inst/doc/scGOclust_vignette.R

In scGOclust: Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----install and load packages------------------------------------------------
# load required libraries

library(Seurat)
library(pheatmap)
library(httr)

## if (!require("devtools")) install.packages("devtools")

## install latest from source
## for reproducibility we do not update dependencies
# devtools::install_github("Papatheodorou-Group/scGOclust", upgrade = FALSE)

library(scGOclust)


## ----load_input---------------------------------------------------------------
# get a gene to GO BP terms mapping table
# remove electronically inferred records

# sometimes ensembl complains about ssh certificate has expired, this is a known issue, run this code
httr::set_config(httr::config(ssl_verifypeer = FALSE)) 

#mmu_tbl = ensemblToGo(species = 'mmusculus', GO_linkage_type = c('experimental', 'phylogenetic', 'computational', 'author', 'curator' ))
#dme_tbl = ensemblToGo(species = 'dmelanogaster', GO_linkage_type = c('experimental', 'phylogenetic', 'computational', 'author', 'curator' ))

# here we load the example data for convenience
data(mmu_tbl)
data(dme_tbl)

## -----------------------------------------------------------------------------
# load the gene expression raw count objects
data(mmu_subset)
data(dme_subset)
ls()

## ----build_GO_BP_profile------------------------------------------------------
## construct a Seurat object with GO BP as features

mmu_go_obj <- makeGOSeurat(ensembl_to_GO = mmu_tbl, feature_type = 'external_gene_name', seurat_obj = mmu_subset)

dme_go_obj <- makeGOSeurat(ensembl_to_GO = dme_tbl, feature_type = 'external_gene_name', seurat_obj = dme_subset)


## ----cell_type_BP-------------------------------------------------------------
# specify the column with cell type annotation in seurat_obj@meta.data

mmu_ct_go <- getCellTypeGO(go_seurat_obj = mmu_go_obj, cell_type_col = 'cell_type_annotation')
dme_ct_go <- getCellTypeGO(go_seurat_obj = dme_go_obj, cell_type_col = 'annotation')


## ----cell_type_corr_within, fig.width = 6,  fig.height = 6--------------------
# heatmap of Pearson's correlation coefficient of cell type average BP profiles within species

mmu_corr = cellTypeGOCorr(cell_type_go = mmu_ct_go, corr_method = 'pearson')
pheatmap(mmu_corr)

## ----fig.height = 6, fig.width = 6--------------------------------------------
dme_corr = cellTypeGOCorr(cell_type_go = dme_ct_go, corr_method = 'pearson')
pheatmap(dme_corr)

## ----cell_type_corr_cross-----------------------------------------------------

# calculate Pearson's correlation coefficient of cell type average BP profiles across species

corr = crossSpeciesCellTypeGOCorr(species_1 = 'mmusculus', species_2 = 'dmelanogaster', cell_type_go_sp1 = mmu_ct_go, cell_type_go_sp2 = dme_ct_go, corr_method = 'pearson')


## ----heatmap_corr_cross, fig.width = 7,  fig.height = 8-----------------------

# tries to put cells with higher values on the diagonal
# helpful when cross-species cell type similarity signal is less clear

plotCellTypeCorrHeatmap(corr, width = 9, height = 10)

# scale per column or row to see the relative similarity
plotCellTypeCorrHeatmap(corr, scale = 'column', width = 9, height = 10)



## ----analyze_GO---------------------------------------------------------------

# analyze the cell-by-GO BP profile as a count matrix
# Note that the example data has very few cells (for reducing data size), so I am using a small UMAP min_dist and small spread here. Default min_dist is 0.3 and spread is 1.

mmu_go_analyzed = analyzeGOSeurat(go_seurat_obj = mmu_go_obj, cell_type_col = 'cell_type_annotation', min.dist = 0.1, spread = 0.5)

## ----fig.width = 6, fig.height = 6--------------------------------------------
# UMAP plot of the analyzed cell-by-GO BP profile
# labeled by previously specified cell annotation column in meta.data

DimPlot(mmu_go_analyzed, label = TRUE) + NoLegend()

## -----------------------------------------------------------------------------
dme_go_analyzed = analyzeGOSeurat(go_seurat_obj = dme_go_obj, cell_type_col = 'annotation', min_dist=0.1, spread=0.5)

## ----fig.width = 6, fig.height = 6--------------------------------------------
DimPlot(dme_go_analyzed, label = TRUE) + NoLegend()

## ----shared_go, eval = FALSE--------------------------------------------------
#  
#  ## calculation takes a few minutes due to the Wilcox signed rank test
#  
#  ct_shared_go = scGOclust::getCellTypeSharedGO(species_1 = 'mmusculus', species_2 = 'dmelanogaster', analyzed_go_seurat_sp1 = mmu_go_analyzed, analyzed_go_seurat_sp2 = dme_go_analyzed, cell_type_col_sp1 = 'cell_type_annotation', cell_type_col_sp2 = 'annotation', layer_use = 'data')

## ----view_shared_go, eval = FALSE---------------------------------------------
#  head(ct_shared_go$shared_sig_markers)

## ----shared go cell type, eval = FALSE----------------------------------------
#  
#  # query shared GO terms for specific cell type pairs
#  
#  getCellTypeSharedTerms(shared_go = ct_shared_go,
#                         cell_type_sp1 = 'intestine_Enteroendocrine cell',
#                         cell_type_sp2 = 'enteroendocrine cell',
#                         return_full = FALSE)
#  
#  

## ----sessioninfo--------------------------------------------------------------

# viola
sessionInfo()