_samples = 10)
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

Compute separation indices of sign scores for given two clusters.

Description

This function computes separation indices of sign scores for given two clusters.

Usage

compute_sepI_clusters(
  sce = NULL,
  labels = NULL,
  nrand_samples = NULL,
  ident_1 = NULL,
  ident_2 = NULL
)
compute_sepI_clusters(
  sce = NULL,
  labels = NULL,
  nrand_samples = NULL,
  ident_1 = NULL,
  ident_2 = NULL
)

Arguments

`sce`	A SingleCellExperiment object.
`labels`	A vector of labels of all the samples.
`nrand_samples`	An integer for the number of samples used for random sampling, which samples at least one sample per cluster.
`ident_1`	Label names identifying cluster numbers, e.g., ident_1 = 1, ident_1 = c(1, 3).
`ident_2`	Label names identifying cluster numbers, e.g., ident_2 = 2, ident_2 = c(2, 4).

Value

A SingleCellExperiment object.

Examples

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_clusters(sce = pbmcs_eg$GO, labels = labels,
                                     nrand_samples = 10, ident_1 = 1,
                                     ident_2 = c(0, 2))
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

data(pbmcs_eg)
labels <- SummarizedExperiment::colData(pbmcs_eg$GO)$seurat_clusters
pbmcs_eg$GO <- compute_sepI_clusters(sce = pbmcs_eg$GO, labels = labels,
                                     nrand_samples = 10, ident_1 = 1,
                                     ident_2 = c(0, 2))
# The results are stored in `metadata(pbmcs_eg$GO)$marker_signs`.

Define signs for strongly and variably correlated gene sets.

Description

This function define signs for strongly and variably correlated gene sets.

Usage

create_signs(sce = NULL, min_cnt_strg = 2, min_cnt_vari = 2)
create_signs(sce = NULL, min_cnt_strg = 2, min_cnt_vari = 2)

Arguments

`sce`	A SingleCellExperiment object.
`min_cnt_strg`	An integer for the cutoff value for strongly correlated gene sets.
`min_cnt_vari`	An integer for the cutoff value for variably correlated gene sets.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
# The results are stored in `metadata(pbmcs$GO)$sign_all`.

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
# The results are stored in `metadata(pbmcs$GO)$sign_all`.

A list of small Cell Ontology and MSigDB databases for human.

Description

A list of small Cell Ontology and MSigDB databases for human.

Usage

human_COMSig_eg
human_COMSig_eg

Format

A list of dataframe.

A list of small Gene Ontology database for human.

Description

A list of small Gene Ontology database for human.

Usage

human_GO_eg
human_GO_eg

Format

A list of dataframe.

A list of small KEGG database for human.

Description

A list of small KEGG database for human.

Usage

human_KEGG_eg
human_KEGG_eg

Format

A list of dataframe.

Create a new SingleCellExperiment object for sign-by-sample matrices.

Description

This function creates a new SingleCellExperiment object for sign-by-sample matrices (SSM) by concatenating SSMs for strongly and variably correlated gene sets.

Usage

makeSignMatrix(sce = NULL, weight_strg = 0.5, weight_vari = 0.5)
makeSignMatrix(sce = NULL, weight_strg = 0.5, weight_vari = 0.5)

Arguments

`sce`	A SingleCellExperiment object.
`weight_strg`	A weight parameter for strongly correlated gene sets.
`weight_vari`	A weight parameter for variably correlated gene sets.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
pbmcs$GO <- makeSignMatrix(sce = pbmcs$GO, weight_strg = 0.5,
                           weight_vari = 0.5)
# The resutls can be check by, e.g., assay(pbmcs$GO, "counts").

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
pbmcs$GO <- makeSignMatrix(sce = pbmcs$GO, weight_strg = 0.5,
                           weight_vari = 0.5)
# The resutls can be check by, e.g., assay(pbmcs$GO, "counts").

A SingleCellExperiment object made from a gene expression table.

Description

A SingleCellExperiment object, including 50 genes and 50 cells. The original data "4k PBMCs from a Healthy Donor" was downloaded from 10x Genomics database.

Usage

pbmc_eg
pbmc_eg

Format

SingleCellExperiment object.

Source

https://support.10xgenomics.com/single-cell-gene-expression

A list of SingleCellExperiment objects made from sign-sample matrices.

