panels.qmd

---
title: "Panels"
---

This page showcases panels implemented in [iSEE][bioc-iSEE] and its known extensions.

Panels are grouped by package in which their are implemented (see the floating table of contents on the right).

Each panel is introduced by a brief description above a single screenshot that illustrates a representative output,
and the code used to produce that particular panel output in a live app.

::: {.callout-note}
Bear in mind that all those panel classes come with many options to alter their respective outputs.
This gallery showcases only a fraction of what each of those panels can do.
In all likelihood, if a panel seems to do *almost* what you have in mind, then there are options to make it do *exactly* that.
Otherwise, options can be added, and more panel classes can be created; check out our [resources](./resources.html) to learn how!
:::

## iSEE

### ColumnDataPlot

Visualise any combination of sample metadata stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `ColumnDataPlot` panel class.](images/panels/iSEE/ColumnDataPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

# launch the app itself ----

app <- iSEE(sce, initial = list(
  ColumnDataPlot(
    PanelWidth = 8L,
    YAxis = "NREADS",
    XAxis = "Column data",
    XAxisColumnData = "driver_1_s",
    ColorBy = "Column data",
    ColorByColumnData = "driver_1_s",
    FacetColumnBy = "Column data",
    FacetColumnByColData = "Core.Type"
    )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::


### ColumnDataTable

Browser and filter sample metadata stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `ColumnDataTable` panel class.](images/panels/iSEE/ColumnDataTable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

# launch the app itself ----

app <- iSEE(sce, initial = list(
  ColumnDataTable(
    PanelWidth = 12L
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### ComplexHeatmapPlot

Visualise any number of features and samples in any assay stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `ComplexHeatmapPlot` panel class.](images/panels/iSEE/ComplexHeatmapPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)
library(tibble)
library(dplyr)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

rowData(sce)$ave_count <- rowMeans(assay(sce, "tophat_counts"))
rowData(sce)$n_cells <- rowSums(assay(sce, "tophat_counts") > 0)
rowData(sce)$row_var <- rowVars(assay(sce, "logcounts"))

# launch the app itself ----

# top 10 genes with highest variance in logcounts
gene_list <- c("Lamp5", "Fam19a1", "Cnr1", "Rorb", "Sparcl1", "Crym", "Lmo3",  "Serpine2", "Ddah1", "Cux2")

app <- iSEE(sce, initial = list(
  ComplexHeatmapPlot(
    PanelWidth = 12L,
    CustomRows = TRUE,
    CustomRowsText = paste0(paste0(gene_list, collapse = "\n"), "\n"),
    ColumnData = "driver_1_s",
    RowData = "row_var"
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### FeatureAssayPlot

Visualise up to two features in any assay stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `FeatureAssayPlot` panel class.](images/panels/iSEE/FeatureAssayPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----
sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

# launch the app itself ----

app <- iSEE(sce, initial = list(
  FeatureAssayPlot(
    PanelWidth = 12L,
    YAxisFeatureName = "Rorb",
    XAxis = "Column data", XAxisColumnData = "driver_1_s",
    ColorBy = "Column data", ColorByColumnData = "driver_1_s",
    FacetColumnBy = "Column data", FacetColumnByColData = "Core.Type"
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### ReducedDimensionPlot

Visualise any two dimensions of any dimensionality reduction result stored in a [`SingleCellExperiment`][bioc-SingleCellExperiment] object.

![The `ReducedDimensionPlot` panel class.](images/panels/iSEE/ReducedDimensionPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

sce <- runPCA(sce, ncomponents=4)
sce <- runUMAP(sce)

# launch the app itself ----

app <- iSEE(sce, initial = list(
  ReducedDimensionPlot(
    PanelWidth = 8L,
    Type = "UMAP",
    ColorBy = "Column data", ColorByColumnData = "driver_1_s")))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### RowDataPlot

Visualise any combination of feature metadata stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `RowDataPlot` panel class.](images/panels/iSEE/RowDataPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

rowData(sce)$row_var <- rowVars(assay(sce, "logcounts"))
rowData(sce)$n_cells <- rowSums(assay(sce, "logcounts") > 0)

