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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@ -98,6 +98,7 @@
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<div id="main">
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<a href = "drug-target/"><h2>Drug/Gene Target explorer</h2></a>
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<a href="ml/"><h2>ML/AI model explorer</h2></a>
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<a href="msa/"><h2>Multiple Sequence Alignment explorer</h2></a>
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</div>
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</div>
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</div>
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BIN
msa/.global.R.swp
Normal file
BIN
msa/.global.R.swp
Normal file
Binary file not shown.
301
msa/global.R
Normal file
301
msa/global.R
Normal file
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@ -0,0 +1,301 @@
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library(shinycssloaders)
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library(DT)
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library(NGLVieweR)
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library(hash)
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load_target_globals=function(target){
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cat(paste0("Reloading Target: ", target))
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source(paste0("/srv/shiny-server/git/LSHTM_analysis/config/", target, ".R")) # load per-target config file
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invisible(assign(paste0(target, "_merged_df3"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df3.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_merged_df2"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-merged_df2.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_corr_df_m3_f"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-corr_df_m3_f.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_lin_lf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_lf.csv")), envir = .GlobalEnv))
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invisible(assign(paste0(target, "_lin_wf"), read.csv(paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/",target,"-lin_wf.csv")), envir = .GlobalEnv))
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lapply(
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c(
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"duet",
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"mcsm_lig",
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"foldx",
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"deepddg",
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"dynamut2",
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"consurf",
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"snap2",
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"provean",
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"dist_gen",
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"mcsm_ppi2"#,
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#"mcsm_na"
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), function(x){
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wf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-wf_", x ,".csv")
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wf_var=paste0("wf_",x)
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if (file.exists(wf_filename)){
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invisible(assign(wf_var,read.csv(wf_filename), envir = .GlobalEnv)) # FILTH
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}
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lf_filename=paste0("/srv/shiny-server/git/Misc/shiny_dashboard/data/", tolower(gene), "-lf_", x ,".csv")
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lf_var=paste0(target, "_lf_",x)
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if (file.exists(lf_filename)){
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invisible(assign(lf_var,read.csv(lf_filename), envir = .GlobalEnv)) # FILTH
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}
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}
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)
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}
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# populate target-specific *_unified_msa vars
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load_msa_global=function(gene){
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drug=target_map[[gene]]
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in_filename_msa = paste0(tolower(gene), "_msa.csv")
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infile_msa = paste0("/srv/shiny-server/git/Data/", drug, "/output/", in_filename_msa)
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print(infile_msa)
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msa1 = read.csv(infile_msa, header = F)
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msa_seq = msa1$V1
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infile_fasta = paste0("/srv/shiny-server/git/Data/", drug, "/input/", tolower(gene), "2_f2.fasta")
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print(infile_fasta)
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msa2 = read.csv(infile_fasta, header = F)
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wt_seq = msa2$V1
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target_name=paste0(gene, '_unified_msa')
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#print(target_name)
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invisible(assign(target_name, list(msa_seq = msa_seq, wt_seq = wt_seq), envir = .GlobalEnv))
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}
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#### Local Functions ####
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# Generate a conditionalPanel() for a given graph function and sidebar name combination
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generate_conditionalPanel = function(tab_name, plot_function, plot_df){
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# e.g.: list("lin_count_bp_diversity", "Lineage diversity count")
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cond=paste0("input.sidebar == '", tab_name, "'")
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conditionalPanel(condition=cond, box(
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title=tab_name
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, status = "info"
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, width=NULL
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, plotOutput(plot_function
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, click = "plot_click") %>% withSpinner(color="#0dc5c1")
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# , plotOutput(plot_function, click = "plot_click")
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)
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)
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}
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# FIXME: passing in the per-plot params is broken
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lin_sc=function(x, all_lineages = F, ...){
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lf_var = get(paste0(x,"_lin_lf"))
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wf_var = get(paste0(x,"_lin_wf"))
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cowplot::plot_grid(lin_count_bp_diversity(wf_var, all_lineages, ...), lin_count_bp(lf_var, all_lineages, ...))
