Package 'scGOclust'

Title: Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq
Description: Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.
Authors: Yuyao Song [aut, cre, ctb], Irene Papatheodorou [aut, ths]
Maintainer: Yuyao Song <[email protected]>
License: GPL (>= 3)
Version: 0.2.1
Built: 2025-02-14 04:14:28 UTC
Source: https://github.com/papatheodorou-group/scgoclust

Help Index

standard seurat analysis on GO_seurat object

Description

standard seurat analysis on GO_seurat object

Usage

analyzeGOSeurat(
  go_seurat_obj,
  cell_type_col,
  norm_log1p = TRUE,
  scale.factor = 10000,
  nfeatures = 2000,
  cluster_res = 1,
  min.dist = 0.3,
  ...
)

Arguments

go_seurat_obj

go seurat object created by makeGOSeurat
cell_type_col

column name in mera.data storing cell type classes
norm_log1p

whether or not to perform data normalisation and log1p transformation, default TRUE
scale.factor

param for Seurat NormalizeData
nfeatures

param for Seurat FindVariableFeatures
cluster_res

resolution for Seurat FindClusters
min.dist

param for Seurat RunUMAP
...

additional params for all Seurat functions involved in this function

Value

standard analyzed GO seurat object until UMAP

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
go_seurat_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")

analyzeGOSeurat(go_seurat_obj = go_seurat_obj, cell_type_col = "cell_type_annotation")

calculate correlation between cell types represented by scaled GO, per-species

Description

calculate correlation between cell types represented by scaled GO, per-species

Usage

cellTypeGOCorr(cell_type_go, corr_method = "pearson")

Arguments

cell_type_go

cell type GO table calculated via getCellTypeGO
corr_method

correlation method, choose among "pearson", "kendall", "spearman", default 'pearson'

Value

a dataframe with correlation between cell types

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
go_seurat_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")

cell_type_go = getCellTypeGO(go_seurat_obj = go_seurat_obj, cell_type_co = "cell_type_annotation")

cellTypeGOCorr(cell_type_go = cell_type_go, corr_method = "pearson")

calculate cross-species correlation between cell types represented by scaled GO

Description

calculate cross-species correlation between cell types represented by scaled GO

Usage

crossSpeciesCellTypeGOCorr(
  species_1,
  species_2,
  cell_type_go_sp1,
  cell_type_go_sp2,
  corr_method = "pearson"
)

Arguments

species_1

name of species one
species_2

name of species two
cell_type_go_sp1

cell type GO table of species one calculated via getCellTypeGO
cell_type_go_sp2

cell type GO table of species two calculated via getCellTypeGO
corr_method

correlation method, choose among "pearson", "kendall", "spearman", default 'pearson'

Value

correlation between cell types

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
data(dme_tbl)
data(dme_subset)
mmu_go_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")
dme_go_obj = makeGOSeurat(ensembl_to_GO = dme_tbl,
 seurat_obj = dme_subset,
 feature_type = "external_gene_name")

mmu_cell_type_go = getCellTypeGO(go_seurat_obj = mmu_go_obj, cell_type_co = "cell_type_annotation")
dme_cell_type_go = getCellTypeGO(go_seurat_obj = dme_go_obj, cell_type_co = "annotation")

crossSpeciesCellTypeGOCorr(species_1 = 'mmusculus',
 species_2 = 'dmelanogaster',
 cell_type_go_sp1 = mmu_cell_type_go,
 cell_type_go_sp2 = dme_cell_type_go)

Drosophila gut scRNA-seq data, 10X Chromium Subset to 45 cells per cell type as an example data

Description

Drosophila gut scRNA-seq data, 10X Chromium Subset to 45 cells per cell type as an example data

Usage

dme_subset

Format

a 'Seurat' object

Source

<https://flycellatlas.org/>

Drosophila EMSEMBL gene and GO annotation, subset to genes present in 'dme_subset'

Description

Drosophila EMSEMBL gene and GO annotation, subset to genes present in 'dme_subset'

Usage

dme_tbl

Format

a 'data.frame' object

Source

<http://www.ensembl.org/>

get requested ensembl ID to GO mapping table

Description

get requested ensembl ID to GO mapping table

Usage

ensemblToGo(
  species,
  GO_type = "biological_process",
  GO_linkage_type = c("standard"),
  ...
)

