In fluentin44/epicR: A package to generate reports for BMIQ pipeline
knitr::opts_chunk$set(echo = TRUE)
#setwd("E:/BMIQ MAV/19-07-30-split_cohort_and_run_season/reports/tG_summer")
knitr::opts_knit$set(root.dir = here::here())
getwd()
library(GenABEL)
library(ggplot2)
library(missMethyl)
library(limma)
library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
library(DMRcate)
library(tidyverse)
library(kableExtra)
library(formattable)
library(Hmisc)
dir.create(paste0("./reports", "/", params$name))
data_output <- paste0("./reports", "/", params$name, "/")
raw_data <- paste0(params$raw_results)
combat_data_raw <- readRDS(paste0(params$combat_data))
pheno_nona_raw <- readRDS(paste0(params$pheno_nona))
pheno_forpcs <- readRDS(paste0(params$pheno_forpcs))
corrplot <- read.table(paste0(params$corr_plot),
sep = "\t",
header=T
)
###### ALSO FILL IN SvA PLOT PATH BELOW #######
cols<-c(
"Row.names",
"logFC",
"AveExpr",
"t",
"P.Value",
"adj.P.Val",
"B",
"chr",
"pos",
"strand",
"Name",
"Relation_to_Island",
"UCSC_RefGene_Name",
"UCSC_RefGene_Accession",
"UCSC_RefGene_Group",
"Regulatory_Feature_Group"
)
# # For tables saved as NON_TSV
# var_raw<-read_tsv(paste0(raw_data),
# col_names = F,
# skip = 1,
# na = character()
# )
# var<-var_raw[,-1]
# For tables saved as TSV
var_raw<-readr::read_tsv(paste0(raw_data),
col_names = T,
na = character()
)
var<-var_raw
# ----------------------------------------------------------
#colnames(var)<-cols
var <- var %>% arrange(adj.P.Val) %>% rowid_to_column("Rank")
var$UCSC_RefGene_Name<-sub(";.*", "", var$UCSC_RefGene_Name)
var$UCSC_RefGene_Group<-sub(";.*", "", var$UCSC_RefGene_Group)
Lambdas & covariates {.tabset .tabset-fade .tabset-pills}
Lambdas
result_list <- tibble(
"analysis" = params$name,
"pcs" = params$pcs,
"samples" = nrow(pheno_nona_raw),
"Lambda" = (estlambda(var$P.Value, method = "median"))$estimate,
"FDR<0.25" = sum(var$adj.P.Val < 0.25),
"FDR<0.20" = sum(var$adj.P.Val < 0.20),
"FDR<0.15" = sum(var$adj.P.Val < 0.15),
"FDR<0.10" = sum(var$adj.P.Val < 0.10),
"FDR<0.05" = sum(var$adj.P.Val < 0.05)
)
result_list
Covariates
tibble::enframe(colnames(pheno_forpcs)) %>%
slice(-1) %>%
select(-"name") %>%
rowid_to_column(var = "Number") %>%
rename("Covariate" = "value")
Top 500 array hits
var %>%
#mutate_at(7, funs(round(., 3))) %>%
arrange(adj.P.Val) %>%
dplyr::slice(1:500) %>%
DT::datatable(
filter = 'top',
rownames = FALSE,
extensions = list('FixedColumns' = NULL,
'Buttons' = NULL),
options = list(pageLength = 10,
dom = 'Bfrtip',
scrollX = TRUE,
fixedColumns = TRUE,
columnDefs = list(
list(
className = 'dt-center',
targets = 0:(ncol(var)-1))
),
buttons = list(
list(
extend = 'colvisGroup',
text = 'simplified',
show = c(0,2,3,6,7,13,15),
hide = c(1,4,5,8,9,10,11,12,14,16)
),
list(
extend = 'colvisGroup',
text = 'show all',
show = c(0:16)
),
list(
extend = 'colvis',
text = 'see columns',
collectionLayout= 'fixed two-column'
)
)
)
) %>%
DT::formatRound(c('logFC', 'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B'), 5) %>%