# launch the app itself ----

app <- iSEE(sce, initial = list(
  RowDataPlot(
    PanelWidth = 12L,
    YAxis = "row_var",
    XAxis = "Row data",
    XAxisRowData = "n_cells"
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### RowDataTable

Browse and filter feature metadata stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `RowDataTable` panel class.](images/panels/iSEE/RowDataTable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

rowData(sce)$ave_count <- rowMeans(assay(sce, "tophat_counts"))
rowData(sce)$n_cells <- rowSums(assay(sce, "tophat_counts") > 0)

# launch the app itself ----

app <- iSEE(sce, initial = list(
  RowDataTable(
    PanelWidth = 12L
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

### SampleAssayPlot

Visualise up to two samples in any assay stored in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `SampleAssayPlot` panel class.](images/panels/iSEE/SampleAssayPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

rowData(sce)$row_var <- rowVars(assay(sce, "logcounts"))

# launch the app itself ----

app <- iSEE(sce, initial = list(
  SampleAssayPlot(
    PanelWidth = 12L,
    YAxisSampleName = "SRR2140028",
    XAxis = "Sample name", XAxisSampleName = "SRR2140022",
    ColorBy = "Row data", ColorByRowData = "row_var"
  )
))

if (interactive()) {
  shiny::runApp(app, port=1234)
}
```
:::

## iSEEde

### DETable

Browse and filter any table of differential expression results embedded in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `DETable` panel class.](images/panels/iSEEde/DETable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEde)
library(airway)
library(DESeq2)

# Example data ----

data("airway")
airway$dex <- relevel(airway$dex, "untrt")

dds <- DESeqDataSet(airway, ~ 0 + dex + cell)

dds <- DESeq(dds)
res_deseq2 <- results(dds, contrast = list("dextrt", "dexuntrt"))

# iSEE / iSEEde ---

airway <- embedContrastResults(res_deseq2, airway, name = "dex: trt vs untrt")

app <- iSEE(airway, initial = list(
  DETable(
    PanelWidth = 12L,
    ContrastName="dex: trt vs untrt",
    RoundDigits = TRUE
  )
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

### LogFCLogFCPlot

Visualise the log-transformed fold-changes of any two differential expression contrasts embedded in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `LogFCLogFCPlot` panel class.](images/panels/iSEEde/LogFCLogFCPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library("iSEEde")
library("airway")
library("edgeR")
library("DESeq2")
library("iSEE")

# Example data ----

data("airway")
airway$dex <- relevel(airway$dex, "untrt")

# DESeq2 ----

dds <- DESeqDataSet(airway, ~ 0 + dex + cell)

dds <- DESeq(dds)
res_deseq2 <- results(dds, contrast = list("dextrt", "dexuntrt"))

airway <- embedContrastResults(res_deseq2, airway, name = "DESeq2")

# edgeR ----

design <- model.matrix(~ 0 + dex + cell, data = colData(airway))

fit <- glmFit(airway, design, dispersion = 0.1)
lrt <- glmLRT(fit, contrast = c(-1, 1, 0, 0, 0))
res_edger <- topTags(lrt, n = Inf)

airway <- embedContrastResults(res_edger, airway, name = "edgeR")

# iSEE / iSEEde ---

airway <- registerAppOptions(airway, factor.maxlevels = 30, color.maxlevels = 30)

app <- iSEE(airway, initial = list(
  LogFCLogFCPlot(
    ContrastNameX = "DESeq2", ContrastNameY = "edgeR",
    ColorBy = "Row data",
    ColorByRowData = "gene_biotype",
    PanelWidth = 12L)
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

### MAPlot

Visualise the M and A values of any differential expression contrast embedded in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `MAPlot` panel class.](images/panels/iSEEde/MAPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library("iSEEde")
library("airway")
library("DESeq2")
library("iSEE")

# Example data ----

data("airway")
airway$dex <- relevel(airway$dex, "untrt")
rowData(airway)$seq_strand <- factor(rowData(airway)$seq_strand)

dds <- DESeqDataSet(airway, ~ 0 + dex + cell)

dds <- DESeq(dds)
res_deseq2 <- results(dds, contrast = list("dextrt", "dexuntrt"))

# iSEE / iSEEde ---

airway <- embedContrastResults(res_deseq2, airway, name = "dex: trt vs untrt")

app <- iSEE(airway, initial = list(
  MAPlot(
    PanelWidth = 12L,
    ContrastName="dex: trt vs untrt",
    ColorBy = "Row data",
    ColorByRowData = "seq_strand"
  )
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

### VolcanoPlot

Visualise the P values and log-transformed fold-changes of any differential expression contrast embedded in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `VolcanoPlot` panel class.](images/panels/iSEEde/VolcanoPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEde)
library(airway)
library(DESeq2)

# Example data ----

data("airway")
airway$dex <- relevel(airway$dex, "untrt")
rowData(airway)$seq_strand <- factor(rowData(airway)$seq_strand)

dds <- DESeqDataSet(airway, ~ 0 + dex + cell)

dds <- DESeq(dds)
res_deseq2 <- results(dds, contrast = list("dextrt", "dexuntrt"))

# iSEE / iSEEde ---

airway <- embedContrastResults(res_deseq2, airway, name = "dex: trt vs untrt")

app <- iSEE(airway, initial = list(
  VolcanoPlot(
    PanelWidth = 12L,
    ContrastName="dex: trt vs untrt",
    ColorBy = "Row data",
    ColorByRowData = "seq_strand"
  )
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

## iSEEhex

### ReducedDimensionHexPlot

Same as [`ReducedDimensionPlot`](#reduceddimensionplot) but summarised using hexagonal bins.

![The `ReducedDimensionHexPlot` panel class.](images/panels/iSEEhex/ReducedDimensionHexPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEhex)
library(scRNAseq)
library(scater)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

sce <- runPCA(sce, ncomponents=4)

# launch the app itself ----

if (interactive()) {
  iSEE(sce, initial=list(
    ReducedDimensionHexPlot(PanelWidth = 12L, BinResolution=50)
  ))
}
```
:::