Description

A list of SingleCellExperiment objects, consisting of small sign-by-sample matrices, pbmcs_eg$CM (using Cell Ontology and MSigDB databases), pbmcs_eg$GO (using Gene Ontology database), and pbmcs_eg$KG (KEGG). Here, pbmcs_eg$CM, pbmcs_eg$GO, and pbmcs_eg$KG include 87, 72, and 64 signs, respectively, and 50 cells.

Usage

pbmcs_eg
pbmcs_eg

Format

A list of SingleCellExperiment objects.

Visualize a three-dimensional data with labels and colors.

Description

This function visualizes a three-dimensional data with labels and colors.

Usage

plot_dataframe3D(
  dataframe3D = NULL,
  labels = NULL,
  colors = NULL,
  theta = 30,
  phi = 30,
  title = "",
  xlabel = "",
  ylabel = "",
  zlabel = ""
)
plot_dataframe3D(
  dataframe3D = NULL,
  labels = NULL,
  colors = NULL,
  theta = 30,
  phi = 30,
  title = "",
  xlabel = "",
  ylabel = "",
  zlabel = ""
)

Arguments

`dataframe3D`	A dataframe with three columns.
`labels`	NULL or a vector of labels of all the samples, corresponding to colors.
`colors`	NULL or a vector of colors of all the samples, corresponding to labels.
`theta`	Angle of the plot.
`phi`	Angle of the plot.
`title`	Title.
`xlabel`	x-axis label.
`ylabel`	y-axis label.
`zlabel`	z-axis label.

Value

A scatter3D object in plot3D package.

Examples

data(pbmcs_eg)
mat <- SingleCellExperiment::reducedDim(pbmcs_eg$CM, "UMAP")[, 1:3]
dataframe3D <- as.data.frame(mat)
labels <- SummarizedExperiment::colData(pbmcs_eg$CM)$seurat_clusters
plot_dataframe3D(dataframe3D = dataframe3D, labels = labels, colors = NULL,
                 theta = 45, phi = 20, title = "PBMC (CO & MSigDB)",
                 xlabel = "UMAP_1", ylabel = "UMAP_2", zlabel = "UMAP_3")

data(pbmcs_eg)
mat <- SingleCellExperiment::reducedDim(pbmcs_eg$CM, "UMAP")[, 1:3]
dataframe3D <- as.data.frame(mat)
labels <- SummarizedExperiment::colData(pbmcs_eg$CM)$seurat_clusters
plot_dataframe3D(dataframe3D = dataframe3D, labels = labels, colors = NULL,
                 theta = 45, phi = 20, title = "PBMC (CO & MSigDB)",
                 xlabel = "UMAP_1", ylabel = "UMAP_2", zlabel = "UMAP_3")

Visualize multivariate data by heatmaps.

Description

This function visualizes multivariate data by heatmaps.

Usage

plot_multiheatmaps(
  ssm_list = NULL,
  gem_list = NULL,
  ssmlabel_list = NULL,
  gemlabel_list = NULL,
  nrand_samples = NULL,
  show_row_names = FALSE,
  title = NULL
)
plot_multiheatmaps(
  ssm_list = NULL,
  gem_list = NULL,
  ssmlabel_list = NULL,
  gemlabel_list = NULL,
  nrand_samples = NULL,
  show_row_names = FALSE,
  title = NULL
)

Arguments

`ssm_list`	A list of sign-by-sample matrices.
`gem_list`	A list of gene-by-sample matrices.
`ssmlabel_list`	NULL or a list of dataframes of sample (cell) labels and colors. The length of the list must be as same as that of ssm_list, and the order of labels in each list must be as same as those in ssm_list.
`gemlabel_list`	NULL or a list of dataframes of sample (cell) annotations and colors. The length of the list must be as same as that of gem_list, and the order of labels in each list must be as same as those in gem_list.
`nrand_samples`	Number of samples (cells) used for random sampling.
`show_row_names`	TRUE or FALSE: if TRUE, row names are shown.
`title`	Title.

Value

A ComplexHeatmap object.

