Once you have covered the main functionality in ggseg
you will want to use it to plot the results of your data. In order to do this, your data must adhere to certain specifications, so that ggseg
can manage to merge your data with the atlas you are using. This means you need to be able to inspect and locate the way the regions you are working with are names in the internal atlas files. This vignette should provide the tools you need to figure these features out, and to manipulate your data to fit these requirements.
There are several ways you can inspect what the data in the atlas looks like. While each atlas has some small differences, they all share six main columns:
1. long - x-axis
2. lat - y-axis
3. area - name of area/network
4. hemi - hemisphere (left or right)
5. side - side of view (medial, lateral, sagittal or axial)
Most atlases also have a label
column, which are raw names assigned from the program run to segment/extract data.
This information is stored in a list of data.frames called atlas.info
, which is loaded when ggseg is loaded, just like the atlases and palettes.
## Loading required package: ggplot2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## area hemi side
## 1 somatomotor left lateral
## 322 default left lateral
## 605 limbic left medial
## 685 limbic left lateral
## 1253 somatomotor right lateral
## 1532 visual left medial
## 1672 visual right medial
## 1809 frontoparietal right lateral
## 2043 ventral attention right lateral
## 2077 default right medial
## 2260 ventral attention right medial
## 2543 default left medial
## 2715 dorsal attention left lateral
## 2985 dorsal attention right lateral
## 3228 ventral attention left medial
## 3449 somatomotor right medial
## 3588 somatomotor left medial
## 3691 visual right lateral
## 3783 visual left lateral
## 3985 limbic right medial
## 4069 default right lateral
## 4231 dorsal attention right medial
## 4398 limbic right lateral
## 4541 dorsal attention left medial
## 4808 ventral attention left lateral
## 5425 frontoparietal left lateral
## 6181 frontoparietal left medial
## 6582 frontoparietal right medial
Here you can see information about the yeo7
atlas, and the main attributes of this atlas. If you want to use external data with your ggseg
plot, you will need to make sure that your data has at least one column corresponding in name and content with another in the atlas you are using.
For instance, here we make some data for the “default” and “visual” networks in the yeo7
atlas, and two p values for those two networks.
## area p
## 1 default 0.03
## 2 visual 0.60
Notice you we have spelled bothe the column name and the area names exactly as they appear in the data. This is necessary for the merging within the ggseg
function to work properly. This merge can be attempted before supplying the data to ggseg
to see if there are any errors.
## Joining, by = "area"
## long lat area hemi side network label group
## 1 0.518175 0.17936 somatomotor left lateral 2 lh_7Networks_2 0.1
## 2 0.517253 0.17964 somatomotor left lateral 2 lh_7Networks_2 0.1
## 3 0.512315 0.17985 somatomotor left lateral 2 lh_7Networks_2 0.1
## 4 0.508193 0.18240 somatomotor left lateral 2 lh_7Networks_2 0.1
## 5 0.507867 0.18354 somatomotor left lateral 2 lh_7Networks_2 0.1
## 6 0.507650 0.18500 somatomotor left lateral 2 lh_7Networks_2 0.1
## 7 0.511665 0.19775 somatomotor left lateral 2 lh_7Networks_2 0.1
## 8 0.511990 0.19906 somatomotor left lateral 2 lh_7Networks_2 0.1
## 9 0.516819 0.20529 somatomotor left lateral 2 lh_7Networks_2 0.1
## 10 0.541885 0.23611 somatomotor left lateral 2 lh_7Networks_2 0.1
## id order p
## 1 0 1 NA
## 2 0 2 NA
## 3 0 3 NA
## 4 0 4 NA
## 5 0 5 NA
## 6 0 6 NA
## 7 0 7 NA
## 8 0 8 NA
## 9 0 9 NA
## 10 0 10 NA
No errors! Yes, the p
column is seemingly full of NA
s, but that is just because the top of the data is the somatomotor network, which we did not supply any p values for, so it has been populated with NA
s. We can sort the data differently, so we can see the p
has been added correctly.
