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app.R
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library(tidyverse)
library(shiny)
library(rsconnect)
library(plotly)
library(shinythemes)
library(ggpubr)
library(DT)
#Load data
good_pv <- read.csv("Data/Pv_STR_Top_Table.csv")
good_pv$Heterozygosity <- round(good_pv$Heterozygosity,3)
good_pf <- read.csv("Data/Pf_STR_Top_Table.csv")
good_pf$Heterozygosity <- round(good_pf$Heterozygosity,3)
#Population or Country pairs
compare_Pv_Country <- colnames(good_pv)[14:ncol(good_pv)]
compare_Pf_Country <- colnames(good_pf)[43:ncol(good_pf)]
compare_Pf_Population <- colnames(good_pf)[15:42]
#Plot data
#Pf Population
#Pf population loci plot
geno_pf <- read.csv("Data/Pf_Genotype.csv")
geno_origin_Pf <- geno_pf
Pf_pop_loci <- read.csv("Data/Pf_STR_Top_ShinyPlot.csv")
Pf_pop_loci <- good_pf %>%
filter(STRid %in% as.character(Pf_pop_loci$STRid))
Pf_pop_loci <- Pf_pop_loci %>%
pivot_longer(cols = CAF.EAF:WSEA.SAM) %>%
filter(is.na(value)==FALSE) %>%
arrange(value)
vars_pf_population <- setdiff(unique(Pf_pop_loci$value), "STRLoci")
geno_pf_population <- geno_origin_Pf
geno_pf_population <- geno_pf_population %>%
filter(STRid %in% as.character(Pf_pop_loci$STRid))
STR_Position <- Pf_pop_loci %>%
dplyr::select(STRid,value)
geno_pf_population <- STR_Position %>%
left_join(geno_pf_population,by="STRid")
geno_pf_population <- geno_pf_population[,-1]
geno_pf_population$value <- as.character(geno_pf_population$value)
geno_pf_population <- as.data.frame(geno_pf_population)
rownames(geno_pf_population) <- geno_pf_population$value
geno_pf_population <- geno_pf_population[,-1]
geno_pf_population <- t(geno_pf_population)
geno_pf_population <- data.frame(Sample=rownames(geno_pf_population),geno_pf_population)
pop_pf_population <- read.csv("Data/Pf_Sample.csv")
geno_pf_population <- geno_pf_population %>%
left_join(pop_pf_population,by="Sample")
vars_pf_population <- gsub(vars_pf_population,pattern="-",replacement=".")
#Pf Country
Pf_country_loci <- read.csv("Data/Pf_STR_Top_ShinyPlot.csv")
Pf_country_loci <- good_pf %>%
filter(STRid %in% as.character(Pf_country_loci$STRid))
Pf_country_loci <- Pf_country_loci %>%
pivot_longer(cols = Bangladesh.Colombia:VietNam.Thailand) %>%
filter(is.na(value)==FALSE) %>%
arrange(value)
vars_pf_country <- setdiff(unique(Pf_country_loci$value), "STRLoci")
vars_pf_country[] <- lapply(vars_pf_country, gsub,pattern="Papua New Guinea",replacement="PapuaNewGuinea")
vars_pf_country[] <- lapply(vars_pf_country, gsub,pattern="Congo DR",replacement="CongoDR")
vars_pf_country[] <- lapply(vars_pf_country, gsub,pattern="Ivory Coast",replacement="IvoryCoast")
vars_pf_country[] <- lapply(vars_pf_country, gsub,pattern="Viet Nam",replacement="VietNam")
geno_pf_country <- geno_origin_Pf
geno_pf_country <- geno_pf_country %>%
filter(STRid %in% as.character(Pf_country_loci$STRid))
STR_Position_pf_country <- Pf_country_loci %>%
dplyr::select(STRid,value)
geno_pf_country <- STR_Position_pf_country %>%
left_join(geno_pf_country,by="STRid")
geno_pf_country <- geno_pf_country[,-1]
geno_pf_country$value <- as.character(geno_pf_country$value)
geno_pf_country$value[] <- lapply(geno_pf_country$value, gsub,pattern="Papua New Guinea",replacement="PapuaNewGuinea")
geno_pf_country$value[] <- lapply(geno_pf_country$value[], gsub,pattern="Congo DR",replacement="CongoDR")
geno_pf_country$value[] <- lapply(geno_pf_country$value[], gsub,pattern="Ivory Coast",replacement="IvoryCoast")
geno_pf_country$value[] <- lapply(geno_pf_country$value[], gsub,pattern="Viet Nam",replacement="VietNam")
geno_pf_country <- as.data.frame(geno_pf_country)
rownames(geno_pf_country) <- geno_pf_country$value
geno_pf_country <- geno_pf_country[,-1]
geno_pf_country <- t(geno_pf_country)
geno_pf_country <- data.frame(Sample=rownames(geno_pf_country),geno_pf_country)
pop_pf_country <- read.csv("Data/Pf_Sample.csv")
geno_pf_country <- geno_pf_country %>%
left_join(pop_pf_country,by="Sample")
vars_pf_country <- gsub(vars_pf_country,pattern="-",replacement=".")
