msa dashboard
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6 changed files with 643 additions and 0 deletions
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@ -316,3 +316,344 @@ consurf_colours = c(
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, "9" = rgb(0.63,0.16,0.37)
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)
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if (interactive()){
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options(shiny.launch.browser = FALSE) # i am a big girl and can tie my own laces
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options(shiny.port = 8000) # don't change the port every time
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options(shiny.host = '0.0.0.0') # This means "listen to all addresses on all interfaces"
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options(width=120)
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options(DT.options = list(scrollX = TRUE))
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ui=dashboardPage(skin="purple",
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dashboardHeader(title = "Drug/Target Explorer"),
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dashboardSidebar(
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sidebarMenu( id = "sidebar",
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selectInput(
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"switch_target",
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label="Switch to New Target",
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choices = c(
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"alr",
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"embb",
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"gid",
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"katg",
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"pnca",
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"rpob"
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),
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selected="embb"),
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menuItem("LogoP SNP", tabName="LogoP SNP"),
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menuItem("Lineage Sample Count", tabName="Lineage Sample Count"),
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menuItem("Site SNP count", tabName="Site SNP count"),
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menuItem("Stability SNP by site", tabName="Stability SNP by site"),
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menuItem("DM OM Plots", tabName="DM OM Plots"),
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menuItem("Correlation", tabName="Correlation"),
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menuItem("Lineage Distribution", tabName="Lineage Distribution"),
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menuItem("Consurf", tabName="Consurf"),
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menuItem("LogoP OR", tabName="LogoP OR"),
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menuItem("LogoP ED", tabName="LogoP ED"),
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menuItem('Stability count', tabName='Stability count'),
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# These conditionalPanel()s make extra settings appear in the sidebar when needed
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conditionalPanel(
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condition="input.sidebar == 'LogoP SNP'",
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textInput(
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"omit_snp_count",
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"Omit SNPs",
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value = c(0),
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placeholder = "1,3,6"
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)
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),
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# NOTE:
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# I *think* we can cheat here slightly and use the min/max from
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# merged_df3[['position']] for everything because the various
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# dataframes for a given gene/drug combination have the
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# same range of positions. May need fixing, especially
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# if we get/shrink the imported data files to something
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# more reasonable.
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP'||
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input.sidebar == 'Stability SNP by site' ||
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input.sidebar == 'Consurf' ||
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input.sidebar == 'LogoP OR' ||
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input.sidebar == 'Site SNP count'",
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sliderInput(
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"display_position_range"
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, "Display Positions"
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, min=1, max=150, value=c(1,150) # 150 is just a little less than the smallest pos_count
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'LogoP ED'",
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sliderInput(
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"display_position_full_range"
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, "Display Positions"
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, min=1, max=150, value=c(1,150)
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)
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),
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP' ||
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input.sidebar == 'LogoP OR' ||
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input.sidebar == 'LogoP ED'",
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selectInput(
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"logoplot_colour_scheme",
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label="Logo Plot Colour Scheme",
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choices = logoPlotSchemes,
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selected="chemistry"
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)
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),
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#conditionalPanel(
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# condition="input.sidebar == 'LogoP SNP' || input.sidebar == 'LogoP ED'|| input.sidebar == 'Consurf'",
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# numericInput(
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# "table_position"
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# , "Table Position", value=1
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# )
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#),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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selectInput(
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"corr_method",
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label="Correlation Method",
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choices = list("spearman",
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"pearson",
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"kendall"),
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selected="spearman"
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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numericInput(
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"corr_lig_dist"
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, "Ligand Distance Cutoff (Å)", value=1
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'Correlation'",
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checkboxGroupInput(
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"corr_selected",
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"Parameters",
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choiceNames = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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#"dst_mode"
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),
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choiceValues = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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#"dst_mode"
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),
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selected = c(
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"DeepDDG",
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"Dynamut2",
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"FoldX",
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"ConSurf"#,
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#"dst_mode"
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)
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)
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),
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conditionalPanel(
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condition="input.sidebar == 'DM OM Plots'",
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selectInput(
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"dm_om_param",
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label="Stability Parameter",
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choices = keys(dm_om_map),
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selected="SNAP2")
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),
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# colour_categ
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conditionalPanel(
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condition="input.sidebar == 'Stability SNP by site'",
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selectInput(
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"stability_snp_param",
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label="Stability Parameter",
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choices = stability_boxes_df$stability_type,
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selected="Average")
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),
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conditionalPanel(
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condition="input.sidebar == 'Stability SNP by site'",
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checkboxInput("reorder_custom_h",
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label="Reorder by SNP count",
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FALSE)
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),
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conditionalPanel(
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condition="input.sidebar.match(/^Lineage.*/)",
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checkboxInput("all_lineages",
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label="All Lineages",
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FALSE)
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),
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# an example of how you can match multiple things in frontend JS
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conditionalPanel(
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condition="input.sidebar == 'LogoP SNP' ||
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input.sidebar =='Stability SNP by site' ||
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input.sidebar =='Consurf' ||
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input.sidebar =='LogoP OR' ||
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input.sidebar =='LogoP ED'",
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actionButton("clear_ngl",
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"Clear Structure")
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),
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conditionalPanel(
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condition="input.sidebar == 'LogoP SNP' ||
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input.sidebar =='Stability SNP by site' ||
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input.sidebar =='Consurf' ||
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input.sidebar =='LogoP OR' ||
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input.sidebar =='LogoP ED'",
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actionButton("test_ngl",
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"Test NGLViewR")
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)#,
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# downloadButton("save",
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# "Download Plot"
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# )
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# actionButton(
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# "reload_target",
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# label="Reload Target\nData (slow!)"