## Joining, by = "area"
## long lat area hemi side network label group id
## 1 0.037056 0.26139 default left lateral 7 lh_7Networks_7 1.1 1
## 2 0.032444 0.26546 default left lateral 7 lh_7Networks_7 1.1 1
## 3 0.016711 0.28956 default left lateral 7 lh_7Networks_7 1.1 1
## 4 0.007542 0.31685 default left lateral 7 lh_7Networks_7 1.1 1
## 5 0.006239 0.32254 default left lateral 7 lh_7Networks_7 1.1 1
## 6 0.005317 0.32449 default left lateral 7 lh_7Networks_7 1.1 1
## 7 0.004667 0.33524 default left lateral 7 lh_7Networks_7 1.1 1
## 8 0.001954 0.34543 default left lateral 7 lh_7Networks_7 1.1 1
## 9 0.000000 0.34711 default left lateral 7 lh_7Networks_7 1.1 1
## 10 0.000000 0.36040 default left lateral 7 lh_7Networks_7 1.1 1
## order p
## 1 1 0.03
## 2 2 0.03
## 3 3 0.03
## 4 4 0.03
## 5 5 0.03
## 6 6 0.03
## 7 7 0.03
## 8 8 0.03
## 9 9 0.03
## 10 10 0.03
If you need your data to be matched on several columns, the approach is the same. Add the column you want to match on, with the exact same name, and make sure it’s content matches the content of the same column in the data.
## area p hemi
## 1 default 0.03 left
## 2 visual 0.60 left
## Joining, by = c("area", "hemi")
## long lat area hemi side network label group id
## 1 0.037056 0.26139 default left lateral 7 lh_7Networks_7 1.1 1
## 2 0.032444 0.26546 default left lateral 7 lh_7Networks_7 1.1 1
## 3 0.016711 0.28956 default left lateral 7 lh_7Networks_7 1.1 1
## 4 0.007542 0.31685 default left lateral 7 lh_7Networks_7 1.1 1
## 5 0.006239 0.32254 default left lateral 7 lh_7Networks_7 1.1 1
## 6 0.005317 0.32449 default left lateral 7 lh_7Networks_7 1.1 1
## 7 0.004667 0.33524 default left lateral 7 lh_7Networks_7 1.1 1
## 8 0.001954 0.34543 default left lateral 7 lh_7Networks_7 1.1 1
## 9 0.000000 0.34711 default left lateral 7 lh_7Networks_7 1.1 1
## 10 0.000000 0.36040 default left lateral 7 lh_7Networks_7 1.1 1
## order p
## 1 1 0.03
## 2 2 0.03
## 3 3 0.03
## 4 4 0.03
## 5 5 0.03
## 6 6 0.03
## 7 7 0.03
## 8 8 0.03
## 9 9 0.03
## 10 10 0.03
Notice how the message now states that it is joining by = c("area", "hemi")
. The merge function has recognized that there are two equally named columns, and assumes (in this case correctly) that these are equivalent.
Notice that everything is case-sensitive, so writing Area
or Left
will not result in matching.
ggseg
When you have managed to create data that merges nicely with the atlas, you can go ahead and supply it to the function.
You can actually also supply it directly as an atlas. For instance, if you had saved the merged data from the previous steps, you can supply this directly to the atlas
option.
## Joining, by = c("area", "hemi")
It is this possibility of supplying a custom atlas that gives you particular flexibility, though a little tricky to begin with. As mentioned in the introductory vignette, if you plan on using faceting, mergin the data and supplying it as an atlas is the way to go. If you do not, you will get unwanted results. Lets do a recap of the unwanted results:
someData = data.frame(
area = rep(c("transverse temporal", "insula",
"pre central","superior parietal"),2),
p = sample(seq(0,.5,.001), 8),
AgeG = c(rep("Young",4), rep("Old",4)),
stringsAsFactors = FALSE)
ggseg(data=someData, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme(legend.position = "bottom")
See how you have three facets, when you only have 2 groups, and that the “background” brain is not printed in your two groups. This is because for ggplot, that is what the data looks like. In order to plot it as we wish, we must completely duplicate the atlas for each group/facet. I like using lists for lapply to do this.