Examples

data(pbmcs_eg)
mat_CM <- SummarizedExperiment::assay(pbmcs_eg$CM, "counts")
mat_GO <- SummarizedExperiment::assay(pbmcs_eg$GO, "counts")
mat_KG <- SummarizedExperiment::assay(pbmcs_eg$KG, "counts")
ssm_list <- list(SSM_COMSig = mat_CM, SSM_GO = mat_GO, SSM_KEGG = mat_KG)
se <- SingleCellExperiment::altExp(pbmcs_eg$CM, "logcounts")
mat <- SummarizedExperiment::assay(se, "counts")
se <- SingleCellExperiment::altExp(pbmcs_eg$CM, "logcounts")
gem_list <- list(GeneExpr = SummarizedExperiment::assay(se, "counts"))
labels <- list() ; ssmlabel_list <- list()
for(i in seq_along(pbmcs_eg)){
  fa <- SummarizedExperiment::colData(pbmcs_eg[[i]])$seurat_clusters
  labels[[i]] <- data.frame(label = fa)
  colors <- rainbow(length(unique(labels[[i]]$label)))[labels[[i]]$label]
  labels[[i]]$color <- colors
  ssmlabel_list[[i]] <- labels[[i]]
}
names(ssmlabel_list) <- c("Label_COMSig", "Label_GO", "Label_KEGG")
phases <- SummarizedExperiment::colData(pbmcs_eg$CM)$Phase
label_CC <- data.frame(label = phases, color = NA)
gemlabel_list <- list(CellCycle = label_CC)
plot_multiheatmaps(ssm_list = ssm_list, gem_list = gem_list,
                   ssmlabel_list = ssmlabel_list,
                   gemlabel_list = gemlabel_list, nrand_samples = 50,
                   show_row_names = FALSE, title = "PBMC")

data(pbmcs_eg)
mat_CM <- SummarizedExperiment::assay(pbmcs_eg$CM, "counts")
mat_GO <- SummarizedExperiment::assay(pbmcs_eg$GO, "counts")
mat_KG <- SummarizedExperiment::assay(pbmcs_eg$KG, "counts")
ssm_list <- list(SSM_COMSig = mat_CM, SSM_GO = mat_GO, SSM_KEGG = mat_KG)
se <- SingleCellExperiment::altExp(pbmcs_eg$CM, "logcounts")
mat <- SummarizedExperiment::assay(se, "counts")
se <- SingleCellExperiment::altExp(pbmcs_eg$CM, "logcounts")
gem_list <- list(GeneExpr = SummarizedExperiment::assay(se, "counts"))
labels <- list() ; ssmlabel_list <- list()
for(i in seq_along(pbmcs_eg)){
  fa <- SummarizedExperiment::colData(pbmcs_eg[[i]])$seurat_clusters
  labels[[i]] <- data.frame(label = fa)
  colors <- rainbow(length(unique(labels[[i]]$label)))[labels[[i]]$label]
  labels[[i]]$color <- colors
  ssmlabel_list[[i]] <- labels[[i]]
}
names(ssmlabel_list) <- c("Label_COMSig", "Label_GO", "Label_KEGG")
phases <- SummarizedExperiment::colData(pbmcs_eg$CM)$Phase
label_CC <- data.frame(label = phases, color = NA)
gemlabel_list <- list(CellCycle = label_CC)
plot_multiheatmaps(ssm_list = ssm_list, gem_list = gem_list,
                   ssmlabel_list = ssmlabel_list,
                   gemlabel_list = gemlabel_list, nrand_samples = 50,
                   show_row_names = FALSE, title = "PBMC")

Remove samples based on expression profiles across variables.

Description

This function removes sample data by setting minimum and maximum threshold values for the metadata.

Usage

remove_samples(
  sce = NULL,
  min_nReads = NULL,
  max_nReads = NULL,
  min_nGenes = NULL,
  max_nGenes = NULL,
  min_percMT = NULL,
  max_percMT = NULL
)
remove_samples(
  sce = NULL,
  min_nReads = NULL,
  max_nReads = NULL,
  min_nGenes = NULL,
  max_nGenes = NULL,
  min_percMT = NULL,
  max_percMT = NULL
)