DT::formatStyle(columns = c(1:14, 16:17), `text-align` = 'center') %>%
DT::formatStyle(
'adj.P.Val',
#color = DT::styleInterval(0.25, c('black', 'Black')),
backgroundColor = DT::styleInterval(0.25, c('#b3de69', '')),
fontWeight = DT::styleInterval(0.25, c('bold', ''))
) %>%
DT::formatStyle(
'AveExpr',
backgroundColor = DT::styleInterval(0.80, c('', '#fbb4ae')),
fontWeight = DT::styleInterval(0.80, c('', 'bold'))
)
Top 100 named hits {.tabset .tabset-fade .tabset-pills}
Top 100 named genes
named_hits <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),]
top100_named_genes <-
named_hits %>%
arrange(adj.P.Val) %>%
slice(1:100) %>%
dplyr::select(UCSC_RefGene_Name)
top100_named_genes %>%
dplyr::rename("Top 100 genes" = "UCSC_RefGene_Name") %>%
#mutate_at(7, funs(round(., 3))) %>%
DT::datatable(
filter = 'top',
rownames = FALSE,
extensions = list('FixedColumns' = NULL,
'Buttons' = NULL),
options = list(pageLength = 10,
dom = 'Brtip',
fixedColumns = TRUE,
columnDefs = list(
list(
className = 'dt-center',
targets = "_all")
),
buttons = 'copy'
)
)
Top 100 cgs from named hits
top100_cgs <-
named_hits %>%
arrange(adj.P.Val) %>%
slice(1:100) %>%
dplyr::select(Row.names)
top100_cgs %>%
dplyr::rename("Top 100 cgs" = "Row.names") %>%
#mutate_at(7, funs(round(., 3))) %>%
DT::datatable(
filter = 'top',
rownames = FALSE,
extensions = list('FixedColumns' = NULL,
'Buttons' = NULL),
options = list(pageLength = 10,
dom = 'Brtip',
fixedColumns = TRUE,
columnDefs = list(
list(
className = 'dt-center',
targets = "_all")
),
buttons = 'copy'
)
)
Significant named genes
named_hits <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),]
sig_genenames <-
named_hits %>%
arrange(adj.P.Val) %>%
filter(adj.P.Val <0.25) %>%
dplyr::select(UCSC_RefGene_Name)
sig_genenames %>%
dplyr::rename("Significant hits associated with named genes" = "UCSC_RefGene_Name") %>%
#mutate_at(7, funs(round(., 3))) %>%
DT::datatable(
filter = 'top',
rownames = FALSE,
extensions = list('FixedColumns' = NULL,
'Buttons' = NULL),
options = list(pageLength = 10,
dom = 'Brtip',
fixedColumns = TRUE,
columnDefs = list(
list(
className = 'dt-center',
targets = "_all")
),
buttons = 'copy'
)
)
Significant cgs from named hits
sig_cgs <-
var %>%
arrange(adj.P.Val) %>%
filter(adj.P.Val <0.25) %>%
dplyr::select(Row.names)
sig_cgs %>%
dplyr::rename("Significant cgs" = "Row.names") %>%
DT::datatable(
filter = 'top',
rownames = FALSE,
extensions = list('FixedColumns' = NULL,
'Buttons' = NULL),
options = list(pageLength = 10,
dom = 'Brtip',
fixedColumns = TRUE,
columnDefs = list(
list(
className = 'dt-center',
targets = "_all")
),
buttons = 'copy'
)
)
Universe (without duplicates)
var_univ <- var_raw[!(is.na(var_raw$UCSC_RefGene_Name) | var_raw$UCSC_RefGene_Name == ""),]
var_univ$UCSC_RefGene_Name <- sub(";.*", "", var_univ$UCSC_RefGene_Name)
gene_names <- unique(var_univ$UCSC_RefGene_Name)
gene_names <- tibble::enframe(gene_names, name = NULL, value = "Universe")
readr::write_csv(gene_names, "./universe.csv", col_names = T)
Lambdas and qqplot {.tabset .tabset-fade .tabset-pills}
qqplot and nominal p.values