# Initiate your list. Creating list newAtlas, that contains two data frames, one for each group
newAtlas = list(Young = someData %>% filter(AgeG %in% "Young"),
Old = someData %>% filter(AgeG %in% "Old"))
newAtlas
## $Young
## area p AgeG
## 1 transverse temporal 0.409 Young
## 2 insula 0.256 Young
## 3 pre central 0.382 Young
## 4 superior parietal 0.096 Young
##
## $Old
## area p AgeG
## 1 transverse temporal 0.478 Old
## 2 insula 0.186 Old
## 3 pre central 0.301 Old
## 4 superior parietal 0.497 Old
# Use list apply (lapply) to do the same operation on each element of the list.
# Here we join each data.frame with the atlas of choice ("dkt"), and make sure the
# group colum "AgeG" has it's group name populated in the column entirely
newAtlas = lapply(newAtlas, function(x) x %>% full_join(dkt) %>% mutate(AgeG = unique(x$AgeG)))
## Joining, by = "area"
## Joining, by = "area"
## $Young
## area p AgeG long lat id hemi side acronym
## 1 transverse temporal 0.409 Young 2.73519 2.27969 20 left lateral trnt
## 2 transverse temporal 0.409 Young 2.76025 2.30942 20 left lateral trnt
## 3 transverse temporal 0.409 Young 2.83864 2.39596 20 left lateral trnt
## 4 transverse temporal 0.409 Young 2.95078 2.50072 20 left lateral trnt
## 5 transverse temporal 0.409 Young 3.07709 2.58665 20 left lateral trnt
## lobe label group order
## 1 temporal lh_transversetemporal 20.1 1
## 2 temporal lh_transversetemporal 20.1 2
## 3 temporal lh_transversetemporal 20.1 3
## 4 temporal lh_transversetemporal 20.1 4
## 5 temporal lh_transversetemporal 20.1 5
##
## $Old
## area p AgeG long lat id hemi side acronym
## 1 transverse temporal 0.478 Old 2.73519 2.27969 20 left lateral trnt
## 2 transverse temporal 0.478 Old 2.76025 2.30942 20 left lateral trnt
## 3 transverse temporal 0.478 Old 2.83864 2.39596 20 left lateral trnt
## 4 transverse temporal 0.478 Old 2.95078 2.50072 20 left lateral trnt
## 5 transverse temporal 0.478 Old 3.07709 2.58665 20 left lateral trnt
## lobe label group order
## 1 temporal lh_transversetemporal 20.1 1
## 2 temporal lh_transversetemporal 20.1 2
## 3 temporal lh_transversetemporal 20.1 3
## 4 temporal lh_transversetemporal 20.1 4
## 5 temporal lh_transversetemporal 20.1 5
# Now, the each data.frame in our list has the exact same columns,
# so we can easily append the dataframes by row.
newAtlas = newAtlas %>% bind_rows()
newAtlas %>% head(5)
## area p AgeG long lat id hemi side acronym
## 1 transverse temporal 0.409 Young 2.73519 2.27969 20 left lateral trnt
## 2 transverse temporal 0.409 Young 2.76025 2.30942 20 left lateral trnt
## 3 transverse temporal 0.409 Young 2.83864 2.39596 20 left lateral trnt
## 4 transverse temporal 0.409 Young 2.95078 2.50072 20 left lateral trnt
## 5 transverse temporal 0.409 Young 3.07709 2.58665 20 left lateral trnt
## lobe label group order
## 1 temporal lh_transversetemporal 20.1 1
## 2 temporal lh_transversetemporal 20.1 2
## 3 temporal lh_transversetemporal 20.1 3
## 4 temporal lh_transversetemporal 20.1 4
## 5 temporal lh_transversetemporal 20.1 5
# We can now supply the newAtlas as an atlas to ggseg
ggseg(atlas=newAtlas, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
theme(legend.position = "bottom")
This whole procedure can be piped together, so you dont have to save all the intermediate steps.
newAtlas = list(Young = someData %>% filter(AgeG %in% "Young"),
Old = someData %>% filter(AgeG %in% "Old")) %>%
lapply(function(x) x %>% full_join(dkt) %>% mutate(AgeG = unique(x$AgeG))) %>%
bind_rows()
## Joining, by = "area"
## Joining, by = "area"
ggseg(atlas=newAtlas, colour="white", mapping=aes(fill=p)) +
facet_wrap(~AgeG, ncol=1) +
scale_fill_gradientn(colours = c("royalblue","firebrick","goldenrod"),na.value="grey")