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cytoviewer citation update #87

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Jan 5, 2024
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cytoviewer citation update #87

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bb609fc
cytoviewer citation and text updates
lassedochreden Jan 4, 2024
d129226
slighty text changes
lassedochreden Jan 5, 2024
775e473
updated changelog
lassedochreden Jan 5, 2024
16919aa
remove markdown header
lassedochreden Jan 5, 2024
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7 changes: 4 additions & 3 deletions 06-quality_control.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ sizes.
An easier and interactive way of observing segmentation quality is to use the
interactive image viewer provided by the
[cytoviewer](https://github.com/BodenmillerGroup/cytoviewer) R/Bioconductor
package [@Meyer2023]. Under "Image-level" > "Basic controls", up to six markers
package [@Meyer2024]. Under "Image-level" > "Basic controls", up to six markers
can be selected for visualization. The contrast of each marker can be adjusted.
Under "Image-level" > "Advanced controls", click the "Show cell outlines" box
to outline segmented cells on the images.
Expand All @@ -72,11 +72,12 @@ library(cytoviewer)

app <- cytoviewer(image = images,
mask = masks,
object = spe,
cell_id = "ObjectNumber",
img_id = "sample_id")

if (interactive()) {
shiny::runApp(app, launch.browser = TRUE)
shiny::runApp(app)
}
```

Expand Down Expand Up @@ -395,7 +396,7 @@ samples or batches of samples. Observing potential staining differences can be
crucial to assess data quality. We will use ridgeline visualizations to check
differences in staining patterns:

```{r ridges, message=FALSE, fig.width=7, fig.height=25}
```{r ridges, message=FALSE, warning = FALSE, fig.width=7, fig.height=25}
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multi_dittoPlot(spe, vars = rownames(spe)[rowData(spe)$use_channel],
group.by = "patient_id", plots = "ridgeplot",
assay = "exprs",
Expand Down
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