var_ps <- var$P.Value
gg_qqplot <- function(var_ps, ci = 0.95) {
n <- length(var_ps)
df <- data.frame(
observed = -log10(sort(var_ps)),
expected = -log10(ppoints(n)),
clower = -log10(qbeta(p = (1 - ci) / 2, shape1 = 1:n, shape2 = n:1)),
cupper = -log10(qbeta(p = (1 + ci) / 2, shape1 = 1:n, shape2 = n:1))
)
log10Pe <- expression(paste("Expected -log"[10], plain(P)))
log10Po <- expression(paste("Observed -log"[10], plain(P)))
ggplot(df) +
geom_point(aes(expected, observed), shape = 1, size = 3) +
geom_abline(intercept = 0, slope = 1, alpha = 0.5) +
geom_line(aes(expected, cupper), linetype = 2) +
geom_line(aes(expected, clower), linetype = 2) +
xlab(log10Pe) +
ylab(log10Po) +
ggtitle("qqplot")
}
gg_qqplot(var_ps)
ggplot(var, aes(x=P.Value)) +
geom_histogram() +
ggtitle("P-values: var")
Correlation plot
corrplot %>%
rownames_to_column() %>%
DT::datatable(filter = 'top',
rownames = FALSE,
extensions = 'FixedColumns',
options = list(
pageLength = nrow(corrplot),
dom = 't',
scrollX = TRUE,
fixedColumns = TRUE
)
) %>%
DT::formatSignif((colnames(corrplot)), digits = 3) %>%
DT::formatStyle(columns = 2:ncol(corrplot), `text-align` = 'center') %>%
DT::formatStyle((colnames(corrplot)),
#color = DT::styleInterval(0.25, c('black', 'Black')),
backgroundColor = DT::styleInterval(0.05, c('#fb9a99', '#b2df8a'))
#fontWeight = DT::styleInterval(0.25, c('', ''))
)
Other plots
Pie chart data {.tabset .tabset-fade .tabset-pills}
## Relation to island
pie_rel_to_island_all <-
var %>%
count(Relation_to_Island) %>%
arrange(desc(n))
pie_region_100 <-
var[!(is.na(var$Relation_to_Island) | var$Relation_to_Island == ""),]
pie_rel_to_island_100 <-
pie_region_100 %>%
arrange(adj.P.Val) %>%
dplyr::slice(1:100) %>%
dplyr::select(Relation_to_Island) %>%
count(Relation_to_Island) %>%
arrange(desc(n))
#rename(Relation_to_Island_100 = Relation_to_Island) %>%
## RefGene group
pie_ref_group_all <-
var %>%
count(UCSC_RefGene_Group) %>%
arrange(desc(n))
pie_ref_group_all$UCSC_RefGene_Group<-sub("^$", "Unknown", pie_ref_group_all$UCSC_RefGene_Group)
pie_region_100 <-
var[!(is.na(var$UCSC_RefGene_Group) | var$UCSC_RefGene_Group == ""),]
pie_ref_group_100 <-
pie_region_100 %>%
arrange(adj.P.Val) %>%
dplyr::slice(1:100) %>%
dplyr::select(UCSC_RefGene_Group) %>%
count(UCSC_RefGene_Group) %>%
arrange(desc(n))
#rename(UCSC_RefGene_Group_100 = UCSC_RefGene_Group) %>%
pie_ref_group_100$UCSC_RefGene_Group<-sub("^$", "Unknown", pie_ref_group_100$UCSC_RefGene_Group)
Relationship to island
colours <- list('#b3de69','#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#fccde5')
plotly::plot_ly() %>%
plotly::add_pie(data = pie_rel_to_island_100,
labels = ~ Relation_to_Island,
values = ~ n,
type = "pie",
#title = list(text = "bums",
# position = "top right",
# font = list(size = 40)),
textposition = "outside",
textinfo = "label+percent",
outsidetextfont = list(family = "Helvetica",
size = 14),
marker = list(colors = colours,
line = list(color = '#FFFFFF', width = 3)),
showlegend = FALSE,
sort = TRUE,
direction = "clockwise",