Arguments

species

species name matching ensembl biomaRt naming, such as hsapiens, mmusculus
GO_type

GO term type, choose among 'biological_process', 'molecular_function', 'cellular_component', default 'biological_process'
GO_linkage_type

GO annotation evidence codes to include. Default is 'standard', which means only including manually checked records (excluding IEA) and excluding those inferred from gene expression experiments to maximally suffice the species expression independence assumption. 'Stringent' means only including those with experimental evidence, also not from gene expression experiment, or from manual curation with evidence (excluding those from mass-annotation pipelines). Choose among 'experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic', 'standard', stringent'
...

additional params for useEnsembl function called in this function

Value

a table with ensembl to GO terms mapping including requested linkage type

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
ensemblToGo("mmusculus", GO_type = "biological_process", GO_linkage_type = 'stringent')

get per cell type average scaled vector of GO terms

Description

get per cell type average scaled vector of GO terms

Usage

getCellTypeGO(go_seurat_obj, cell_type_col, norm_log1p = TRUE)

Arguments

go_seurat_obj

go seurat object created by makeGOSeurat
cell_type_col

column name in mera.data storing cell type classes
norm_log1p

whether or not to perform data normalisation and log1p transformation, default TRUE

Value

a table of scaled GO representation per cell type (averaged)

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
go_seurat_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")
getCellTypeGO(go_seurat_obj = go_seurat_obj, cell_type_co = "cell_type_annotation")

get shared up and down regulated GO terms for all pairs of cell types

Description

get shared up and down regulated GO terms for all pairs of cell types

Usage

getCellTypeSharedGO(
  species_1,
  species_2,
  analyzed_go_seurat_sp1,
  analyzed_go_seurat_sp2,
  cell_type_col_sp1,
  cell_type_col_sp2,
  layer_use = "data",
  p_val_threshould = 0.01
)

Arguments

species_1

name of species one
species_2

name of species two
analyzed_go_seurat_sp1

analyzed GO seurat object of species one
analyzed_go_seurat_sp2

analyzed GO seurat object of species two
cell_type_col_sp1

cell type column name for species 1 data
cell_type_col_sp2

cell type column name for species 2 data
layer_use

layer to use for marker computation, default 'data' which after NormalizeData will be log1p normalized data.
p_val_threshould

p value threshold for selecting DEG (p_adjust)

Value

a list with sp1 raw, sp2 raw and shared, significant up and down regulated GO terms per cell type (pair)

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
data(dme_tbl)
data(dme_subset)

mmu_go_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")
dme_go_obj = makeGOSeurat(ensembl_to_GO = dme_tbl,
 seurat_obj = dme_subset,
 feature_type = "external_gene_name")


mmu_go_obj_analyzed = analyzeGOSeurat(mmu_go_obj, "cell_type_annotation")
dme_go_obj_analyzed = analyzeGOSeurat(dme_go_obj, "annotation")

getCellTypeSharedGO(species_1 = 'mmusculus',
species_2 = 'dmelanogaster',
analyzed_go_seurat_sp1 =  mmu_go_obj_analyzed,
analyzed_go_seurat_sp2 =  dme_go_obj_analyzed,
cell_type_col_sp1 = 'cell_type_annotation',
cell_type_col_sp2 = 'annotation',
layer_use = "data",
p_val_threshould = 0.01)

query co-up and co-down regulated GO terms from certain cell type pairs

Description

query co-up and co-down regulated GO terms from certain cell type pairs

Usage

getCellTypeSharedTerms(
  shared_go,
  cell_type_sp1,
  cell_type_sp2,
  return_full = FALSE,
  arrange_avg_log2FC = TRUE
)

Arguments

shared_go

cell type shared GO table from getCellTypeSharedGO
cell_type_sp1

cell type from sp1 to query
cell_type_sp2

cell type from sp2 to query
return_full

if return also pvals and logfc info, default FALSE
arrange_avg_log2FC

arrange result by decreasing mean avg_log2FC, default TRUE

Value

a dataframe displaying co-up or co-down regulated GO terms for the queried cell type pair