Arguments

`sce`	A SingleCellExperiment object.
`min_nReads`	A minimum threshold value of the number of reads.
`max_nReads`	A maximum threshold value of the number of reads.
`min_nGenes`	A minimum threshold value of the number of non-zero expressed genes.
`max_nGenes`	A maximum threshold value of the number of non-zero expressed genes.
`min_percMT`	A minimum threshold value of the percent of reads that map to mitochondrial genes.
`max_percMT`	A maximum threshold value of the percent of reads that map to mitochondrial genes.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")
pbmc <- remove_samples(sce = pbmc, min_nReads = 0, max_nReads = 1e+10,
                       min_nGenes = 0, max_nGenes = 1e+10,
                       min_percMT = NULL, max_percMT = NULL)

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")
pbmc <- remove_samples(sce = pbmc, min_nReads = 0, max_nReads = 1e+10,
                       min_nGenes = 0, max_nGenes = 1e+10,
                       min_percMT = NULL, max_percMT = NULL)

Remove signs including too few or too many genes.

Description

This function removes signs including too few or too many genes.

Usage

remove_signs(sce = NULL, min_ngenes = 2, max_ngenes = 1000)
remove_signs(sce = NULL, min_ngenes = 2, max_ngenes = 1000)

Arguments

`sce`	A SingleCellExperiment object.
`min_ngenes`	Minimum number of genes, which must be greater than one.
`max_ngenes`	Maximum number of genes, which must be greater than one.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
# The results are stored in `metadata(pbmcs$GO)$sign`.

data(pbmc_eg)
data(human_GO_eg)
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
# The results are stored in `metadata(pbmcs$GO)$sign`.

Remove signs by specifying keywords.

Description

This function removes signs by specifying keywords.

Usage

remove_signs_manually(sce = NULL, keywords = NULL)
remove_signs_manually(sce = NULL, keywords = NULL)

Arguments

`sce`	A SingleCellExperiment object.
`keywords`	keywords separated by pipes '\|'.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
keywords <- "Covid19|foofoo|hogehoge"
pbmcs$GO <- remove_signs_manually(sce = pbmcs$GO, keywords = keywords)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, and `metadata(pbmcs$GO)$sign_all`.

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
keywords <- "Covid19|foofoo|hogehoge"
pbmcs$GO <- remove_signs_manually(sce = pbmcs$GO, keywords = keywords)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, and `metadata(pbmcs$GO)$sign_all`.

Remove redundant signs using semantic similarity matrices.

Description

This function removes redundant signs using semantic similarity matrices.

Usage

remove_signs_redundant(
  sce = NULL,
  similarity_matrix = NULL,
  threshold = NULL,
  keep_rareID = NULL
)
remove_signs_redundant(
  sce = NULL,
  similarity_matrix = NULL,
  threshold = NULL,
  keep_rareID = NULL
)

Arguments

`sce`	A SingleCellExperiment object.
`similarity_matrix`	A semantic similarity matrix.
`threshold`	A threshold value of semantic similarity, used for regarding biological terms as similar ones
`keep_rareID`	If TRUE, biological terms with the larger ICs are kept.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
pbmcs$GO <- remove_signs_redundant(
  sce = pbmcs$GO, similarity_matrix = human_GO_eg$similarity_matrix$BP,
  threshold = 0.80, keep_rareID = TRUE)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, `metadata(pbmcs$GO)$sign_all`,
# and if there exist, `metadata(pbmcs$GO)$sign_SCG_redundant` and
# `metadata(pbmcs$GO)$sign_VCG_redundant`.

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
pbmcs$GO <- remove_signs_redundant(
  sce = pbmcs$GO, similarity_matrix = human_GO_eg$similarity_matrix$BP,
  threshold = 0.80, keep_rareID = TRUE)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, `metadata(pbmcs$GO)$sign_all`,
# and if there exist, `metadata(pbmcs$GO)$sign_SCG_redundant` and
# `metadata(pbmcs$GO)$sign_VCG_redundant`.

Remove variables based on expression profiles across samples.

Description

This function removes low expressed variable data.

Usage

remove_variables(sce = NULL, min_nsamples = 0)
remove_variables(sce = NULL, min_nsamples = 0)

Arguments

`sce`	A SingleCellExperiment object.
`min_nsamples`	An integer. This function removes variables for which the numbers of non-zero expressing samples are less than this value.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")
pbmc <- remove_variables(sce = pbmc, min_nsamples = 10)

data(pbmc_eg)
pbmc <- add_metadata(sce = pbmc_eg, mitochondria_symbol = "^MT-")
pbmc <- remove_variables(sce = pbmc, min_nsamples = 10)

Remove variables based on the mean expression levels across samples.