## iSEEpathways

### FgseaEnrichmentPlot

GSEA enrichment plot produced by the [*fgsea*](https://bioconductor.org/packages/fgsea/) package.

![The `FgseaEnrichmentPlot` panel class.](images/panels/iSEEpathways/FgseaEnrichmentPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(fgsea)
library(iSEEpathways)

# Example data ----

set.seed(1)
simulated_data <- simulateExampleData()

pathways_list <- simulated_data[["pathwaysList"]]
features_stat <- simulated_data[["featuresStat"]]
se <- simulated_data[["summarizedexperiment"]]

# fgsea ----

set.seed(42)
fgseaRes <- fgsea(pathways = pathways_list,
  stats    = features_stat,
  minSize  = 15,
  maxSize  = 500)
fgseaRes <- fgseaRes[order(pval), ]

# iSEE / iSEEpathways ---

se <- embedPathwaysResults(fgseaRes, se, name = "fgsea", class = "fgsea", pathwayType = "simulated",
  pathwaysList = pathways_list, featuresStats = features_stat)

app <- iSEE(se, initial = list(
  FgseaEnrichmentPlot(ResultName="fgsea", PathwayId = "pathway_1350", PanelWidth = 12L)
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

### PathwaysTable

Browse and filter any table of gene set analysis results embedded in a [`SummarizedExperiment`][bioc-SummarizedExperiment] object.

![The `PathwaysTable` panel class.](images/panels/iSEEpathways/PathwaysTable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(fgsea)
library(iSEEpathways)

# Example data ----

set.seed(1)
simulated_data <- simulateExampleData()

pathways_list <- simulated_data[["pathwaysList"]]
features_stat <- simulated_data[["featuresStat"]]
se <- simulated_data[["summarizedexperiment"]]

# fgsea ----

set.seed(42)
fgseaRes <- fgsea(pathways = pathways_list,
  stats    = features_stat,
  minSize  = 15,
  maxSize  = 500)
fgseaRes <- fgseaRes[order(pval), ]

