Creating Presentations in Quarto 101

Najla Abassi

Bio* Meeting - Methods Presentation

Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI)

14.10.2025

Why Quarto?

  • An open-source scientific & technical publishing system

  • Different programming languages

  • Different outputs

  • Reproducible & Shareable…

Styling & Layout

Incremental lists

Lists can optionally be displayed incrementally:

  • First item
  • Second item
  • Third item

Fragments

Incremental text display and animation with fragments:

Fade in

Fade in, then out

Slide up while fading in

Highlight red

Fade out

Column Layout

Tip

Note that there are five types of callouts, including: note, tip, warning, caution, and important.

Slide Transitions

The next few slides will transition using the slide transition

Transition Description
none No transition (default, switch instantly)
fade Cross fade
slide Slide horizontally
convex Slide at a convex angle
concave Slide at a concave angle
zoom Scale the incoming slide so it grows in from the center of the screen.

Code & Output

Code Blocks

# create res, a list to hold pseudobulk DE results for all contrasts
for (i in names(contrast)) {
  cat("Contrast: ", i,"\n")
  res <- pbDS(pb,
              design = mm,
              contrast = contrast[[i]],
              verbose = TRUE,
              BPPARAM = BiocParallel::MulticoreParam(6))
  
  results_list[[i]] <- res
}

# extract contrast
contrast_vtp_DMSO <- res$table$`VTP-DMSO`

# renaming columns
for (cell in names(contrast_vtp_DMSO)) {
  contrast_vtp_DMSO[[cell]] <-
  contrast_vtp_DMSO[[cell]] |>
  dplyr::rename(log2FoldChange = logFC,
                pvalue = p_val,
                padj = p_adj.loc)
  
  rownames(contrast_vtp_DMSO[[cell]]) <- contrast_vtp_DMSO[[cell]]$gene
}

# optional: update de + enrich list names
new_names <- c(
  "NK1 A+B" = "NK1_A_B",
  "NK1 C" = "NK1_C",
  "NK2" = "NK2",
  "NK3" = "NK3" ,
  "NKint" = "NKint"
)
names(contrast_vtp_DMSO) <- new_names[names(contrast_vtp_DMSO)]
names(func_res) <- new_names[names(func_res)]

# create dde object
dde <- DeeDeeExperiment(sce_NKcells, # sce object
                        de_results = contrast_vtp_DMSO, # DEA results
                        enrich_results = func_res # FEA results
                        )

Line Highlighting

# create res, a list to hold pseudobulk DE results for all contrasts
for (i in names(contrast)) {
  cat("Contrast: ", i,"\n")
  res <- pbDS(pb,
              design = mm,
              contrast = contrast[[i]],
              verbose = TRUE,
              BPPARAM = BiocParallel::MulticoreParam(6))
  
  results_list[[i]] <- res
}

# extract contrast
contrast_vtp_DMSO <- res$table$`VTP-DMSO`

# renaming columns
for (cell in names(contrast_vtp_DMSO)) {
  contrast_vtp_DMSO[[cell]] <-
  contrast_vtp_DMSO[[cell]] |>
  dplyr::rename(log2FoldChange = logFC,
                pvalue = p_val,
                padj = p_adj.loc)
  
  rownames(contrast_vtp_DMSO[[cell]]) <- contrast_vtp_DMSO[[cell]]$gene
}

# optional: update de + enrich list names
new_names <- c(
  "NK1 A+B" = "NK1_A_B",
  "NK1 C" = "NK1_C",
  "NK2" = "NK2",
  "NK3" = "NK3" ,
  "NKint" = "NKint"
)
names(contrast_vtp_DMSO) <- new_names[names(contrast_vtp_DMSO)]
names(func_res) <- new_names[names(func_res)]

# create dde object
dde <- DeeDeeExperiment(sce_NKcells, # sce object
                        de_results = contrast_vtp_DMSO, # DEA results
                        enrich_results = func_res # FEA results
                        )

Exacutable Code

Temperature and ozone level.

Exacutable code

library(ggplot2)

ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess")

Temperature and ozone level.

Exacutable code

library(ggplot2)

ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess")

Temperature and ozone level.

Tabsets

mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

We want to examine whether cars with higher horsepower (hp) tend to have lower fuel efficiency (mpg).

library(ggplot2)
ggplot(mtcars, aes(hp, mpg, color = am)) +
  geom_point() +
  geom_smooth(formula = y ~ x, method = "loess")

Extra Features …

Interactive Slides

Chalkboard

Free form drawing and slide annotations

Use the chalkboard button at the bottom left of the slide to toggle the chalkboard.

Use the notes canvas button at the bottom left of the slide to toggle drawing on top of the current slide.

You can also press to toggle the chalkboard or to toggle the notes canvas.

And More …

  • Speaker notes (press )
  • Custom themes
  • Touch optimized (presentations look great on mobile, swipe to navigate slides)
  • Footer & Logo (optionally specify custom footer per-slide or hide global footer)
  • Multiplex (allows your audience to follow the slides of the presentation you are controlling on their own phone, tablet or laptop).

Thank you for your attention ☺