#Pv country
geno_pv <- read.csv("Data/Pv_Genotype.csv")
geno_origin_Pv <- geno_pv
Pv_country_loci <- read.csv("Data/Pv_STR_Top_ShinyPlot.csv")
Pv_country_loci <- good_pv %>%
filter(STRid %in% as.character(Pv_country_loci$STRid))
Pv_country_loci <- Pv_country_loci %>%
pivot_longer(cols = Cambodia.Colombia:Thailand.Peru) %>%
filter(is.na(value)==FALSE) %>%
arrange(value)
vars_pv_country <- setdiff(unique(Pv_country_loci$value), "STRLoci")
geno_pv_country <- geno_origin_Pv
geno_pv_country <- geno_pv_country %>%
filter(STRid %in% as.character(Pv_country_loci$STRid))
STR_Position_pv_country <- Pv_country_loci %>%
dplyr::select(STRid,value)
geno_pv_country <- STR_Position_pv_country %>%
left_join(geno_pv_country,by="STRid")
geno_pv_country <- geno_pv_country[,-1]
geno_pv_country$value <- as.character(geno_pv_country$value)
geno_pv_country <- as.data.frame(geno_pv_country)
rownames(geno_pv_country) <- geno_pv_country$value
geno_pv_country <- geno_pv_country[,-1]
geno_pv_country <- t(geno_pv_country)
geno_pv_country <- data.frame(Sample=rownames(geno_pv_country),geno_pv_country)
pop_pv_country <- read.csv("Data/Pv_Sample.csv")
colnames(pop_pv_country)[1] <- "Sample"
pop_pv_country <- pop_pv_country[,c(1,3)]
pop_pv_country$Country <- as.character(pop_pv_country$Country)
geno_pv_country <- geno_pv_country %>%
left_join(pop_pv_country,by="Sample")
vars_pv_country <- gsub(vars_pv_country,pattern="-",replacement=".")
#Pf PCA plot data
Pf_PCA <- read.csv("Data/PCA_Pf.csv")
Pf_PCA$Population <- factor(Pf_PCA$Population,levels = c("CAF","EAF","WAF","SAM","OCE","SAS","ESEA","WSEA"))
#Pv PCA plot data
Pv_PCA <- read.csv("Data/PCA_Pv.csv")
#STR Pf3K Rshiny
str_R <- read.csv("Data/STR_Pf3K_Rshiny.csv")
#STR model parameter pf and pv
Pf_model_parameter <- read.csv("Data/STR_Model_Parameter_Pf.csv")
Pv_model_parameter <- read.csv("Data/STR_Model_Parameter_Pv.csv")
#Define ui
ui <- navbarPage(inverse = TRUE,
"PlasmoSTR",
theme = shinytheme("cerulean"),
id = "panels_main",
tabPanel(icon("home"),
fluidPage(
h1("PlasmoSTR: A population-wide database of short tandem repeat variation in ", em("Plasmodium falciparum"), "and ", em("Plasmodium vivax")),
br(),
h3("About PlasmoSTR"),
br(),
p(strong("PlasmoSTR contains population and genome-wide information about genetic variation and other characteristics of short tandem repeats (STRs) in ",em("Plasmodium falciparum"), "and ", em("Plasmodium vivax"), " .",style = "font-size:20px;")),
br(),
p(style="text-align: justify;", "We genotype STRs using bioinformatic tool ", a(strong("HipSTR"),style = "color:#337ab7", href = "https://hipstr-tool.github.io/HipSTR/"), "in more than",
a(strong("3,000", em("P. falciparum ")),"(Data generated by the MalariaGEN Plasmodium falciparum Community Project)",style = "color:#337ab7", href = "https://www.malariagen.net/resource/26"), "and ",
a(strong("174", em("P. vivax")),"(Data generated by the Plasmodium vivax Genome Variation project)", style = "color:#337ab7", href = "https://www.malariagen.net/projects/p-vivax-genome-variation"), "and ",
a("(Data from Hupalo et al 2016).", style = "color:#337ab7", href = "https://www.nature.com/articles/ng.3588"),
"We develop a multivariable logistic regression model for the measurement and prediction of the quality of STRs. A set of high-quality STR loci (6,768 from ",
em("P. falciparum"), "and 3,496 from ", em("P. vivax"), ") were selected.",style = "font-size: 13pt"),
p(actionLink("link_to_tabpanel_STRdatasets",strong("1. STR callsets[Searchable]",style = "color:#337ab7")),style = "font-size: 14pt"),
p(style="text-align: justify;","Genome-wide high-quality STRs.",style = "font-size: 13pt"),
p(style="text-align: justify;","Choose the dataset (e.g., Pf or Pv) and the STR genomic location (e.g., All, Coding, Promoter, Intergenic, Intron, or Other) to access the high-quality STR loci information.",style = "font-size: 13pt"),
p(style="text-align: justify;","A genomic region of STR locus using the syntax chrom:start-end (e.g., 7:1322349-1322361). PlasmoSTR is currently based on Pf3D7 (v3 PlasmoDB-41) and PvP01 (PlasmoDB release 41) coordinates.",style = "font-size: 13pt"),
p(actionLink("link_to_tabpanel_PCA",strong("2. PCA",style = "color:#337ab7")),style = "font-size: 14pt"),
p(style="text-align: justify;","Population structure and principal component analysis (PCA) of Plasmodium parasite populations using genome-wide high-quality STRs.",style = "font-size: 13pt"),
p(actionLink("link_to_tabpanel_TopSTR",strong("3. TopSTR",style = "color:#337ab7")),style = "font-size: 14pt"),
p("The top ten most highly differentiated STRs based on ", em("Jost's D "), "proposed by ",
a(strong("Jost (2008)."),style = "color:#337ab7", href = "https://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2008.03887.x"),
style= "font-size: 13pt"),