domain = list(x = c(0, 0.35), y = c(0, 0.7))
) %>%
plotly::add_pie(data = pie_rel_to_island_all,
labels = ~ Relation_to_Island,
values = ~ n,
type = "pie",
#title = list(text = "bums",
# position = "top right",
# font = list(size = 40)),
textposition = "outside",
textinfo = "label+percent",
outsidetextfont = list(family = "Helvetica",
size = 14),
marker = list(colors = colours,
line = list(color = '#FFFFFF', width = 3)),
showlegend = FALSE,
sort = TRUE,
direction = "clockwise",
domain = list(x = c(0.60, 0.95), y = c(0, 0.7))
)
Refgene group
colours <- list('#b3de69','#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#fccde5')
plotly::plot_ly() %>%
plotly::add_pie(data = pie_ref_group_100,
labels = ~ UCSC_RefGene_Group,
values = ~ n,
type = "pie",
#title = list(text = "bums",
# position = "top right",
# font = list(size = 40)),
textposition = "outside",
textinfo = "label+percent",
outsidetextfont = list(family = "Helvetica",
size = 14),
marker = list(colors = colours,
line = list(color = '#FFFFFF', width = 3)),
showlegend = FALSE,
sort = TRUE,
direction = "clockwise",
domain = list(x = c(0, 0.35), y = c(0, 0.7))
) %>%
plotly::add_pie(data = pie_ref_group_all,
labels = ~ UCSC_RefGene_Group,
values = ~ n,
type = "pie",
#title = list(text = "bums",
# position = "top right",
# font = list(size = 40)),
textposition = "outside",
textinfo = "label+percent",
outsidetextfont = list(family = "Helvetica",
size = 14),
marker = list(colors = colours,
line = list(color = '#FFFFFF', width = 3)),
showlegend = FALSE,
sort = TRUE,
direction = "clockwise",
domain = list(x = c(0.60, 0.95), y = c(0, 0.7))
)
missMethyl {.tabset .tabset-pills}
Universe<-var$Row.names
Sig_0.25<-var$Row.names[var$adj.P.Val<0.25]
top100_named_genes <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),]
top_100_cpgs <- top100_named_genes %>% arrange(adj.P.Val) %>% dplyr::slice(1:100) %>% pull(Row.names)
#FDR<0.25
results_0.25_go <- epicR::mm_sig_cpgs(Sig_0.25)
## 1:100
#top_100_cpgs<-as.character(top_100_cpgs) Tests gene ontology enrichment for
#significant CpGs from Illumina's Infinium HumanMethylation450 or
#MethylationEPIC array, taking into account the differing number of probes per
#gene present on the array.
Universe<-as.character(Universe)
gst_top100_go<-gometh(sig.cpg=top_100_cpgs,
all.cpg = Universe,
collection = "GO",
array.type = "EPIC"
)
colnames(gst_top100_go)[2]<-"Term"
### Results 1:100 - topGO pulls the most significant from the pack
results_top100_go <- topGO(gst_top100_go, number = 50)
results_top100_go <-
results_top100_go %>%
arrange(ONTOLOGY)
Significant hits (FDR<0.25)
epicR::mm_plot_0.25(results_0.25_go)
Top 100 cpgs
results_top100_go %>%
DT::datatable(filter = 'top',
rownames = FALSE,
extensions = 'FixedColumns',
options = list(
pageLength = 10,
dom = 'tpf',
scrollX = TRUE,
fixedColumns = TRUE
)
) %>%
DT::formatStyle(columns = c("ONTOLOGY", "N", "DE", "P.DE", "FDR"),
`text-align` = 'center') %>%
DT::formatSignif(columns = c("P.DE", "FDR"), digits = 3)
fluentin44/epicR documentation built on June 29, 2022, 6:39 a.m.
