FOGBoston2024

Festival of Genomics and Biodata 2024 in Boston workshop presentation https://festivalofgenomics.com/boston/en/page/workshops.

Transcript

1. @lcolladotor lcolladotor.github.io lcolladotor.github.io/bioc_team_ds An Introduction to spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data Leonardo Collado Torres, LIBD Investigator + Asst. Prof. Johns Hopkins Biostatistics Festival of Genomics & Biodata June 13 2024 Slides available at speakerdeck.com/lcolladotor
2. doi.org/10.1016/j.biopsych.2020.06.005 Michael Gandal @mikejg84 Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders
3. Background: Human DLPFC 3 slideshare.net Louise A Huuki-Myers @lahuuki
4. Zoom in: spatial omics Kristen R Maynard @kr_maynard Keri Martinowich @martinowk Stephanie C Hicks @stephaniehicks Andrew E Jaffe @andrewejaffe Stephanie C Page @CerceoPage
5. Visium Platform for Spatial Gene Expression Image from 10x Genomics - A slide contains 4 capture areas, each full of thousands of 55um-wide “spots” (often containing 1-10 cells) - Unique barcodes in each spot bind to particular genes; after sequencing, gene expression can be tied back to exact spots, forming a spatial map Kristen R. Maynard 5
6. bioconductor.org/packages/spatialLIBD Pardo et al, 2022 DOI 10.1186/s12864-022-08601-w Maynard, Collado-Torres, 2021 DOI 10.1038/s41593-020-00787-0 Brenda Pardo Abby Spangler @PardoBree @abspangler Louise A. Huuki-Myers @lahuuki
7. 2 pairs spatial adjacent replicates x subject = 12 sections 7 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0μm) Adjacent spatial replicates (300μm) PCP4 Maynard, Collado-Torres, et al, Nat Neuro, 2021
8. “Pseudo-bulking” collapses data: spot to layer level 8 Maynard, Collado-Torres, et al, Nat Neuro, 2021
9. DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 Andrew E Jaffe @andrewejaffe Kristen R Maynard @kr_maynard Keri Martinowich @martinowk
10. DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 DOI 10.1093/bioinformatics/btac299 Since Feb 2020 spatialLIBD::fetch_data() provides access to SpatialExperiment R/Bioconductor objects Stephanie C Hicks @stephaniehicks Lukas M Weber @lmweber
11. DOI: 10.1038/s41593-020-00787-0 twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29 twitter.com/CrowellHL/status/1597579271945715717 DOI 10.1093/bioinformatics/btac299 Since Feb 2020 spatialLIBD::fetch_data() provides access to SpatialExperiment R/Bioconductor objects
12. Pseudobulk to find DE genes DOI: 10.1038/s41593-020-00787-0
13. Single cell analysis workflow bioconductor.org/books/release/OSCA Solid foundational base for getting started with the world of spatial transcriptomics (with Visium)
14. Some challenges ⚠ with Visium 14
15. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper() • Spot QC ◦ Drop high mitochondria % spots? ◦ Drop spots with low UMI counts? ▪ Biological vs technical reasons • Spot clustering (spatially-aware) and/or manual annotation • Choosing a spot clustering resolution ◦ Can be guided by spatially cluster registration
16. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools)
17. Align your data with spaceranger --count 17 www.10xgenomics.com/support/software/space-ranger/latest
18. Loupe Browser Manually: • Align fiducial frame • Select spots “in tissue” lmweber.org/Visium- data-preprocessing/
19. spatialLIBD: visualize SpatialExperiment R objects 19 bioconductor.org/packages/spatialLIBD
20. spatialLIBD::vis_*() functions
21. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper()
22. spatialLIBD::read10xVisiumWrapper() 22 bioconductor.org/packages/spatialLIBD
23. spatialLIBD::run_app() 23 libd.shinyapps.io/Habenula_Visium_raw/ Default non-spatially aware graph based clustering results + K-means (k = 2 to 10)
24. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper() • Spot QC ◦ Drop high mitochondria % spots? ◦ Drop spots with low UMI counts?
25. sc/snRNA-seq QC metrics such as # detected genes, # UMI, mitochondria expression % are likely biologically related!
26. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper() • Spot QC ◦ Drop high mitochondria % spots? ◦ Drop spots with low UMI counts? ▪ Biological vs technical reasons
27. isOutlier(sce.416b$sum, log=TRUE, type="lower") drive.google.com/drive/folders/1cn5d-Ey7-kkMiex8-74qxvxtCQT6o72h Slide by Peter Hickey
28. doi.org/10.1089/genbio.2023.0019 Fig S9 Low library size (UMIs, # detected genes): • Biological: ◦ Not all cells / layers are equally active • Technical: ◦ Edge of tissue: should have been called “out of tissue” ◦ Truly low quality
29. Prashanthi Ravichandran @prashanthi-ravichandran (GH) Artifacts in general are normalized away by library size, though there are caveats doi.org/10.1126/science.adh1938
30. Diffusion issues: could be related to permeabilization step doi.org/10.1126/science.adh1938
31. Having more data is useful to provide context! Here 4 new samples have low sequencing saturation (outliers) but are within range of good samples from other studies
32. Having more data is useful to provide context! Those 4 samples have great median UMI counts per spot ^_^
33. Software keeps evolving and as leaders in the field we aim to use the best methods 33 Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19, 534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2
34. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper() • Spot QC ◦ Drop high mitochondria % spots? ◦ Drop spots with low UMI counts? ▪ Biological vs technical reasons • Spot clustering (spatially-aware) and/or manual annotation
35. Visualize known marker genes
36. Visualize known marker genes: manually annotate
37. 37 bioconductor.org/packages/spatialLIBD Visualizing multiple genes
38. 38 bioconductor.org/packages/spatialLIBD Spatially-aware clustering results BayesSpace PRECAST …
39. Some analysis differences • 10x Genomics CellRanger → SpaceRanger (or other tools) • DropletUtils::read10xVisium() → spatialLIBD::read10xVisiumWrapper() • Spot QC ◦ Drop high mitochondria % spots? ◦ Drop spots with low UMI counts? ▪ Biological vs technical reasons • Spot clustering (spatially-aware) and/or manual annotation • Choosing a spot clustering resolution ◦ Can be guided by spatially cluster registration
40. 40 bioconductor.org/packages/spatialLIBD doi.org/10.1126/science.adh1938
41. spatialLIBD::registration_wrapper() + layer_stat_cor() 41 doi.org/10.1126/science.adh1938
42. spatialLIBD apps • Useful for visualizing & annotating the data • Immediately make them after running SpaceRanger • Update them as we generate more results • Share them with the publication of the results • spatial.libd.org/spatialLIBD/ • research.libd.org/spatialDLPFC/#interactive-websites • research.libd.org/Visium_SPG_AD/#interactive-websites • …
43. 43 research.libd.org/rstatsclub LIBD rstats club videos
44. 44 research.libd.org/rstatsclub LIBD rstats club videos
45. lcolladotor.github.io/#projects • Every assay has caveats • We re-use tricks: think adding 0, multiplying by 1 • It nearly always takes a team • Data sharing accelerates science + democratizes access to it • Zooming in allows us to reduce the heterogeneity • We can learn from each other: from uniformly processing our data & re-using it → replicate / validate?
46. @MadhaviTippani Madhavi Tippani @HeenaDivecha Heena R Divecha @lmwebr Lukas M Weber @stephaniehicks Stephanie C Hicks @abspangler Abby Spangler @martinowk Keri Martinowich @CerceoPage Stephanie C Page @kr_maynard Kristen R Maynard @lcolladotor Leonardo Collado-Torres @Nick-Eagles (GH) Nicholas J Eagles Kelsey D Montgomery Sang Ho Kwon Image Analysis Expression Analysis Data Generation Thomas M Hyde @lahuuki Louise A Huuki-Myers @BoyiGuo Boyi Guo @mattntran Matthew N Tran @sowmyapartybun Sowmya Parthiban Slides available at speakerdeck.com /lcolladotor + Many more LIBD, JHU, and external collaborators @mgrantpeters Melissa Grant-Peters @prashanthi-ravichandran (GH) Prashanthi Ravichandran
47. lcolladotor.github.io @lcolladotor