Package 'scROSHI'

Test config file

Description

Config file for the test_sce_data set

Usage

config

Format

A data frame with 2 columns and 7 rows

Details

Config file to define major cell types and hierarchical subtypes. It should be provided as a two-column data.frame where the first column are the major cell types and the second column are the subtypes. If several subtypes exists, they should be separated by comma.

Source

scROSHI - robust supervised hierarchical identification of single cells https://www.biorxiv.org/content/10.1101/2022.04.05.487176v1

---

Choose best matching cell type

Description

Choose best matching cell type

Usage

f_annot_ctgenes(m.cts, unknown, uncertain)

Arguments

m.cts	Matrix containing the cell type scores. The rows represent the cell types, whereas the columns represent the samples.
unknown	If none of the probabilities is above this threshold, the cell type label is assigned to the class unknown.
uncertain	If the ratio between the largest and the second largest probability is below this threshold, the cell type label is assigned to the class uncertain for the major cell types.

Value

Data frame containing the best matching cell type for each sample.

Examples

m.cts <- matrix(c(0.2, 0.001, 0.002, 0.1), nrow=2)
colnames(m.cts) <- c("sample1", "sample2")
rownames(m.cts) <- c("cell_type1", "cell_type2")
f_annot_ctgenes(m.cts, 0.05, 0.1)

---

Spearman correlation test

Description

Calculate spearman correlation p-value or return preset values.

Usage

f_my_correlation_test(x, y, min_genes = 5)

Arguments

x	A numeric vector of values.
y	A numeric vector of values.
min_genes	A numeric value defining the threshold for the minimum number of genes.

Value

A numeric value representing the p value of the Spearman correlation test.

Examples

f_my_correlation_test(rnorm(10,1,2),rnorm(10,5,2))

---

Wilcox test

Description

Calculate Wilcox p-value or return preset values

Usage

f_my_wilcox_test(x, y, min_genes = 5)

Arguments

x	A numeric vector of values.
y	A numeric vector of values.
min_genes	A numeric value defining the threshold for the minimum number of genes.

Value

A numeric value representing the p value of the Wilcox test.

Examples

f_my_wilcox_test(rnorm(10, 1, 2), rnorm(10, 5, 2))

---

Calculate cell type score

Description

Calculate cell type score

Usage

f_score_ctgenes_U(
  sce,
  gset,
  count_data = "normcounts",
  gene_symbol = "SYMBOL",
  min_genes = 5,
  verbose = 0
)

Arguments

sce	A SingleCellExperiment object containing the expression profiles of the single cell analysis.
gset	Marker gene list for all cell types.
count_data	Assay name in the SingleCellExperiment object containing the count data.
gene_symbol	Variable name in the row data of the SingleCellExperiment object containing the gene names.
min_genes	Minimum number of genes.
verbose	Level of verbosity. Zero means silent, one makes a verbose output.

Value

Matrix containing the cell type scores. The rows represent the cell types, whereas the columns represent the samples.

Examples

data("test_sce_data")
gset <- list(cell_type1 = c("CD79A", "TCL1A", "VPREB3"),
cell_type2 = c("FCER1A", "CLEC10A", "ENHO"))
f_score_ctgenes_U(test_sce_data[,1:3], gset, count_data = "normcounts",
gene_symbol = "SYMBOL", min_genes = 3, verbose = 0)

---

Calculate cell type score: U-test

Description

Calculate cell type score: U-test

Usage

f_score_profile_cor(sce, lprof, min_genes = 5, verbose = 0)

Arguments

sce	A SingleCellExperiment object containing the expression profiles of the single cell analysis.
lprof	List of profiles (named expression vectors).
min_genes	Minimum number of genes.
verbose	Level of verbosity. Zero means silent, one makes a verbose output.

Value

Matrix containing the cell type scores.

Examples

data("test_sce_data")
set.seed(123)
prof1 <- rpois(n = 20, lambda = 3)
names(prof1) <- rownames(test_sce_data)[51:70]
prof2 <- rpois(n = 20, lambda = 5)
names(prof2) <-  rownames(test_sce_data)[71:90]
lprof <- list(prof1 = prof1, prof2 = prof2)
f_score_profile_cor(test_sce_data[,1:3], lprof, min_genes = 5, verbose = 0)

---

Marker gene list for the test SCE data Set

Description

Marker gene list for the test_sce_data set

Usage

marker_list

Format

Marker gene list for all cell types as a list of genes with cell types as names

Source

scROSHI - robust supervised hierarchical identification of single cells https://www.biorxiv.org/content/10.1101/2022.04.05.487176v1

---

Robust Supervised Hierarchical Identification of Single Cells

Description

Identifying cell types based on expression profiles is a pillar of single cell analysis.

scROSHI identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data.

Usage

scROSHI(
  sce_data,
  celltype_lists,
  type_config,
  count_data = "normcounts",
  gene_symbol = "SYMBOL",
  cell_scores = FALSE,
  min_genes = 5,
  min_var = 1.5,
  n_top_genes = 2000,
  n_nn = 5,
  thresh_unknown = 0.05,
  thresh_uncert = 0.1,
  thresh_uncert_second = 0.8,
  verbose = 0,
  output = "sce"
)

Arguments

sce_data	A SingleCellExperiment object containing the expression profiles of the single cell analysis.
celltype_lists	Marker gene list for all cell types. It can be provided as a list of genes with cell types as names or as a path to a file containing the marker genes. Supported file formats are .gmt or .gmx files.
type_config	Config file to define major cell types and hierarchical subtypes. It should be provided as a two-column data.frame where the first column are the major cell types and the second column are the subtypes. If several subtypes exists, they should be separated by comma.
count_data	Assay name in the SingleCellExperiment object containing the count data.
gene_symbol	Variable name in the row data of the SingleCellExperiment object containing the gene names.
cell_scores	Boolean value determining if the scores should be saved.
min_genes	scROSHI filters out non-unique genes as long as more than min_genes are left. If there is a cell type that has less than min_genes genes, it will be replaced with the cell type list BEFORE filtering for unique genes (default 5).
min_var	Minimum variance for highly variable genes (default 1.5).
n_top_genes	Maximum number of highly variable genes (default 2000).
n_nn	Number of nearest neighbors for umap for assignment of cell types (default 5).
thresh_unknown	If none of the probabilities is above this threshold, the cell type label is assigned to the class unknown (default 0.05).
thresh_uncert	If the ratio between the largest and the second largest probability is below this threshold, the cell type label is assigned to the class uncertain for the major cell types (default 0.1).
thresh_uncert_second	If the ratio between the largest and the second largest probability is below this threshold, the cell type label is assigned to the class uncertain for the subtypes (default 0.8).
verbose	Level of verbosity. Zero means silent, one makes a verbose output.
output	Defines the output. sce: The output is a SingleCellExperiment object with the cell types appended to the meta data. df: The output id a data.frame with two columns. The first column contains the barcode of the cell and the second column contains the cell type labels.

Examples

data("test_sce_data")
data("config")
data("marker_list")

results <- scROSHI(sce_data = test_sce_data,
                   celltype_lists = marker_list,
                   type_config = config)
table(results$celltype_final)

---

Test SCE Data Set

Description

Data from a peripheral blood mononuclear cell experiments from an adult human.

Usage

test_sce_data

Format

A SingleCellExperiment object with 3368 genes and 1316 cells

Source

scROSHI - robust supervised hierarchical identification of single cells https://www.biorxiv.org/content/10.1101/2022.04.05.487176v1

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