Unverified Commit 9d308f5b authored by Marius van den Beek's avatar Marius van den Beek Committed by GitHub
Browse files

Merge pull request #16616 from neoformit/isee-fix 

[23.1] Remove render.py script from iSEE
parents 9f2b5702 517ca97c

tools/interactive/interactivetool_isee.xml

+68 −12
Original line number Diff line number Diff line
@@ -31,16 +31,6 @@ cp '$isee_script' '$outfile' && 
  <configfiles>

    <configfile name="isee_script"><![CDATA[



## Import render function to template R Shiny app code

## -----------------------------------------------------------------------------

#import os

#import importlib.util

#set modpath = $os.path.join($__tool_directory__, "isee/render.py")

#set spec = $importlib.util.spec_from_file_location("render", $modpath)

#set render = $importlib.util.module_from_spec(spec)

#$spec.loader.exec_module(render)





## Stop warning messages being emitted from R while still allowing genuine job failure

## -----------------------------------------------------------------------------

devNull <- file("/dev/null", open = "wt")
@@ -54,8 +44,74 @@ library(HDF5Array) 


sce_path <- 'sce'

sce <- loadHDF5SummarizedExperiment(sce_path)

sce <- registerAppOptions(sce, color.maxlevels=40)



categorical_color_fun <- function(n){

  if (n <= 37) {

    # Less than 37 colours, use something from colour brewer

    # (joining a bunch of palettes, best colours up front)

    multiset <-  c(

        RColorBrewer::brewer.pal(9, "Set1"),

        RColorBrewer::brewer.pal(8, "Set2"),

        RColorBrewer::brewer.pal(12, "Set3"),

        RColorBrewer::brewer.pal(8, "Dark2"))

    return(multiset[1:n])

  }

  else {

    # More that 37, well at least it looks pretty

    return(rainbow(n))

  }

}





$render.app()

ecm <- ExperimentColorMap(



  # The default is viridis::viridis

  # https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html#the-color-scales

  # Setting continous is entirely a matter of taste

  # Some find magma easier to read than viridis



  all_continuous = list(

    assays  = viridis::magma,

    colData = viridis::magma,

    rowData = viridis::magma

  ),

  all_discrete = list(

    colData = categorical_color_fun,

    rowData = categorical_color_fun

  )

)





# These options are all sce-contents agnostic.

initial_plots <- c(



  # Show umap with clusters by default

  ReducedDimensionPlot(

                   DataBoxOpen=TRUE,

                   ColorBy="Column data",

                   VisualBoxOpen=TRUE,

                   PanelWidth=6L),



  # Show gene expression plot separated (and coloured) by cluster, by default.

  FeatureAssayPlot(XAxis = "Column data",

                   DataBoxOpen=TRUE,

                   VisualBoxOpen=TRUE,

                   ColorBy="Column data",

                   PanelWidth=6L

                   ),

  # Gene list is better wide

  RowDataTable(PanelWidth=12L),



  # For cell level observations (QC.)

  ColumnDataPlot(PanelWidth=6L,

                 DataBoxOpen=TRUE,

                 VisualBoxOpen=TRUE )

)



app <- iSEE(sce,

            colormap=ecm,

            initial=initial_plots)



shiny::runApp(app, host="0.0.0.0", port=8888, quiet=TRUE, launch.browser=FALSE)

tools/interactive/isee/render.py

deleted100644 → 0
+0 −147
Original line number Diff line number Diff line 
"""Pre-configured iSEE options for selection by user.



Currently user configuration is NOT IMPLEMENTED - the code is here but option

is hidden from user in the tool form. Without ``custom`` selection, this simply

returns a DEFAULT iSEE configuration (defined at the bottom of this file).



These are preconfigured iSEE parameters that can be chosen by the user

as multiple choice (checkbox/select) input fields. Each parameter

(e.g. "initial") has a list of options whose indices should match the value of

the input field in the tool XML.



In all likelihood, the majority of options in

iSEE don't make sense without first being able to sniff the data and so are not

feasible to expose in the Galaxy tool form. Currently you will see that only

plot type and width can be exposed.

"""





def app():

    """Render R code to create iSEE app from user input."""

    return DEFAULT





def render_plots(call, plots):

    """Render plot calls from user input."""

    if not plots:

        return call

    plot_calls_list = [

        get_render_func(plot)(

            # user plot params as kwargs here

        )

        for plot in plots

    ]

    plot_calls = ",\ninitial=c(\n" + ",\n".join(plot_calls_list) + ")"

    return call + plot_calls





def get_render_func(plot):

    """Return the appropriate function to render plot."""

    # This is probably broken and unused

    return OPTIONS["plots"][plot["plot_types"]["plot_type"].value]  # type: ignore[index]





def reduced_dimension_plot(pw="6L"):

    """Render a ReducedDimensionPlot object call."""

    return f"""ReducedDimensionPlot(

        PanelWidth={pw})"""





def feature_assay_plot(pw="6L"):

    """Render a FeatureAssayPlot object call."""

    return f"""FeatureAssayPlot(

        PanelWidth={pw})"""





def row_data_table(pw="12L"):

    """Render a RowDataTable object call."""

    return f"RowDataTable(PanelWidth={pw})"





def column_data_plot(pw="6L"):

    """Render a ColumnDataPlot object call."""

    return f"ColumnDataPlot(PanelWidth={pw})"





OPTIONS = {

    "plots": {

        "reduced_dimension_plot": reduced_dimension_plot,

        "feature_assay_plot": feature_assay_plot,

        "row_data_table": row_data_table,

        "column_data_plot": column_data_plot,

    },

    "colormaps": {},

    "extra": {},

}





DEFAULT = """

sce <- registerAppOptions(sce, color.maxlevels=40)



categorical_color_fun <- function(n){

  if (n <= 37) {

    # Less than 37 colours, use something from colour brewer

    # (joining a bunch of palettes, best colours up front)

    multiset <-  c(

        RColorBrewer::brewer.pal(9, "Set1"),

        RColorBrewer::brewer.pal(8, "Set2"),

        RColorBrewer::brewer.pal(12, "Set3"),

        RColorBrewer::brewer.pal(8, "Dark2"))

    return(multiset[1:n])

  }

  else {

    # More that 37, well at least it looks pretty

    return(rainbow(n))

  }

}





ecm <- ExperimentColorMap(



  # The default is viridis::viridis

  # https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html#the-color-scales

  # Setting continous is entirely a matter of taste

  # Some find magma easier to read than viridis



  all_continuous = list(

    assays  = viridis::magma,

    colData = viridis::magma,

    rowData = viridis::magma

  ),

  all_discrete = list(

    colData = categorical_color_fun,

    rowData = categorical_color_fun

  )

)





# These options are all sce-contents agnostic.

initial_plots <- c(



  # Show umap with clusters by default

  ReducedDimensionPlot(

                   DataBoxOpen=TRUE,

                   ColorBy="Column data",

                   VisualBoxOpen=TRUE,

                   PanelWidth=6L),



  # Show gene expression plot separated (and coloured) by cluster, by default.

  FeatureAssayPlot(XAxis = "Column data",

                   DataBoxOpen=TRUE,

                   VisualBoxOpen=TRUE,

                   ColorBy="Column data",

                   PanelWidth=6L

                   ),

  # Gene list is better wide

  RowDataTable(PanelWidth=12L),



  # For cell level observations (QC.)

  ColumnDataPlot(PanelWidth=6L,

                 DataBoxOpen=TRUE,

                 VisualBoxOpen=TRUE )

)



app <- iSEE(sce,

            colormap=ecm,

            initial=initial_plots)

"""