# iSEE ---

se <- embedPathwaysResults(fgseaRes, se, name = "fgsea", class = "fgsea", pathwayType = "simulated",
  pathwaysList = pathways_list, featuresStats = features_stat)

app <- iSEE(se, initial = list(
  PathwaysTable(ResultName="fgsea", PanelWidth = 12L)
))

if (interactive()) {
  shiny::runApp(app)
}
```
:::

## iSEEu

### AggregatedDotPlot

Represents groups of samples by dots, where colour scales with means assay value and size scales with proportion of non-zero values for selected features.

![The `AggregatedDotPlot` panel class.](images/panels/iSEEu/AggregatedDotPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(scRNAseq)
library(scater)
library(iSEEu)

# Example data ----

sce <- ReprocessedAllenData(assays="tophat_counts")

sce <- logNormCounts(sce, exprs_values="tophat_counts")

# launch the app itself ----

if (interactive()) {
  iSEE(
    sce,
    initial = list(
      AggregatedDotPlot(
        ColumnDataLabel="Primary.Type",
        CustomRowsText = "Rorb\nSnap25\nFoxp2",
        # PanelHeight = 500L,
        PanelWidth = 12L
      )
    )
  )
}
```
:::

### DynamicMarkerTable

A table that dynamically identifies marker genes for a subset of samples transmitted from another panel.
Comparisons are made between the active selection in the transmitting panel and either

* all non-selected points, if no saved selections are available, or
* each subset of points in each saved selection.

![The `DynamicMarkerTable` panel class, alongside a `ReducedDimensionPlot` panel from which it receives a selection of samples.](images/panels/iSEEu/DynamicMarkerTable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEu)
library(scRNAseq)
library(scater)
library(scran)

sce <- ReprocessedAllenData(assays="tophat_counts")
sce <- logNormCounts(sce, exprs_values="tophat_counts")
sce <- runPCA(sce, ncomponents=4)

if (interactive()) {
  iSEE(sce, initial=list(
    ReducedDimensionPlot(
      PanelWidth=4L,
      BrushData = list(
        lasso = NULL, closed = TRUE,
        mapping = list(x = "X", y = "Y"),
        coord = structure(c(
          -47.8, -41.9, -14.6, -13.6, -19.1, -27.3, -33.6, -44, -47.8,
          -23.6, -44.1, -56.4, -46.9, -26.4, -17.4, -6.2, -5.4, -23.6),
          dim = c(9L, 2L))
        )
    ),
    DynamicMarkerTable(
      PanelWidth=8L,
      ColumnSelectionSource="ReducedDimensionPlot1"
    )
  ))
}
```
:::

### DynamicReducedDimensionPlot

A dimensionality reduction plot that dynamically recomputes the coordinates for the samples, based on a subset of samples and features transmitted from other panels.

![The `DynamicReducedDimensionPlot` panel class, alongside a `ReducedDimensionPlot` panel from which it receives a selection of samples.](images/panels/iSEEu/DynamicReducedDimensionPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEu)
library(scRNAseq)
library(scater)

set.seed(1)
sce <- ReprocessedAllenData(assays="tophat_counts")
sce <- logNormCounts(sce, exprs_values="tophat_counts")
sce <- runPCA(sce, ncomponents=4)

if (interactive()) {
  iSEE(sce, initial=list(
    ReducedDimensionPlot(
      PanelWidth = 6L,
      BrushData = list(
        lasso = NULL, closed = TRUE,
        mapping = list(x = "X", y = "Y"),
        coord = structure(c(
          -47.8, -41.9, -14.6, -13.6, -19.1, -27.3, -33.6, -44, -47.8,
          -23.6, -44.1, -56.4, -46.9, -26.4, -17.4, -6.2, -5.4, -23.6),
          dim = c(9L, 2L))
      )
    ),
    DynamicReducedDimensionPlot(
      PanelWidth = 6L,
      Assay="logcounts",
      ColumnSelectionSource="ReducedDimensionPlot1"
    )
  ))
}
```
:::

### FeatureSetTable

A table where each row is itself a set of features (i.e., rows) and can be used to transmit such a feature set to another panel. 