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knitr::opts_chunk$set(echo = TRUE) #setwd("E:/BMIQ MAV/19-07-30-split_cohort_and_run_season/reports/tG_summer") knitr::opts_knit$set(root.dir = here::here()) getwd()
library(GenABEL) library(ggplot2) library(missMethyl) library(limma) library(IlluminaHumanMethylationEPICanno.ilm10b4.hg19) library(DMRcate) library(tidyverse) library(kableExtra) library(formattable) library(Hmisc)
dir.create(paste0("./reports", "/", params$name)) data_output <- paste0("./reports", "/", params$name, "/") raw_data <- paste0(params$raw_results) combat_data_raw <- readRDS(paste0(params$combat_data)) pheno_nona_raw <- readRDS(paste0(params$pheno_nona)) pheno_forpcs <- readRDS(paste0(params$pheno_forpcs)) corrplot <- read.table(paste0(params$corr_plot), sep = "\t", header=T ) ###### ALSO FILL IN SvA PLOT PATH BELOW ####### cols<-c( "Row.names", "logFC", "AveExpr", "t", "P.Value", "adj.P.Val", "B", "chr", "pos", "strand", "Name", "Relation_to_Island", "UCSC_RefGene_Name", "UCSC_RefGene_Accession", "UCSC_RefGene_Group", "Regulatory_Feature_Group" ) # # For tables saved as NON_TSV # var_raw<-read_tsv(paste0(raw_data), # col_names = F, # skip = 1, # na = character() # ) # var<-var_raw[,-1] # For tables saved as TSV var_raw<-readr::read_tsv(paste0(raw_data), col_names = T, na = character() ) var<-var_raw # ---------------------------------------------------------- #colnames(var)<-cols var <- var %>% arrange(adj.P.Val) %>% rowid_to_column("Rank") var$UCSC_RefGene_Name<-sub(";.*", "", var$UCSC_RefGene_Name) var$UCSC_RefGene_Group<-sub(";.*", "", var$UCSC_RefGene_Group)
Lambdas & covariates {.tabset .tabset-fade .tabset-pills}
Lambdas
result_list <- tibble( "analysis" = params$name, "pcs" = params$pcs, "samples" = nrow(pheno_nona_raw), "Lambda" = (estlambda(var$P.Value, method = "median"))$estimate, "FDR<0.25" = sum(var$adj.P.Val < 0.25), "FDR<0.20" = sum(var$adj.P.Val < 0.20), "FDR<0.15" = sum(var$adj.P.Val < 0.15), "FDR<0.10" = sum(var$adj.P.Val < 0.10), "FDR<0.05" = sum(var$adj.P.Val < 0.05) ) result_list
Covariates
tibble::enframe(colnames(pheno_forpcs)) %>% slice(-1) %>% select(-"name") %>% rowid_to_column(var = "Number") %>% rename("Covariate" = "value")
Top 500 array hits
var %>% #mutate_at(7, funs(round(., 3))) %>% arrange(adj.P.Val) %>% dplyr::slice(1:500) %>% DT::datatable( filter = 'top', rownames = FALSE, extensions = list('FixedColumns' = NULL, 'Buttons' = NULL), options = list(pageLength = 10, dom = 'Bfrtip', scrollX = TRUE, fixedColumns = TRUE, columnDefs = list( list( className = 'dt-center', targets = 0:(ncol(var)-1)) ), buttons = list( list( extend = 'colvisGroup', text = 'simplified', show = c(0,2,3,6,7,13,15), hide = c(1,4,5,8,9,10,11,12,14,16) ), list( extend = 'colvisGroup', text = 'show all', show = c(0:16) ), list( extend = 'colvis', text = 'see columns', collectionLayout= 'fixed two-column' ) ) ) ) %>% DT::formatRound(c('logFC', 'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B'), 5) %>% DT::formatStyle(columns = c(1:14, 16:17), `text-align` = 'center') %>% DT::formatStyle( 'adj.P.Val', #color = DT::styleInterval(0.25, c('black', 'Black')), backgroundColor = DT::styleInterval(0.25, c('#b3de69', '')), fontWeight = DT::styleInterval(0.25, c('bold', '')) ) %>% DT::formatStyle( 'AveExpr', backgroundColor = DT::styleInterval(0.80, c('', '#fbb4ae')), fontWeight = DT::styleInterval(0.80, c('', 'bold')) )
