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Leonardo Collado-Torres

October 23, 2023

More Decks by Leonardo Collado-Torres

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  1. @lcolladotor lcolladotor.github.io lcolladotor.github.io/bioc_team_ds Metodologías para análisis de RNA-seq aplicadas al

    cerebro 🧠 Leonardo Collado Torres Investigator, LIEBER INSTITUTE for BRAIN DEVELOPMENT Asst. Prof. Dept. of Biostatistics, Johns Hopkins Bloomberg School of Public Health #PROINNOVA 󰐏 Octubre 23 2023 Slides available at speakerdeck.com/lcolladotor
  2. • 2017: ◦ idea en BioC2017 e inicio de la

    fundación de CDSB • 2018: ◦ primer taller ^_^, con instructores de Bioconductor: Martin Morgan & Benilton Carvalho • 2019: ◦ BioC2019: apoyo a solicitud de becas ◦ Taller con materiales adaptados de RStudio • 2020: ◦ regutools: primer paquete en Bioconductor ◦ Taller con RStudio & Bioconductor • 2021: ◦ primera vez con 2 talleres https://comunidadbioinfo.github.io/
  3. Zoom in: base pair resolution Jeff Leek @jtleek Ph.D. advisor

    Andrew E Jaffe @andrewejaffe Ph.D. co-advisor
  4. Fetal Infant Child Teen Adult 50+ 6 / group, N

    = 36 Discovery data Postmortem Human Brain Samples Fetal Infant Child Teen Adult 50+ 6 / group, N = 36 Replication data Andrew E Jaffe @andrewejaffe Ph.D. co-advisor Developmental regulation of human cortex transcription and its clinical relevance at single base resolution doi.org/10.1038/nn.3898 github.com/leekgroup/libd_n36
  5. doi.org/10.1038/nn.3898 Developmental regulation of human cortex transcription and its clinical

    relevance at single base resolution github.com/leekgroup/libd_n36
  6. Zoom in: more data! Ben Langmead @BenLangmead Abhinav Nellore @nellore

    (GitHub) