Gene (and miRNA target) enrichment

Search of validated miRNA targets and Gene Enrichment

0. Call interesting packages

Check for proper loading of all packages. Otherwise, look for the vignettes and install the packages adequately.

#if (!require("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")

#BiocManager::install("EWCE",dependencies=T)
#BiocManager::install("multiMiR",dependencies=T)
#BiocManager::install("celldex",dependencies=T)

library(multiMiR)
library(EWCE)
library(celldex)
library(ggplot2)

Elapsed time: 12.886 seconds

1. Set a list of relevant miRNAs

For this example, we use a random list of murine microRNAs saved on the miRs object. All microRNAs are reported as MIMAT codes. For more information take a look at the miRbase database

miRs<-c("MIMAT0000521","MIMAT0000522","MIMAT0000523","MIMAT0000383","MIMAT0000524",
        "MIMAT0000525","MIMAT0000121","MIMAT0000122","MIMAT0000123","MIMAT0000655",
        "MIMAT0000133","MIMAT0000616","MIMAT0000546","MIMAT0004856","MIMAT0000385",
        "MIMAT0000386","MIMAT0000647","MIMAT0000648","MIMAT0000208","MIMAT0005836",
        "MIMAT0015644","MIMAT0005837","MIMAT0005843","MIMAT0005847","MIMAT0005849",
        "MIMAT0005850","MIMAT0005852","MIMAT0005856")

2. Look for targets

We will use the function get_multimir to get the corresponding miRNA-gene database for selected miRNAs. Please check that we can opt for validated (table = 'validated') or predicted targets.

multimir_results <- get_multimir(org     = 'mmu', #Organism-dependent (Mus musculus - mmu)
                                 mirna   = miRs,
                                 table   = 'validated',
                                 summary = TRUE)

## Searching mirecords ...
## Searching mirtarbase ...
## Searching tarbase ...

Elapsed time: 2.224 seconds

3. Download expression data of immune cells in murine models

In this step, we will download data from the ImmGenData and set two information levels (‘main’ and ‘fine’).

#Download data
ref.se <- celldex::ImmGenData(cell.ont = "all") 
ref.se@NAMES->rownames(ref.se@assays@data$logcounts)

#Correct gene names
ref.se@assays@data$logcounts = fix_bad_mgi_symbols(ref.se@assays@data$logcounts)

## Warning in fix_bad_mgi_symbols(ref.se@assays@data$logcounts): Possible presence
## of excel corrupted date-like genes: Marcksl1-ps3

#Generate celltype
annotLevels <- list(level1class=ref.se@colData$label.main,
                    level2class=ref.se@colData$label.fine)


fNames_immgen <- EWCE::generate_celltype_data(
  exp =  ref.se@assays@data$logcounts,
  annotLevels = annotLevels,
  groupName = "ImmGenData") 

ctd_immGenData <- EWCE::load_rdata(fNames_immgen)

Elapsed time: 1.174 seconds

# Test first plot
plt_exp <- EWCE::plot_ctd(ctd = ctd_immGenData,
                    level = 1,
                    genes = unique(multimir_results@data$target_symbol)[1:10],
                    metric = "mean_exp")

Elapsed time: 0.88 seconds

4. miRNA targets enrichment using immune cell types

Now, we will look for enriched cell types in our miRNA target list.

### Looking for enriched cell lines
rm(merged_results)

## Warning in rm(merged_results): objeto 'merged_results' não encontrado

for(i in 1:length(miRs)){ ## Check for errors in annotation
  print(i)
  length(unique(multimir_results@data[which(multimir_results@data$mature_mirna_acc==miRs[i]),]$target_symbol)%in% rownames(ctd_immGenData[[1]]$mean_exp))  
  
  if(length(unique(multimir_results@data[which(multimir_results@data$mature_mirna_acc==miRs[i]),]$target_symbol))>4){
    full_results <- EWCE::bootstrap_enrichment_test(sct_data = ctd_immGenData,verbose=F,
                                                    sctSpecies = "mouse",
                                                    genelistSpecies = "mouse",
                                                    hits = unique(multimir_results@data[which(multimir_results@data$mature_mirna_acc==miRs[i]),]$target_symbol), 
                                                    reps = 100,
                                                    annotLevel = 1 ##CHECK 1 or 2
                                                       )
    
    if(!exists("merged_results")){
      
      data.frame(full_results$results,
                 list=miRs[i])->merged_results
    }else{
      
      rbind(merged_results,data.frame(full_results$results,
                                      list=miRs[i]))->merged_results
      