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
data(dme_tbl)
data(dme_subset)

mmu_go_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")
dme_go_obj = makeGOSeurat(ensembl_to_GO = dme_tbl,
 seurat_obj = dme_subset,
 feature_type = "external_gene_name")


mmu_go_obj_analyzed = analyzeGOSeurat(mmu_go_obj, "cell_type_annotation")
dme_go_obj_analyzed = analyzeGOSeurat(dme_go_obj, "annotation")

shared_go = getCellTypeSharedGO(species_1 = 'mmusculus',
species_2 = 'dmelanogaster',
analyzed_go_seurat_sp1 = mmu_go_obj_analyzed,
analyzed_go_seurat_sp2 = dme_go_obj_analyzed,
cell_type_col_sp1 = 'cell_type_annotation',
cell_type_col_sp2 = 'annotation',
layer_use = "data",
p_val_threshould = 0.01)


getCellTypeSharedTerms(shared_go = shared_go,
cell_type_sp1 = 'intestine_Enteroendocrine cell',
cell_type_sp2 = 'enteroendocrine cell',
return_full = FALSE)

record some global variables: pre-defined column name in biomaRt query and markers

Description

record some global variables: pre-defined column name in biomaRt query and markers

create a seurat object with GO terms

Description

create a seurat object with GO terms

Usage

makeGOSeurat(ensembl_to_GO, seurat_obj, feature_type = "ensembl_gene_id")

Arguments

ensembl_to_GO

ensembl_to_go mapping table from function ensemblToGo
seurat_obj

count matrix with genes to cells
feature_type

feature type of count matrix, choose from ensembl_gene_id, external_gene_name, default ensembl_gene_id

Value

a seurat object with GO terms as features

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")

Mouse stomach and intestine scRNA-seq data, microwell-seq Subset to 50 cells per cell type as an example data

Description

Mouse stomach and intestine scRNA-seq data, microwell-seq Subset to 50 cells per cell type as an example data

Usage

mmu_subset

Format

a 'Seurat' object

Source

<https://bis.zju.edu.cn/MCA/>

Mouse EMSEMBL gene and GO annotation, subset to genes present in 'mmu_subset'

Description

Mouse EMSEMBL gene and GO annotation, subset to genes present in 'mmu_subset'

Usage

mmu_tbl

Format

a 'data.frame' object

Source

<http://www.ensembl.org/>

plot clustered heatmap for cell type corr

Description

plot clustered heatmap for cell type corr

Usage

plotCellTypeCorrHeatmap(corr_matrix, scale = NA, ...)

Arguments

corr_matrix

correlation matrix from cellTypeGOCorr or crossSpeciesCellTypeGOCorr
scale

scale value by column, row, or default no scaling (NA)
...

params to pass to slanter::sheatmap

Value

a sheatmap heatmap

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)

go_seurat_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")

cell_type_go = getCellTypeGO(go_seurat_obj = go_seurat_obj, cell_type_co = "cell_type_annotation")

corr_matrix = cellTypeGOCorr(cell_type_go = cell_type_go, corr_method = "pearson")

plotCellTypeCorrHeatmap(corr_matrix = corr_matrix, scale = "column")

plot Sankey diagram for cell type links above a certain threshould

Description

plot Sankey diagram for cell type links above a certain threshould

Usage

plotCellTypeSankey(corr_matrix, corr_threshould = 0.1, ...)

Arguments

corr_matrix

cell type corr matrix from crossSpeciesCellTypeGOCorr
corr_threshould

minimum corr value for positively related cell types, default 0.6
...

additional params for sankeyNetwork

Value

a Sankey plot showing related cell types

Examples

library(scGOclust)
library(httr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
data(mmu_tbl)
data(mmu_subset)
go_seurat_obj = makeGOSeurat(ensembl_to_GO = mmu_tbl,
 seurat_obj = mmu_subset,
 feature_type = "external_gene_name")

cell_type_go = getCellTypeGO(go_seurat_obj = go_seurat_obj, cell_type_co = "cell_type_annotation")
corr_matrix = cellTypeGOCorr(cell_type_go = cell_type_go, corr_method = "pearson")

plotCellTypeSankey(corr_matrix = corr_matrix, 0.1)