![The `FeatureSetTable` panel class, alongside `RowDataPlot` and `RowDataTable` panels to which it transmits a feature set.](images/panels/iSEEu/FeatureSetTable.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
library(iSEE)
library(iSEEu)
library(scRNAseq)
library(scater)
library(scran)
library(org.Mm.eg.db)

sce <- LunSpikeInData(location=FALSE)

sce <- logNormCounts(sce)

rowData(sce) <- cbind(rowData(sce), modelGeneVarWithSpikes(sce, "ERCC"))

cmds <- createGeneSetCommands(collections="GO",
  organism="org.Mm.eg.db", identifier="ENSEMBL")
sce <- registerFeatureSetCommands(sce, cmds)

# Setting up the application.

gst <- FeatureSetTable(
  Selected = "GO:0002020"
)

rdp <- RowDataPlot(
  YAxis="total",
  XAxis="Row data", XAxisRowData="mean",
  ColorBy="Row selection",
  RowSelectionSource="FeatureSetTable1"
)

rdt <- RowDataTable(
  RowSelectionSource="FeatureSetTable1"
)

if (interactive()) {
  iSEE(sce, initial=list(gst, rdp, rdt))
}
```
:::

### LogFCLogFCPlot {#logfclogfcplot-iseeu}

Precursor to the [`iSEEde::LogFCLogFCPlot`](#logfclogfcplot) class.

::: {.callout-warning}
Deprecation imminent. Please use `iSEEde::LogFCLogFCPlot()` instead.
:::

![The `LogFCLogFCPlot` panel class.](images/panels/iSEEu/LogFCLogFCPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
# Making up some results:
se <- SummarizedExperiment(matrix(rnorm(10000), 1000, 10))
rownames(se) <- paste0("GENE_", seq_len(nrow(se)))
rowData(se)$PValue1 <- runif(nrow(se))
rowData(se)$LogFC1 <- rnorm(nrow(se))
rowData(se)$PValue2 <- runif(nrow(se))
rowData(se)$LogFC2 <- rnorm(nrow(se))

if (interactive()) {
  iSEE(se, initial=list(
    LogFCLogFCPlot(
      PanelWidth = 12L,
      XAxisRowData="LogFC1", YAxis="LogFC2",
      XPValueField="PValue1", YPValueField="PValue2"
    )
  ))
}
```
:::

### MAPlot {#maplot-iseeu}

Precursor to the [`iSEEde::MAPlot`](#maplot) class.

::: {.callout-warning}
Deprecation imminent. Please use `iSEEde::MAPlot()` instead.
:::

![The `MAPlot` panel class.](images/panels/iSEEu/MAPlot.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
# Making up some results:
se <- SummarizedExperiment(matrix(rnorm(10000), 1000, 10))
rownames(se) <- paste0("GENE_", seq_len(nrow(se)))
rowData(se)$PValue <- runif(nrow(se))
rowData(se)$LogFC <- rnorm(nrow(se))
rowData(se)$AveExpr <- rnorm(nrow(se))

if (interactive()) {
  iSEE(se, initial=list(
    MAPlot(PanelWidth=12L)
  ))
}
```
:::

### MarkdownBoard

A panel providing an [aceEditor](https://ace.c9.io/) that can be used to take notes within the app.
Notes should be typed using Markdown syntax, and the panel continuously renders them in HTML format for preview.

![The `MarkdownBoard` panel class.](images/panels/iSEEu/MarkdownBoard.png)

::: {.callout-caution collapse="true"}
## Reproduce This Output

```{r, eval=FALSE}
if (interactive()) {
  iSEE(SummarizedExperiment(), initial=list(
    MarkdownBoard(
      PanelWidth = 12L,
      Content = paste0(c(
        "# Level 1 header",
        "## Level 2 header",
        "**Bold** and *italic*.",
        "[Link](https://isee.github.io/)",
        "* Bullet point\n"),
        collapse = "\n\n"
      )
    )
  ))
}
```
:::

<!-- Links -->

[bioc-iSEE]: https://bioconductor.org/packages/iSEE/
[bioc-SingleCellExperiment]: https://bioconductor.org/packages/SingleCellExperiment/
[bioc-SummarizedExperiment]: https://bioconductor.org/packages/SummarizedExperiment/