p(style="text-align: justify;","Choose the", strong(em("P. falciparum")), "population pair (e.g., CAF.EAF),", strong(em("P. falciparum")), "country pair (e.g., Bangladesh.Colombia), or ", strong(em("P. vivax")), "country pair (e.g., Cambodia.Colombia) to access the top ten most highly differentiated STRs between the population or country pairs.",style = "font-size: 13pt"),
p(style="text-align: justify;","Click each row of the datasets can show the samples corresponding motif copies and allele frequency plots. The figures is shown in ", actionLink("link_to_tabpanel_TopPfs",strong("PfPopulation_TopSTRPlots; ",style = "color:#337ab7")),
actionLink("link_to_tabpanel_TopPfsc",strong("PfCountry_TopSTRPlots; ",style = "color:#337ab7")),
actionLink("link_to_tabpanel_TopPvs",strong("PvCountry_TopSTRPlots", ".", style = "color:#337ab7")),
style = "font-size: 13pt"),
p(strong(em("P. falciparum")), strong("population:"), " Central Africa (CAF), East Africa (EAF), West Africa (WAF), South America (SAM), Oceania (OCE), South Asia (SAS), the eastern part of Southeast Asia (ESEA), the western part of Southeast Asia (WSEA).",style = "font-size: 13pt"),
p(strong("4. TopSTRMotifPlots",style = "color:#337ab7"),style = "font-size: 14pt"),
p(style="text-align: justify;","Samples corresponding motif copies and allele frequency plots. Plot samples corresponding motif copies and allele frequency distribution of each highly differentiated STR locus (based on ",em("Jost's D"), "value) over different population or country pair.",style = "font-size: 13pt"),
p(strong("Access by clicking on each row of each dataset in"),actionLink("link_to_tabpanel_TopSTRSTR",strong("TopSTR",style = "color:#337ab7")), ".",
style = "font-size: 13pt"),
p(actionLink("link_to_tabpanel_Accessible_STR",strong("5. Genetic Crosses STR",style = "color:#337ab7")),style = "font-size: 14pt"),
p("Genome-wide accessible STR variation in ", em("P. falciparum "), "genome based on ",
a(strong(em("P. falciparum Genetic Crosses.")),style = "color:#337ab7", href = "https://www.malariagen.net/parasite/p-falciparum-genetic-crosses"),
style= "font-size: 13pt"),
p(style="text-align: justify;","These STRs show highly accurate calls (No Mendelian errors or discordance between biological replicates) in three experimental genetic crosses (",
a(strong("3D7×HB3, Walliker et al. 1987; "),style = "color:#337ab7", href = "https://www.science.org/lookup/doi/10.1126/science.3299700"),
a(strong("HB3×Dd2, Wellems et al. 1990; "),style = "color:#337ab7", href = "https://www.nature.com/articles/345253a0"),
a(strong("7G8×GB4, Hayton et al. 2008) "),style = "color:#337ab7", href = "https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(08)00185-6?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1931312808001856%3Fshowall%3Dtrue"),
"of ", em("P. falciparum "), "(98 samples).",style = "font-size: 13pt"),
#Change
p(actionLink("link_to_tabpanel_STR_parameter",strong("6. STR model parameter",style = "color:#337ab7")),style = "font-size: 14pt"),
p("Genome-wide high-quality STRs of ",em("P. falciparum "),"and",em("P. vivax"),"multivariable logistic regression model parameter.",
style= "font-size: 13pt"),
br(),
br(),
br(),
br(),
br(),
div(p(strong("PlasmoSTR was developed by Jiru Han with input from Jacob E. Munro, Melanie Bahlo and other "), a("Bahlo Lab members", href = "https://www.wehi.edu.au/people/melanie-bahlo/372/melanie-bahlo-lab-team")),
p(strong("It has been made with Rstudio and Shiny and you may find the code on "), a("github", href = "https://github.com/bahlolab/PlasmoSTR/"), "."),
style="text-align: right;")
)
),
# Sidebar layout with input and output definitions ----
#Pf and Pv dataset
tabPanel("STR callsets",
fluidPage(
h1("Genome-wide high-quality STRs"),
sidebarLayout(position = "left",
sidebarPanel(
wellPanel(style = "background: white",
h4(em("Plasmodium falciparum"),": 6,768"),
h4(em("Plasmodium vivax"),": 3,496"),
hr(),
selectInput(inputId = "dataset",
label = "Please choose a dataset:",
choices = c("","Pf", "Pv")),
selectInput(inputId = "STRcategory",
label = "Please choose a genomic location:",
choices = c("","All","Coding", "Promoter","Intergenic","Intron", "Other")),
actionButton("View","Generate Table"))
),
mainPanel(
br(),
DT::dataTableOutput("view1")
)
)
)
),
#PCA Page
tabPanel("PCA",
fluidPage(
h1("Principal component analysis (PCA)"),
br(),
br(),
mainPanel(
p(strong("PCA plot of 6,768 genome-wide high-quality STRs (Pf MalariaGEN 3,047 samples)"),style = "font-size: 17pt"),
br(),
br(),
plotlyOutput('Pf_PCA'),
br(),
p("Colors represent populations, each sample is represented by a single point."),
p("Central Africa (CAF), East Africa (EAF), West Africa (WAF), South America (SAM), Oceania (OCE), South Asia (SAS), the eastern part of Southeast Asia (ESEA), the western part of Southeast Asia (WSEA)"),
hr(),
br(),
br(),
p(strong("PCA plot of 3,496 genome-wide high-quality STRs (Pv 174 samples)"),style = "font-size: 17pt"),
br(),
br(),
plotlyOutput('Pv_PCA'),
br(),
p("Colors represent countries, each sample is represented by a single point.")
