LIEBER INSTITUTE for BRAIN DEVELOPMENT
https://twitter.com/raphg/status/1318244963927289857?s=20
Slide 3
Slide 3 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
https://www.biorxiv.org/content/10.1101/2020.09.04.283812v1.article-metrics
Slide 4
Slide 4 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Resources
● Pre-print:
https://www.biorxiv.org/content/10.1101/2020.09.04.283812
v1
○ Has 2 supplementary files
● Bioconductor package
○ http://bioconductor.org/packages/BayesSpace
○ https://github.com/edward130603/BayesSpace
○ https://edward130603.github.io/BayesSpace/
● Processing code: https://github.com/msto/spatial-datasets
Slide 5
Slide 5 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
2 main features: enhanced clustering + sub-spot level analysis
Slide 6
Slide 6 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Main logical/processing steps:
expression → PCs → sub-spot level modeling → high res PCs→ predicted expression
Slide 7
Slide 7 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Main parameter: number of clusters. Can be derived from the data too
Slide 8
Slide 8 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure 7 from our pre-print
https://www.biorxiv.org/content/10.1101/2020.02.28.969931v1.full.pdf
Slide 9
Slide 9 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Remembering ARI: from our pre-print
https://www.biorxiv.org/content/10.1101/2020.02.28.969931v1.full.pdf
Slide 10
Slide 10 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure 2: they do a better job! ^^
Slide 11
Slide 11 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure S1: their w
i
point towards outliers
Slide 12
Slide 12 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Some notes: positive and not so positive
● Bioconductor-friendly
● Will be a part of
Bioconductor 3.12
● We can **use** it
● BayesSpace (right now)
is only meant for 1
image at a time
● How long does it take to
run?
● Their analysis code is
actually not public (or
I haven’t found it) >.<
Slide 13
Slide 13 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure 3: can use marker genes & get some profiles at sub-spot level
Slide 14
Slide 14 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure 4: works for other tissue organizations too (non-laminar) &
when you don’t know the number of clusters (q)
Slide 15
Slide 15 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure 5: sub-spot level model works with simulated data
(from their own model)
Slide 16
Slide 16 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Figure S5: choosing the number of PCs is important too. Here they use PCs 1-7 for
melanoma, 1-15 for DLPFC. Check outliers, think about computing time vs biology.
Slide 17
Slide 17 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
stLearn
Uses deep learning on images
https://stlearn.readthedocs.io/
en/latest/stSME_clustering.ht
ml#Human-Brain-dorsolateral-
prefrontal-cortex-(DLPFC)
Slide 18
Slide 18 text
LIEBER INSTITUTE for BRAIN DEVELOPMENT
Some questions
● Will stLearn improve with improved images?
● How sure are we that w
i
captures outliers?
● How much will it be affected by artifacts?
○ Can we use it to QC spots?
○ If we drop spots, can we still run sub-spot? My guess: yes
● How would you apply it to all 12 DLPFC images?