Top 100 named hits {.tabset .tabset-fade .tabset-pills}
Top 100 named genes
named_hits <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),] top100_named_genes <- named_hits %>% arrange(adj.P.Val) %>% slice(1:100) %>% dplyr::select(UCSC_RefGene_Name)
top100_named_genes %>% dplyr::rename("Top 100 genes" = "UCSC_RefGene_Name") %>% #mutate_at(7, funs(round(., 3))) %>% DT::datatable( filter = 'top', rownames = FALSE, extensions = list('FixedColumns' = NULL, 'Buttons' = NULL), options = list(pageLength = 10, dom = 'Brtip', fixedColumns = TRUE, columnDefs = list( list( className = 'dt-center', targets = "_all") ), buttons = 'copy' ) )
Top 100 cgs from named hits
top100_cgs <- named_hits %>% arrange(adj.P.Val) %>% slice(1:100) %>% dplyr::select(Row.names)
top100_cgs %>% dplyr::rename("Top 100 cgs" = "Row.names") %>% #mutate_at(7, funs(round(., 3))) %>% DT::datatable( filter = 'top', rownames = FALSE, extensions = list('FixedColumns' = NULL, 'Buttons' = NULL), options = list(pageLength = 10, dom = 'Brtip', fixedColumns = TRUE, columnDefs = list( list( className = 'dt-center', targets = "_all") ), buttons = 'copy' ) )
Significant named genes
named_hits <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),] sig_genenames <- named_hits %>% arrange(adj.P.Val) %>% filter(adj.P.Val <0.25) %>% dplyr::select(UCSC_RefGene_Name)
sig_genenames %>% dplyr::rename("Significant hits associated with named genes" = "UCSC_RefGene_Name") %>% #mutate_at(7, funs(round(., 3))) %>% DT::datatable( filter = 'top', rownames = FALSE, extensions = list('FixedColumns' = NULL, 'Buttons' = NULL), options = list(pageLength = 10, dom = 'Brtip', fixedColumns = TRUE, columnDefs = list( list( className = 'dt-center', targets = "_all") ), buttons = 'copy' ) )
Significant cgs from named hits
sig_cgs <- var %>% arrange(adj.P.Val) %>% filter(adj.P.Val <0.25) %>% dplyr::select(Row.names)
sig_cgs %>% dplyr::rename("Significant cgs" = "Row.names") %>% DT::datatable( filter = 'top', rownames = FALSE, extensions = list('FixedColumns' = NULL, 'Buttons' = NULL), options = list(pageLength = 10, dom = 'Brtip', fixedColumns = TRUE, columnDefs = list( list( className = 'dt-center', targets = "_all") ), buttons = 'copy' ) )
Universe (without duplicates)
var_univ <- var_raw[!(is.na(var_raw$UCSC_RefGene_Name) | var_raw$UCSC_RefGene_Name == ""),] var_univ$UCSC_RefGene_Name <- sub(";.*", "", var_univ$UCSC_RefGene_Name) gene_names <- unique(var_univ$UCSC_RefGene_Name) gene_names <- tibble::enframe(gene_names, name = NULL, value = "Universe") readr::write_csv(gene_names, "./universe.csv", col_names = T)
Lambdas and qqplot {.tabset .tabset-fade .tabset-pills}
qqplot and nominal p.values
