| Type: | Package |
| Title: | Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq |
| Version: | 0.2.1 |
| 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. |
| URL: | https://github.com/Papatheodorou-Group/scGOclust |
| BugReports: | https://github.com/Papatheodorou-Group/scGOclust/issues |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | bzip2 |
| RoxygenNote: | 7.2.3 |
| Imports: | limma, Seurat(≥ 5.0.0), biomaRt, dplyr, magrittr, stats, tibble, tidyr, Matrix, utils, networkD3, slanter |
| Suggests: | knitr, devtools, pheatmap, rmarkdown, httr |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2024-01-23 17:05:34 UTC; ysong |
| Author: | Yuyao Song [aut, cre, ctb], Irene Papatheodorou [aut, ths] |
| Maintainer: | Yuyao Song <ysong@ebi.ac.uk> |
| Repository: | CRAN |
| Date/Publication: | 2024-01-24 14:40:02 UTC |
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)