)
)
),
#The top ten highly differentiated STRs
tabPanel("TopSTR",
h1("Highly differentiated STRs of each pair population or country"),
navlistPanel(
"Plasmodium falciparum",
tabPanel("1. Pf Population Pair",
fluidRow(column(12,
wellPanel(#Pf population
style = "background: white",
selectInput(inputId = "PfPopulationPair",
label = "Please choose a P. falciparum population pair:",
choices = c("",compare_Pf_Population)),
actionButton("View2","Generate Table"))),
fluidRow(column(12,
DT::dataTableOutput("view2"))))),
tabPanel("2. Pf Country Pair",
fluidRow(column(12,
wellPanel(#Pf population
style = "background: white",
#Pf country
selectInput(inputId = "PfCountrypair",
label = "Please choose a P. falciparum country pair:",
choices = c("",compare_Pf_Country)),
actionButton("View3","Generate Table"))),
fluidRow(column(12,
DT::dataTableOutput("view3"))))),
"Plasmodium vivax",
tabPanel("3. Pv Country Pair",
fluidRow(column(12,
wellPanel(
style = "background: white",
#Pv country
selectInput(inputId = "PvCountrypair",
label = "Please choose a P. vivax country pair:",
choices = c("",compare_Pv_Country)),
actionButton("View4","Generate Table"))),
fluidRow(column(12,
DT::dataTableOutput("view4")))))
)
),
#Plots Pf population
tabPanel("PfPopulation_TopSTRPlots",
tabPanel("Pf Population Pair",
fluidRow(column(6,
wellPanel(
style = "background: white",
selectInput(inputId = 'xcol',
label ='Please choose the plot color for P. falciparum (Population or Country)',
choices = c("Population","Country"))))),
fluidRow(
column(12,
h3(textOutput('textPf')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pfx1', height = 500),
plotlyOutput('Pfx2', height = 500)
),
h3(textOutput('textPfA')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pfx3', height = 500),
plotlyOutput('Pfx4', height = 500)
)
)))
),
#Plots Pf country
tabPanel("PfCountry_TopSTRPlots",
tabPanel("Pf Country Pair",
fluidRow(column(6,
wellPanel(
style = "background: white",
selectInput(inputId = 'xcol1',
label ='Please choose the plot color for P. falciparum (Population or Country)',
choices = c("Population","Country"))))),
fluidRow(
column(12,
h3(textOutput('textPfc')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pfcx1', height = 500),
plotlyOutput('Pfcx2', height = 500)
),
h3(textOutput('textPfAc')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pfcx3', height = 500),
plotlyOutput('Pfcx4', height = 500)
)
)
))
),
#Plots Pv country
tabPanel("PvCountry_TopSTRPlots",
tabPanel("Pv Country Pair",
fluidRow(
column(12,
h3(textOutput('textPv')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pvx1', height = 500),
plotlyOutput('Pvx2', height = 500)
),
h3(textOutput('textPvc')),
splitLayout(style = "border: 1px solid silver:", cellWidths = c("40%","60%"),
plotlyOutput('Pvx3', height = 500),
plotlyOutput('Pvx4', height = 500)
)
)
))
),
#Pf Pf3K dataset
tabPanel("GeneticCross_STR",
fluidPage(
h1("Accessible STR variation in", em("Plasmodium falciparum"), "genome (P. falciparum Genetic Crosses)"),
sidebarLayout(position = "left",
sidebarPanel(
wellPanel(style = "background: white",
selectInput(inputId = "datasetRs",
label = "Please choose a dataset:",
choices = c("","Pf")),
actionButton("ViewR5","Generate Table"))
),
mainPanel(
br(),
DT::dataTableOutput("viewRs")
)
)
)
),
#Pf and Pv model parameter
tabPanel("STR_Model_Parameter",
fluidPage(
h1("STR multivariable logistic regression model parameter"),
sidebarLayout(position = "left",
sidebarPanel(
wellPanel(style = "background: white",
selectInput(inputId = "datasetMs",
label = "Please choose a dataset:",
choices = c("","Pf","Pv")),
actionButton("ViewM7","Generate Table"))
),
mainPanel(
br(),
DT::dataTableOutput("viewMs")
)
)
)
)
)
# Define server logic to summarize and view selected dataset ----
server <- function(input, output,session) {
#Link internal page
