Use fig.height = 4 on the rmarkdown chunks to reduce the inst/doc/v…

…isiumStitched.html file size from like 9 mb to 6.4, which then reduces the package tarball to below 5 mb

Co-authored-by: Nick Eagles <[email protected]>

vignettes/visiumStitched.Rmd

@@ -187,7 +187,7 @@ memory footprints.
 ```{r "prep_fiji"}
 spe_input_dir <- tempdir()
 prep_fiji_coords(sample_info, out_dir = spe_input_dir)
-prep_fiji_image(sample_info, out_dir = spe_input_dir, lowres_max_size = 400)
+prep_fiji_image(sample_info, out_dir = spe_input_dir)
 ```
 
 ## Constructing the Object
@@ -240,7 +240,7 @@ variation across spots can bias the apparent distribution. Later, we'll show tha
 normalization is critical to producing a visually seamless transition between overlapping
 capture areas.
 
-```{r "explore_coords"}
+```{r "explore_coords", fig.height = 4}
 wm_genes <- rownames(spe)[
     match(c("MBP", "GFAP", "PLP1", "AQP4"), rowData(spe)$gene_name)
 ]
@@ -299,14 +299,14 @@ colData(spe) |>
 As a `SpatialExperiment`, the stitched data may be rotated or mirrored by group, such as with
 the `SpatialExperiment::rotateObject()` or `SpatialExperiment::mirrorObject()` functions.
 
-```{r "rotate"}
+```{r "rotate", fig.height=4}
 vis_gene(
-    rotateObject(spe, sample_id = "Br2719", degrees = 90),
+    rotateObject(spe, sample_id = "Br2719", degrees = 180),
     geneid = wm_genes, assayname = "counts", is_stitched = TRUE
 )
 ```
 
-```{r "mirror"}
+```{r "mirror", fig.height = 4}
 vis_gene(
     mirrorObject(spe, sample_id = "Br2719", axis = "v"),
     geneid = wm_genes, assayname = "counts", is_stitched = TRUE
@@ -322,7 +322,7 @@ data is log-normalized. Instead of performing normalization here, we'll fetch th
 object with [normalized](https://bioconductor.org/books/3.12/OSCA/normalization.html#normalization-by-deconvolution)
 counts from `spatialLIBD`, then plot a few white matter genes as before:
 
-```{r "fetch_norm"}
+```{r "fetch_norm", fig.height = 4}
 spe_norm <- fetch_data(type = "visiumStitched_brain_spe")
 
 wm_genes_ens <- rownames(spe_norm)[
@@ -337,7 +337,7 @@ vis_gene(
 
 Recall the unnormalized version of this plot, which is not nearly as clean:
 
-```{r "unnorm_plot"}
+```{r "unnorm_plot", fig.height = 4}
 vis_gene(
     spe,
     geneid = wm_genes, assayname = "counts", is_stitched = TRUE