var_ps <- var$P.Value gg_qqplot <- function(var_ps, ci = 0.95) { n <- length(var_ps) df <- data.frame( observed = -log10(sort(var_ps)), expected = -log10(ppoints(n)), clower = -log10(qbeta(p = (1 - ci) / 2, shape1 = 1:n, shape2 = n:1)), cupper = -log10(qbeta(p = (1 + ci) / 2, shape1 = 1:n, shape2 = n:1)) ) log10Pe <- expression(paste("Expected -log"[10], plain(P))) log10Po <- expression(paste("Observed -log"[10], plain(P))) ggplot(df) + geom_point(aes(expected, observed), shape = 1, size = 3) + geom_abline(intercept = 0, slope = 1, alpha = 0.5) + geom_line(aes(expected, cupper), linetype = 2) + geom_line(aes(expected, clower), linetype = 2) + xlab(log10Pe) + ylab(log10Po) + ggtitle("qqplot") }
gg_qqplot(var_ps) ggplot(var, aes(x=P.Value)) + geom_histogram() + ggtitle("P-values: var")
Correlation plot
corrplot %>% rownames_to_column() %>% DT::datatable(filter = 'top', rownames = FALSE, extensions = 'FixedColumns', options = list( pageLength = nrow(corrplot), dom = 't', scrollX = TRUE, fixedColumns = TRUE ) ) %>% DT::formatSignif((colnames(corrplot)), digits = 3) %>% DT::formatStyle(columns = 2:ncol(corrplot), `text-align` = 'center') %>% DT::formatStyle((colnames(corrplot)), #color = DT::styleInterval(0.25, c('black', 'Black')), backgroundColor = DT::styleInterval(0.05, c('#fb9a99', '#b2df8a')) #fontWeight = DT::styleInterval(0.25, c('', '')) )
Other plots
Pie chart data {.tabset .tabset-fade .tabset-pills}
## Relation to island pie_rel_to_island_all <- var %>% count(Relation_to_Island) %>% arrange(desc(n)) pie_region_100 <- var[!(is.na(var$Relation_to_Island) | var$Relation_to_Island == ""),] pie_rel_to_island_100 <- pie_region_100 %>% arrange(adj.P.Val) %>% dplyr::slice(1:100) %>% dplyr::select(Relation_to_Island) %>% count(Relation_to_Island) %>% arrange(desc(n)) #rename(Relation_to_Island_100 = Relation_to_Island) %>% ## RefGene group pie_ref_group_all <- var %>% count(UCSC_RefGene_Group) %>% arrange(desc(n)) pie_ref_group_all$UCSC_RefGene_Group<-sub("^$", "Unknown", pie_ref_group_all$UCSC_RefGene_Group) pie_region_100 <- var[!(is.na(var$UCSC_RefGene_Group) | var$UCSC_RefGene_Group == ""),] pie_ref_group_100 <- pie_region_100 %>% arrange(adj.P.Val) %>% dplyr::slice(1:100) %>% dplyr::select(UCSC_RefGene_Group) %>% count(UCSC_RefGene_Group) %>% arrange(desc(n)) #rename(UCSC_RefGene_Group_100 = UCSC_RefGene_Group) %>% pie_ref_group_100$UCSC_RefGene_Group<-sub("^$", "Unknown", pie_ref_group_100$UCSC_RefGene_Group)
Relationship to island
colours <- list('#b3de69','#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#fccde5') plotly::plot_ly() %>% plotly::add_pie(data = pie_rel_to_island_100, labels = ~ Relation_to_Island, values = ~ n, type = "pie", #title = list(text = "bums", # position = "top right", # font = list(size = 40)), textposition = "outside", textinfo = "label+percent", outsidetextfont = list(family = "Helvetica", size = 14), marker = list(colors = colours, line = list(color = '#FFFFFF', width = 3)), showlegend = FALSE, sort = TRUE, direction = "clockwise", domain = list(x = c(0, 