observeEvent(input$link_to_tabpanel_STRdatasets,{
newvalue <- "STR callsets"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_PCA,{
newvalue <- "PCA"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_TopSTR,{
newvalue <- "TopSTR"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_TopSTRSTR,{
newvalue <- "TopSTR"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_Accessible_STR,{
newvalue <- "GeneticCross_STR"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_STR_parameter,{
newvalue <- "STR_Model_Parameter"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_TopPfs,{
newvalue <- "PfPopulation_TopSTRPlots"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_TopPfsc,{
newvalue <- "PfCountry_TopSTRPlots"
updateNavbarPage(session, "panels_main", newvalue)
})
observeEvent(input$link_to_tabpanel_TopPvs,{
newvalue <- "PvCountry_TopSTRPlots"
updateNavbarPage(session, "panels_main", newvalue)
})
# Return the requested dataset ----
Pf = good_pf %>%
dplyr::select(STRLocus,Motif,copies,Gene,Product.Description,Category,Heterozygosity,PlasmoDB_Link,Protein_Link) %>%
dplyr::rename("STR Locus (Pf3D7)" = "STRLocus") %>%
dplyr::rename("# copies (Pf3D7)" = "copies")
Pv = good_pv %>%
dplyr::select(STRLocus,Motif,copies,Gene,Product.Description,Category,Heterozygosity,PlasmoDB_Link) %>%
dplyr::rename("STR Locus (PvP01)" = "STRLocus") %>%
dplyr::rename("# copies (PvP01)" = "copies")
str_Rshiny = str_R %>%
dplyr::rename("# Repeatcopies (Pf3D7)" = "Repeatcopies")
Pf_model <- Pf_model_parameter %>%
dplyr::rename("STR Locus (Pf3D7)" = "STRLocus")
Pv_model <- Pv_model_parameter %>%
dplyr::rename("STR Locus (PvP01)" = "STRLocus")
db <- reactive({
require(input$View)
if (input$dataset == "Pf" & input$STRcategory == "All") {
Pf[,-c(4,8,9)]
} else if (input$dataset == "Pf" & input$STRcategory == "Coding") {
Pf %>%
filter(Category=="coding")
} else if (input$dataset == "Pf" & input$STRcategory == "Promoter") {
Pf[,-c(4,8,9)] %>%
filter(Category=="promoter")
} else if (input$dataset == "Pf" & input$STRcategory == "Intergenic") {
Pf[,-c(4,8,9)] %>%
filter(Category=="intergenic")
} else if (input$dataset == "Pf" & input$STRcategory == "Intron") {
Pf[,-c(4,8,9)] %>%
filter(Category=="intron")
} else if (input$dataset == "Pf" & input$STRcategory == "Other") {
Pf[,-c(4,8,9)] %>%
filter(!(Category %in% c("coding", "promoter","intergenic","intron")))
} else if (input$dataset == "Pv" & input$STRcategory == "All") {
Pv[,-c(4,8)]
} else if (input$dataset == "Pv" & input$STRcategory == "Coding") {
Pv %>%
filter(Category=="coding")
} else if (input$dataset == "Pv" & input$STRcategory == "Promoter") {
Pv[,-c(4,8)] %>%
filter(Category=="promoter")
} else if (input$dataset == "Pv" & input$STRcategory == "Intergenic") {
Pv[,-c(4,8)] %>%
filter(Category=="intergenic")
} else if (input$dataset == "Pv" & input$STRcategory == "Intron") {
Pv[,-c(4,8)] %>%
filter(Category=="intron")
} else if (input$dataset == "Pv" & input$STRcategory == "Other") {
Pv[,-c(4,8)] %>%
filter(!(Category %in% c("coding", "promoter","intergenic","intron")))
}
})
data <- eventReactive(input$View, {
db()
})
output$view1 <- DT::renderDataTable(
DT::datatable(data(),caption = 'Table: This is genome-wide high-quality STR loci table.',
filter = 'top',
extensions = 'Buttons',
escape = FALSE,
options = list(dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All")))
)
)
db_Rshiny <- reactive({
require(input$ViewR5)
if (input$datasetRs == "Pf") {
str_Rshiny
}
})
data_Rshiny <- eventReactive(input$ViewR5, {
db_Rshiny()
})
output$viewRs <- DT::renderDataTable(
DT::datatable(data_Rshiny(),caption = 'Table: This is genome-wide high-quality STR loci table (P. falciparum Genetic Crosses).',
filter = 'top',
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All")))
)
)
#Pf and Pv model
db_model <- reactive({
require(input$ViewM7)
if (input$datasetMs == "Pf") {