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}
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options(shiny.port = 8000)
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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(shiny.launch.browser = FALSE)
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options(width=120)
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options(DT.options = list(scrollX = TRUE))
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################ STATIC GLOBALS ONLY ##############
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# never quite sure where "outdir" gets set :-|
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# using dataframes instead of lists lets us avoid use of map()
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plot_functions_df=data.frame(
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tab_name=c(
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"LogoP SNP",
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"Lineage Sample Count",
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"Site SNP count",
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"Stability SNP by site",
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"DM OM Plots",
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"Correlation",
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"Lineage Distribution",
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"Consurf",
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"LogoP OR",
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"LogoP ED"
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),
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plot_function=c(
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"LogoPlotSnps",
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"lin_sc",
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"site_snp_count_bp",
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"bp_stability_hmap",
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"lf_bp2",
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"my_corr_pairs",
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"lineage_distP",
|
||||
"wideP_consurf3",
|
||||
"LogoPlotCustomH",
|
||||
"LogoPlotMSA"
|
||||
),
|
||||
plot_df=c(
|
||||
"mutable_df3" ,
|
||||
"lin_lf",
|
||||
"mutable_df3",
|
||||
"merged_df3" ,
|
||||
"lf_duet" ,
|
||||
"corr_df_m3_f",
|
||||
"merged_df2",
|
||||
"merged_df3",
|
||||
"merged_df2",
|
||||
"unified_msa"
|
||||
)
|
||||
)
|
||||
|
||||
stability_boxes_df=data.frame(
|
||||
outcome_colname=c("duet_outcome",
|
||||
"foldx_outcome",
|
||||
"deepddg_outcome",
|
||||
"ddg_dynamut2_outcome",
|
||||
"mcsm_na_outcome",
|
||||
"mcsm_ppi2_outcome",
|
||||
"snap2_outcome",
|
||||
"consurf_outcome",
|
||||
"avg_stability_outcome"),
|
||||
stability_type=c(
|
||||
"DUET",
|
||||
"FoldX",
|
||||
"DeepDDG",
|
||||
"Dynamut2",
|
||||
"mCSM-NA",
|
||||
"mCSM-ppi2",
|
||||
"SNAP2",
|
||||
"Consurf",
|
||||
"Average"
|
||||
),
|
||||
stability_colname=c(
|
||||
"duet_scaled",
|
||||
"foldx_scaled",
|
||||
"deepddg_scaled",
|
||||
"ddg_dynamut2_scaled",
|
||||
"mcsm_na_scaled",
|
||||
"mcsm_ppi2_scaled",
|
||||
"snap2_scaled",
|
||||
"consurf_scaled",
|
||||
"avg_stability_scaled"
|
||||
)
|
||||
)
|
||||
|
||||
table_columns = c(
|
||||
"position",
|
||||