    }
  }
}

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Elapsed time: 3.244 seconds

knitr::kable(full_results$results)

	CellType	annotLevel	p	fold_change	sd_from_mean	q
Stem_cells	Stem_cells	1	0.00	1.0184441	4.4574324	0.0000000
B_cells	B_cells	1	0.00	1.0185012	4.2899707	0.0000000
T_cells	T_cells	1	0.01	1.0104626	2.4567476	0.0666667
Tgd	Tgd	1	0.02	1.0084925	2.0867991	0.1000000
Mast_cells	Mast_cells	1	0.04	1.0106219	1.7912822	0.1600000
B_cells,_pro	B_cells,_pro	1	0.09	1.0063710	1.3644206	0.3000000
NKT	NKT	1	0.16	1.0047384	1.0031300	0.4571429
DC	DC	1	0.19	1.0031293	0.8568343	0.4750000
Macrophages	Macrophages	1	0.40	1.0009862	0.2219607	0.8000000
NK_cells	NK_cells	1	0.40	1.0007520	0.1670678	0.8000000
Endothelial_cells	Endothelial_cells	1	0.49	0.9998331	-0.0260453	0.8909091
Epithelial_cells	Epithelial_cells	1	0.60	0.9975150	-0.3423916	0.9800000
Monocytes	Monocytes	1	0.84	0.9950521	-1.1065752	0.9800000
ILC	ILC	1	0.90	0.9951552	-1.2918978	0.9800000
Neutrophils	Neutrophils	1	0.95	0.9904220	-1.5749126	0.9800000
Fibroblasts	Fibroblasts	1	0.95	0.9891905	-1.5850545	0.9800000
Microglia	Microglia	1	0.96	0.9889706	-1.9896712	0.9800000
Stromal_cells	Stromal_cells	1	0.97	0.9852485	-1.9237129	0.9800000
Basophils	Basophils	1	0.97	0.9878214	-2.0302665	0.9800000
Eosinophils	Eosinophils	1	0.98	0.9885307	-2.2234537	0.9800000

Finally, let’s plot our enrichment graph

# For the last miRNA evaluated
EWCE::ewce_plot(total_res = full_results$results,
                mtc_method = "BH",
                ctd = ctd_immGenData,make_dendro = F)

## $plain

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# Summarizing all findings

merged_results%>%
  dplyr::filter(CellType %in% unique(merged_results[which(merged_results$q<0.01),]$CellType),
                list %in% unique(merged_results[which(merged_results$q<0.01),]$list))%>%
  dplyr::mutate(q=p.adjust(p,"BH"),
                abs_sd=abs(sd_from_mean),
                y_ast=sd_from_mean*1.05,
                ast_q=ifelse(q<0.001,"*",
                             ifelse(q<0.01,"",ifelse(q<0.05,"",""))))%>%
  ggplot()+
  geom_hline(yintercept = 4,color="#E1E1E1",linetype="dashed") + 
  geom_bar(aes_string(x='CellType',y='abs_sd',fill="abs_sd"),stat="identity") +
  theme_bw(base_size = 12, base_family = "Helvetica") +
  theme(panel.grid.major = element_line(size = .5, color = "white"),
        axis.line = element_line(size=.7, color = "black"), text = element_text(size=14),
        axis.title.y = element_text(vjust = 0.6))+
  #theme(plot.margin=unit(c(0,0,0,0),"mm"))+
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1),
        axis.text.x = element_text(angle = 55, hjust = 1,size=10))+
  xlab("") + 
  geom_text(aes_string(label="ast_q",x="CellType",y="y_ast"),size=6)+
  scale_y_continuous(breaks=c(0,4,8),limits = c(0,10)) +
  theme(strip.text.y = element_text(angle = 0),strip.background =element_rect(fill="white")) +
  #coord_cartesian(ylim = c(0,1.1*upperLim))+
  ylab("Std.Devs. from the mean") + 
  theme(plot.margin = unit(c(1,1,1,5), "cm"))+ # + theme(legend.position="none")
  facet_grid("list ~ .",scales="free", space = "free_x")+
  paletteer::scale_fill_paletteer_c("viridis::plasma")+
  labs(fill="Absolute \nvalues\n (Std. Dev.)")->p

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p

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