Pf_model
} else if (input$datasetMs == "Pv") {
Pv_model
}
})
data_model <- eventReactive(input$ViewM7, {
db_model()
})
output$viewMs <- DT::renderDataTable(
DT::datatable(data_model(),caption = 'Table: This is genome-wide high-quality STR multivariable logistic regression model parameter.',
filter = 'top',
extensions = 'Buttons',
options = list(dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenu = list(c(10,25,50,-1),
c(10,25,50,"All")))
)
)
# Downloadable csv of selected dataset ----
#Pf population pair
datasetInput2 <- eventReactive(input$View2,{
good_pf %>%
dplyr::filter(is.na(good_pf[,which(colnames(good_pf)==input$PfPopulationPair)])==FALSE) %>%
dplyr::select(STRLocus,Motif,copies,Product.Description,Category,Heterozygosity,input$PfPopulationPair) %>%
dplyr::rename("STR Locus (Pf3D7)" = "STRLocus") %>%
dplyr::rename("# copies (Pf3D7)" = "copies") %>%
arrange(as.numeric(str_extract(.[,7], "\\d+$")))
})
#Pf country pair
datasetInput3 <- eventReactive(input$View3,{
good_pf %>%
dplyr::filter(is.na(good_pf[,which(colnames(good_pf)==input$PfCountrypair)])==FALSE) %>%
dplyr::select(STRLocus,Motif,copies,Product.Description,Category,Heterozygosity,input$PfCountrypair) %>%
dplyr::rename("STR Locus (Pf3D7)" = "STRLocus") %>%
dplyr::rename("# copies (Pf3D7)" = "copies") %>%
arrange(as.numeric(str_extract(.[,7], "\\d+$")))
})
#Pv country pair
datasetInput4 <- eventReactive(input$View4,{
good_pv %>%
dplyr::filter(is.na(good_pv[,which(colnames(good_pv)==input$PvCountrypair)])==FALSE) %>%
dplyr::select(STRLocus,Motif,copies,Product.Description,Category,Heterozygosity, input$PvCountrypair) %>%
dplyr::rename("STR Locus (PvP01)" = "STRLocus") %>%
dplyr::rename("# copies (PvP01)" = "copies") %>%
arrange(as.numeric(str_extract(.[,7], "\\d+$")))
})
#Pf_PCA
fig1_pf <- Pf_PCA %>% plot_ly(
type = 'scatter',
x = ~PC1,
y = ~PC2,
color = ~Population,
legend = ~Population,
legendgroup = ~Population,
text =~Sample
)
fig1_pf <- fig1_pf %>% layout(
xaxis = list(
showgrid = F
),
yaxis = list(
showgrid = F
)
)
fig2_pf <- Pf_PCA %>% plot_ly(
type = 'scatter',
x = ~PC1,
y = ~PC3,
color = ~Population,
legendgroup = ~Population,
text =~Sample,
showlegend = F
)
fig2_pf <- fig2_pf %>% layout(
xaxis = list(
showgrid = F
),
yaxis = list(
showgrid = F
)
)
fig_pf <- subplot(fig1_pf, fig2_pf, nrows = 2, shareX = T)
fig_pf
output$Pf_PCA <- renderPlotly({
print(
fig_pf
)
})
#Pv_PCA
fig1_pv <- Pv_PCA %>% plot_ly(
type = 'scatter',
x = ~PC1,
y = ~PC2,
color = ~Country,
legend = ~Country,
legendgroup = ~Country,
text =~Sample
)
fig1_pv <- fig1_pv %>% layout(
xaxis = list(
showgrid = F
),
yaxis = list(
showgrid = F
)
)
fig2_pv <- Pv_PCA %>% plot_ly(
type = 'scatter',
x = ~PC1,
y = ~PC3,
color = ~Country,
legendgroup = ~Country,
text =~Sample,
showlegend = F
)
fig2_pv <- fig2_pv %>% layout(
xaxis = list(
showgrid = F
),
yaxis = list(
showgrid = F
)
)
fig_pv <- subplot(fig1_pv, fig2_pv, nrows = 2, shareX = T)
fig_pv
output$Pv_PCA <- renderPlotly({
print(
fig_pv
)
})
#The top ten highly differentiated STRs P. falciparum population pair
output$view2 <- DT::renderDataTable(
DT::datatable(datasetInput2(),caption = 'Table: This is top 10 most highly differentitated STR loci table (P. falciparum population pair). Click each row can show the samples corresponding motif copies plots.',
filter = 'top',
#options = list(dom = 't'),
selection = list(mode="single", target="row"),
options = list(paging=FALSE,
dom = 't',
searching=FALSE,
filtering=FALSE,
ordering=FALSE),
callback=JS(
'table.on("click.dt", "tr", function() {
tabs = $(".tabbable .nav.nav-tabs li a");
var data=table.row(this).data();
$(tabs[1]).click();
table.row(this).deselect();
})'
))
)
# A function factory for getting integer y-axis values.
integer_breaks <- function(n = 5, ...) {
fxn <- function(x) {
breaks <- floor(pretty(x, n, ...))