Description

This function removes variable data such that the mean expression levels across samples are less than 'min_meannReads'.

Usage

remove_variables_second(sce = NULL, min_meannReads = 0)
remove_variables_second(sce = NULL, min_meannReads = 0)

Arguments

`sce`	A SingleCellExperiment object.
`min_meannReads`	An integer. This function removes variables for which the mean read counts are less than this value.

Value

A SingleCellExperiment object.

Examples

data(pbmc_eg)
pbmc <- remove_variables_second(sce = pbmc_eg, min_meannReads = 0.01)

data(pbmc_eg)
pbmc <- remove_variables_second(sce = pbmc_eg, min_meannReads = 0.01)

Select signs by specifying keywords.

Description

This function selects signs by specifying keywords.

Usage

select_signs_manually(sce = NULL, keywords = NULL)
select_signs_manually(sce = NULL, keywords = NULL)

Arguments

`sce`	An ASURAT object.
`keywords`	Keywords separated by a pipe.

Value

An ASURAT object.

Examples

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
keywords <- "cell|process"
pbmcs$GO <- select_signs_manually(sce = pbmcs$GO, keywords = keywords)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, and `metadata(pbmcs$GO)$sign_all`.

data(pbmc_eg)
data(human_GO_eg)
mat <- t(as.matrix(SummarizedExperiment::assay(pbmc_eg, "centered")))
pbmc_cormat <- cor(mat, method = "spearman")
pbmcs <- list(GO = pbmc_eg)
S4Vectors::metadata(pbmcs$GO) <- list(sign = human_GO_eg[["BP"]])
pbmcs$GO <- remove_signs(sce = pbmcs$GO, min_ngenes = 2, max_ngenes = 1000)
pbmcs$GO <- cluster_genesets(sce = pbmcs$GO, cormat = pbmc_cormat,
                             th_posi = 0.24, th_nega = -0.20)
pbmcs$GO <- create_signs(sce = pbmcs$GO, min_cnt_strg = 2, min_cnt_vari = 2)
keywords <- "cell|process"
pbmcs$GO <- select_signs_manually(sce = pbmcs$GO, keywords = keywords)
# The results are stored in `metadata(pbmcs$GO)$sign_SCG`,
# `metadata(pbmcs$GO)$sign_VCG`, and `metadata(pbmcs$GO)$sign_all`.

Perform one-shot adjacent swapping for each element.

Description

Perform one-shot adjacent swapping for each element.

Usage

swap_pass(listdata)
swap_pass(listdata)

Arguments

listdata

A list of vector and integer.

Value

A List.

Examples

swap_pass(list(vec = c(1, 1, 0), cnt = 0))

swap_pass(list(vec = c(1, 1, 0), cnt = 0))

Package 'ASURAT'

Help Index

Add metadata of variables and samples.

Description

Usage

Arguments

Value

Examples

Functional annotation-driven unsupervised clustering of SingleCell data.

Description

Perform bubble sorting, counting the number of steps.

Description

Usage

Arguments

Value

Examples

Cluster each functional gene set into three groups.

Description

Usage

Arguments

Value

Examples

Compute separation indices for each cluster against the others.

Description

Usage

Arguments

Value

Examples

Compute separation indices of sign scores for given two clusters.

Description

Usage

Arguments

Value

Examples

Define signs for strongly and variably correlated gene sets.

Description

Usage

Arguments

Value

Examples

A list of small Cell Ontology and MSigDB databases for human.

Description

Usage

Format

A list of small Gene Ontology database for human.

Description

Usage

Format

A list of small KEGG database for human.

Description

Usage

Format

Create a new SingleCellExperiment object for sign-by-sample matrices.

Description

Usage

Arguments

Value

Examples

A SingleCellExperiment object made from a gene expression table.

Description

Usage

Format

Source

A list of SingleCellExperiment objects made from sign-sample matrices.

Description

Usage

Format

Visualize a three-dimensional data with labels and colors.

Description

Usage

Arguments

Value

Examples

Visualize multivariate data by heatmaps.

Description

Usage

Arguments

Value

Examples

Remove samples based on expression profiles across variables.