0.35), y = c(0, 0.7)) ) %>% plotly::add_pie(data = pie_rel_to_island_all, labels = ~ Relation_to_Island, values = ~ n, type = "pie", #title = list(text = "bums", # position = "top right", # font = list(size = 40)), textposition = "outside", textinfo = "label+percent", outsidetextfont = list(family = "Helvetica", size = 14), marker = list(colors = colours, line = list(color = '#FFFFFF', width = 3)), showlegend = FALSE, sort = TRUE, direction = "clockwise", domain = list(x = c(0.60, 0.95), y = c(0, 0.7)) )
Refgene group
colours <- list('#b3de69','#8dd3c7','#ffffb3','#bebada','#fb8072','#80b1d3','#fdb462','#fccde5') plotly::plot_ly() %>% plotly::add_pie(data = pie_ref_group_100, labels = ~ UCSC_RefGene_Group, values = ~ n, type = "pie", #title = list(text = "bums", # position = "top right", # font = list(size = 40)), textposition = "outside", textinfo = "label+percent", outsidetextfont = list(family = "Helvetica", size = 14), marker = list(colors = colours, line = list(color = '#FFFFFF', width = 3)), showlegend = FALSE, sort = TRUE, direction = "clockwise", domain = list(x = c(0, 0.35), y = c(0, 0.7)) ) %>% plotly::add_pie(data = pie_ref_group_all, labels = ~ UCSC_RefGene_Group, values = ~ n, type = "pie", #title = list(text = "bums", # position = "top right", # font = list(size = 40)), textposition = "outside", textinfo = "label+percent", outsidetextfont = list(family = "Helvetica", size = 14), marker = list(colors = colours, line = list(color = '#FFFFFF', width = 3)), showlegend = FALSE, sort = TRUE, direction = "clockwise", domain = list(x = c(0.60, 0.95), y = c(0, 0.7)) )
missMethyl {.tabset .tabset-pills}
Universe<-var$Row.names Sig_0.25<-var$Row.names[var$adj.P.Val<0.25] top100_named_genes <- var[!(is.na(var$UCSC_RefGene_Name) | var$UCSC_RefGene_Name == ""),] top_100_cpgs <- top100_named_genes %>% arrange(adj.P.Val) %>% dplyr::slice(1:100) %>% pull(Row.names) #FDR<0.25 results_0.25_go <- epicR::mm_sig_cpgs(Sig_0.25) ## 1:100 #top_100_cpgs<-as.character(top_100_cpgs) Tests gene ontology enrichment for #significant CpGs from Illumina's Infinium HumanMethylation450 or #MethylationEPIC array, taking into account the differing number of probes per #gene present on the array. Universe<-as.character(Universe) gst_top100_go<-gometh(sig.cpg=top_100_cpgs, all.cpg = Universe, collection = "GO", array.type = "EPIC" ) colnames(gst_top100_go)[2]<-"Term" ### Results 1:100 - topGO pulls the most significant from the pack results_top100_go <- topGO(gst_top100_go, number = 50) results_top100_go <- results_top100_go %>% arrange(ONTOLOGY)
Significant hits (FDR<0.25)
epicR::mm_plot_0.25(results_0.25_go)
Top 100 cpgs
results_top100_go %>% DT::datatable(filter = 'top', rownames = FALSE, extensions = 'FixedColumns', options = list( pageLength = 10, dom = 'tpf', scrollX = TRUE, fixedColumns = TRUE ) ) %>% DT::formatStyle(columns = c("ONTOLOGY", "N", "DE", "P.DE", "FDR"), `text-align` = 'center') %>% DT::formatSignif(columns = c("P.DE", "FDR"), digits = 3)
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