names(breaks) <- attr(breaks, "labels")
breaks
}
return(fxn)
}
#Plot pf population
observeEvent(input$view2_rows_selected,{
output$textPf <- renderText({"STR copies of samples in highly differentiated STR locus"})
output$textPfA <- renderText({"Allele frequency of samples in highly differentiated STR locus"})
selectedCellsPf <- renderText(as.character(datasetInput2()[input$view2_rows_selected,7]))
selectedCellsPfReference <- renderText(datasetInput2()[input$view2_rows_selected,3])
output$Pfx1 = renderPlotly({
plot1pf <- geno_pf_population %>%
filter(is.na(geno_pf_population[,which(colnames(geno_pf_population)==selectedCellsPf())])==FALSE) %>%
filter(Population %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPf()),"\\."))) %>%
filter(is.na(selectedCellsPf())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPf()) %>%
ggplot(aes_string(x=input$xcol,y="Motif",col=input$xcol,
label="Sample",label2=input$xcol,label3="Motif"))+
geom_jitter(alpha=0.5,height = 0.01)+
theme_bw()+
scale_y_continuous(breaks = integer_breaks())+
labs(
title = "Pair population (P. falciparum)",
x="Pairwise population",
y="STR copies")+
geom_hline(yintercept = as.numeric(selectedCellsPfReference()),linetype="dashed",size=0.5)+
annotate("text",0.8,as.numeric(selectedCellsPfReference()),vjust = -1,label = "Pf3D7")
ggplotly(plot1pf)
})
output$Pfx2 = renderPlotly({
plot2pf <- geno_pf_population %>%
filter(is.na(geno_pf_population[,which(colnames(geno_pf_population)==selectedCellsPf())])==FALSE) %>%
filter(!(Population %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPf()),"\\.")))) %>%
filter(is.na(selectedCellsPf())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPf()) %>%
ggplot(aes_string(x=input$xcol,y="Motif",col=input$xcol,
label="Sample",label2=input$xcol,label3="Motif"))+
geom_jitter(alpha=0.5,height = 0.01)+
theme_bw()+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Other populations exclude pair population (P. falciparum)",
x="Other populations",
y="STR copies")+
geom_hline(yintercept = as.numeric(selectedCellsPfReference()),linetype="dashed",size=0.5)+
annotate("text",0.8,as.numeric(selectedCellsPfReference()),vjust = -1,label = "Pf3D7")
ggplotly(plot2pf)
})
output$Pfx3 = renderPlotly({
plot3pf <- geno_pf_population %>%
filter(is.na(geno_pf_population[,which(colnames(geno_pf_population)==selectedCellsPf())])==FALSE) %>%
filter(Population %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPf()),"\\."))) %>%
filter(is.na(selectedCellsPf())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPf()) %>%
group_by(.dots = c("Motif",input$xcol)) %>%
summarise(Count=n()) %>%
ungroup() %>%
ggplot(aes_string(x="Motif",y="Count",fill=input$xcol))+
geom_col(alpha=0.8) +
theme_bw()+
scale_x_continuous(breaks = integer_breaks())+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Pair population (P. falciparum)",
x="STR copies",
y="Count")
ggplotly(plot3pf)
})
output$Pfx4 = renderPlotly({
plot4pf <- geno_pf_population %>%
filter(is.na(geno_pf_population[,which(colnames(geno_pf_population)==selectedCellsPf())])==FALSE) %>%
filter(!(Population %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPf()),"\\.")))) %>%
filter(is.na(selectedCellsPf())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPf()) %>%
group_by(.dots = c("Motif",input$xcol)) %>%
summarise(Count=n()) %>%
ungroup() %>%
ggplot(aes_string(x="Motif",y="Count",fill=input$xcol))+
geom_col(alpha=0.8) +
theme_bw()+
scale_x_continuous(breaks = integer_breaks())+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Other populations exclude pair population (P. falciparum)",
x="STR copies",
y="Count")
ggplotly(plot4pf)
})
updateNavbarPage(session, "panels_main", selected = "PfPopulation_TopSTRPlots")
})
#The top ten highly differentiated STRs P. falciparum country pair
output$view3 <- DT::renderDataTable(
DT::datatable(datasetInput3(),caption = 'Table: This is top 10 most highly differentitated STR loci table (P. falciparum country pair). Click each row can show the samples corresponding motif copies plots.',
filter = 'top',
#options = list(dom = 't'),
selection = list(mode="single", target="row"),
options = list(paging=FALSE,
dom = 't',
searching=FALSE,
filtering=FALSE,
ordering=FALSE),
callback=JS(
'table.on("click.dt", "tr", function() {
tabs = $(".tabbable .nav.nav-tabs li a");
var data=table.row(this).data();
$(tabs[1]).click();
table.row(this).deselect();
})'
))
)
#plot pf country