"mutationinformation",
|
||||
"sensitivity",
|
||||
"ligand_distance",
|
||||
"avg_lig_affinity",
|
||||
"avg_lig_affinity_outcome",
|
||||
"avg_stability",
|
||||
"avg_stability_outcome",
|
||||
"or_mychisq",
|
||||
"maf",
|
||||
"snap2_outcome",
|
||||
"consurf_outcome",
|
||||
"provean_outcome",
|
||||
"rsa",
|
||||
"kd_values" ,
|
||||
"rd_values"
|
||||
)
|
||||
|
||||
logoPlotSchemes <- list("chemistry"
|
||||
, "taylor"
|
||||
, "hydrophobicity"
|
||||
, "clustalx")
|
||||
dm_om_methods = c("DUET ΔΔG"
|
||||
, "Consurf"
|
||||
, "Deepddg ΔΔG"
|
||||
, "Dynamut2 ΔΔG"
|
||||
, "FoldX ΔΔG"
|
||||
, "Ligand affinity (log fold change)"
|
||||
, "mCSM-NA affinity ΔΔG"
|
||||
, "SNAP2")
|
||||
dm_om_map = hash(c(
|
||||
"DUET ΔΔG"
|
||||
, "Consurf"
|
||||
, "Deepddg ΔΔG"
|
||||
, "Dynamut2 ΔΔG"
|
||||
, "FoldX ΔΔG"
|
||||
, "Ligand affinity (log fold change)"
|
||||
, "mCSM-NA affinity ΔΔG"
|
||||
, "SNAP2"
|
||||
), c(
|
||||
"lf_duet"
|
||||
,"lf_consurf"
|
||||
,"lf_deepddg"
|
||||
,"lf_dynamut2"
|
||||
,"lf_foldx"
|
||||
,"lf_mcsm_lig"
|
||||
,"lf_mcsm_na"
|
||||
,"lf_snap2"
|
||||
)
|
||||
)
|
||||
#### target_map: handy gene/drug mapping hash ####
|
||||
target_map = hash(
|
||||
c(
|
||||
"alr",
|
||||
"gid",
|
||||
"embb",
|
||||
"pnca",
|
||||
"rpob",
|
||||
"katg"),
|
||||
c(
|
||||
"cycloserine",
|
||||
"streptomycin",
|
||||
"ethambutol",
|
||||
"pyrazinamide",
|
||||
"rifampicin",
|
||||
"isoniazid")
|
||||
)
|
||||
|
||||
# load E V E R Y T H I N G
|
||||
lapply(c(
|
||||
"alr",
|
||||
"embb",
|
||||
"gid",
|
||||
"katg",
|
||||
"pnca",
|
||||
"rpob"
|
||||
),function(x){
|
||||
invisible(load_target_globals(x))
|
||||
invisible(load_msa_global(x)) # turn off to speed up start time at the expense of "LogoP ED"
|
||||
}
|
||||
)
|
||||
|
||||
consurf_palette1 = c("0" = "yellow2"
|
||||
, "1" = "cyan1"
|
||||
, "2" = "steelblue2"
|
||||
, "3" = "cadetblue2"
|
||||
, "4" = "paleturquoise2"
|
||||
, "5" = "thistle3"
|
||||
, "6" = "thistle2"
|
||||
, "7" = "plum2"
|
||||
, "8" = "maroon"
|
||||
, "9" = "violetred2")
|
||||
|
||||
consurf_palette2 = c("0" = "yellow2"
|
||||
, "1" = "forestgreen"
|
||||
, "2" = "seagreen3"
|
||||
, "3" = "palegreen1"
|
||||
, "4" = "darkseagreen2"
|
||||
, "5" = "thistle3"
|
||||
, "6" = "lightpink1"
|
||||
, "7" = "orchid3"
|
||||
, "8" = "orchid4"
|
||||
, "9" = "darkorchid4")
|
||||
|
||||
# decreasing levels mess legend
|
||||
# consurf_colours_LEVEL = c(
|
||||
# "0" = rgb(1.00,1.00,0.59)
|
||||
# , "9" = rgb(0.63,0.16,0.37)
|
||||
# , "8" = rgb(0.94,0.49,0.67)
|
||||
# , "7" = rgb(0.98,0.78,0.86)
|
||||
# , "6" = rgb(0.98,0.92,0.96)
|
||||
# , "5" = rgb(1.00,1.00,1.00)
|
||||
# , "4" = rgb(0.84,0.94,0.94)
|
||||
# , "3" = rgb(0.65,0.86,0.90)
|
||||
# , "2" = rgb(0.29,0.69,0.75)
|
||||
# , "1" = rgb(0.04,0.49,0.51)
|
||||
# )
|
||||
|
||||
consurf_colours = c(
|
||||
"0" = rgb(1.00,1.00,0.59)
|
||||
, "1" = rgb(0.04,0.49,0.51)
|
||||
, "2" = rgb(0.29,0.69,0.75)
|
||||
, "3" = rgb(0.65,0.86,0.90)
|
||||
, "4" = rgb(0.84,0.94,0.94)
|
||||
, "5" = rgb(1.00,1.00,1.00)
|
||||
, "6" = rgb(0.98,0.92,0.96)
|
||||
, "7" = rgb(0.98,0.78,0.86)
|
||||
, "8" = rgb(0.94,0.49,0.67)
|
||||
, "9" = rgb(0.63,0.16,0.37)
|
||||
)
|
||||
|
||||
|
||||
0
msa/server.R
Normal file
0
msa/server.R
Normal file
0
msa/ui.R
Normal file
0
msa/ui.R
Normal file
Loading…
Add table
Add a link
Reference in a new issue