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# )
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)
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),
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body <- dashboardBody(
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tabItems(
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tabItem(tabName = "dashboard",
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h2("Dashboard tab content")
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),
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tabItem(tabName = "widgets",
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h2("Widgets tab content")
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)
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),
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# creates a 'Conditional Panel' containing a plot object from each of our
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# ggplot plot functions (and its associated data frame)
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fluidRow(column(width=12,
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lapply(plot_functions_df$tab_name,
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function(x){
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plot_function=plot_functions_df[
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plot_functions_df$tab_name==x,
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"plot_function"]
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plot_df=plot_functions_df[
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plot_functions_df$tab_name==x,
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"plot_df"]
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cat(paste0('\nCreating output: ', x))
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generate_conditionalPanel(x, plot_function, plot_df)
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}
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)
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)
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),
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#### fluidRow()s for "Stability Count" in the sidebar ####
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fluidRow(
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP' ||
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input.sidebar =='Stability SNP by site' ||
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input.sidebar =='Consurf' ||
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input.sidebar =='LogoP OR' ||
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input.sidebar =='LogoP ED'",
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column(NGLVieweROutput("structure"),
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width=3
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)
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),
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conditionalPanel(
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condition="
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input.sidebar == 'LogoP SNP' ||
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input.sidebar == 'Stability SNP by site' ||
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input.sidebar == 'Site SNP count' ||
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input.sidebar == 'Consurf' ||
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input.sidebar == 'LogoP OR' ||
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input.sidebar == 'LogoP ED'",
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column(
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DT::dataTableOutput('table'),
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width=9
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)
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)
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)
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)
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)
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server = function(input, output) {
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observeEvent({
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input$combined_model
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input$combined_data
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input$combined_training_genes
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input$score_dropdown
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input$resample_dropdown
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input$drug_dropdown
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input$split_dropdown
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},{
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combined_model = input$combined_model
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selection = input$score_dropdown
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resampler = input$resample_dropdown
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selected_drug = input$drug_dropdown
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selected_split = input$split_dropdown
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combined_data = input$combined_data
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combined_training_genes = input$combined_training_genes
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selected_gene = combo[combo$drug == selected_drug,'gene']
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# hide stuff if selected
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if(combined_model == "combined") {
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#if(combined_model == TRUE) {
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hide("split_dropdown")
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hide("resample_dropdown")
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show("combined_data")
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show("combined_training_genes")
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filedata = paste0(combined_training_genes,
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'genes_logo_skf_BT_',
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selected_gene,
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'_',
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combined_data
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)
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print(filedata)
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print('doing COMBINED plot')
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output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
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selection,
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"none", # always 'none' for combined plot
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gene = combo[drug==selected_drug,"gene"],
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combined_training_genes = combined_training_genes,
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display_combined = TRUE,
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)
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)
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# e.g.
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# makeplot(loaded_files$`5genes_logo_skf_BT_pnca_actual`, "MCC", "none" , gene = 'foo', combined_training_genes = '1234', display_combined = TRUE)
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} else {
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show("split_dropdown")
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show("resample_dropdown")
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hide("combined_data")
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hide("combined_training_genes")
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filedata = paste0(combo[drug==selected_drug,"gene"],
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'_baselineC_',
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selected_split
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)
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print(filedata)
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print("doing GENE plot")
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output$plot <- renderPlot(makeplot(loaded_files[[filedata]],
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selection,
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resampler,
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gene = combo[drug==selected_drug,"gene"],
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display_combined = FALSE,
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)
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)
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}
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# 6genes_logo_skf_BT_gid_complete
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# filedata example for combined: 6genes_logo_skf_BT_embb_actual
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# 6genes_logo_skf_BT_embb_combined
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})
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}
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app <- shinyApp(ui, server)
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runApp(app)
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}
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