observeEvent(input$view3_rows_selected,{
output$textPfc <- renderText({"STR copies of samples in highly differentiated STR locus"})
output$textPfAc <- renderText({"Allele frequency of samples in highly differentiated STR locus"})
selectedCellsPfc <- renderText(as.character(datasetInput3()[input$view3_rows_selected,7]))
selectedCellsPfcReference <- renderText(datasetInput3()[input$view3_rows_selected,3])
output$Pfcx1 = renderPlotly({
plot1pfc <- geno_pf_country %>%
filter(is.na(geno_pf_country[,which(colnames(geno_pf_country)==selectedCellsPfc())])==FALSE) %>%
filter(Country %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPfc()),"\\."))) %>%
filter(is.na(selectedCellsPfc())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPfc()) %>%
ggplot(aes_string(x=input$xcol1,y="Motif",col=input$xcol1,
label="Sample",label2=input$xcol1,label3="Motif"))+
geom_jitter(alpha=0.5,height = 0.01)+
theme_bw()+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Pair country (P. falciparum)",
x="Pairwise country",
y="STR copies")+
geom_hline(yintercept = as.numeric(selectedCellsPfcReference()),linetype="dashed",size=0.5)+
annotate("text",0.8,as.numeric(selectedCellsPfcReference()),vjust = -1,label = "Pf3D7")
ggplotly(plot1pfc)
})
output$Pfcx2 = renderPlotly({
plot2pfc <- geno_pf_country %>%
filter(is.na(geno_pf_country[,which(colnames(geno_pf_country)==selectedCellsPfc())])==FALSE) %>%
filter(!(Country %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPfc()),"\\.")))) %>%
filter(is.na(selectedCellsPfc())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPfc()) %>%
ggplot(aes_string(x=input$xcol1,y="Motif",col=input$xcol1,
label="Sample",label2=input$xcol1,label3="Motif"))+
geom_jitter(alpha=0.5,height = 0.01)+
theme_bw()+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Other countries exclude pair country (P. falciparum)",
x="Other countries",
y="STR copies")+
geom_hline(yintercept = as.numeric(selectedCellsPfcReference()),linetype="dashed",size=0.5)+
annotate("text",0.8,as.numeric(selectedCellsPfcReference()),vjust = -1,label = "Pf3D7")
ggplotly(plot2pfc)
})
output$Pfcx3 = renderPlotly({
plot3pfc <- geno_pf_country %>%
filter(is.na(geno_pf_country[,which(colnames(geno_pf_country)==selectedCellsPfc())])==FALSE) %>%
filter(Country %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPfc()),"\\."))) %>%
filter(is.na(selectedCellsPfc())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPfc()) %>%
group_by(.dots = c("Motif",input$xcol1)) %>%
summarise(Count=n()) %>%
ungroup() %>%
ggplot(aes_string(x="Motif",y="Count",fill=input$xcol1))+
geom_col(alpha=0.8) +
theme_bw()+
scale_x_continuous(breaks = integer_breaks())+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Pair country (P. falciparum)",
x="STR copies",
y="Count")
ggplotly(plot3pfc)
})
output$Pfcx4 = renderPlotly({
plot4pfc <- geno_pf_country %>%
filter(is.na(geno_pf_country[,which(colnames(geno_pf_country)==selectedCellsPfc())])==FALSE) %>%
filter(!(Country %in% unlist(strsplit(gsub('[[:digit:]]+', '', selectedCellsPfc()),"\\.")))) %>%
filter(is.na(selectedCellsPfc())==FALSE) %>%
dplyr::rename("Motif"=selectedCellsPfc()) %>%
group_by(.dots = c("Motif",input$xcol1)) %>%
summarise(Count=n()) %>%
ungroup() %>%
ggplot(aes_string(x="Motif",y="Count",fill=input$xcol1))+
geom_col(alpha=0.8) +
theme_bw()+
scale_x_continuous(breaks = integer_breaks())+
scale_y_continuous(breaks = integer_breaks())+
labs(title = "Other countries exclude pair country (P. falciparum)",
x="STR copies",
y="Count")
ggplotly(plot4pfc)
})
updateNavbarPage(session, "panels_main", selected = "PfCountry_TopSTRPlots")
})
#The top ten highly differentiated STRs P. vivax country pair
output$view4 <- DT::renderDataTable(
DT::datatable(datasetInput4(),caption = 'Table: This is top 10 most highly differentitated STR loci table (P. vivax country pair). Click each row can show the samples corresponding motif copies plots.',
filter = 'top',
#options = list(dom = 't'),
selection = list(mode="single", target="row"),
options = list(paging=FALSE,
dom = 't',
searching=FALSE,
filtering=FALSE,
ordering=FALSE),
callback=JS(
'table.on("click.dt", "tr", function() {
tabs = $(".tabbable .nav.nav-tabs li a");
var data=table.row(this).data();
$(tabs[1]).click();
table.row(this).deselect();
})'
))
)
#plot pv country
observeEvent(input$view4_rows_selected,{
output$textPv <- renderText({"STR copies of samples in highly differentiated STR locus"})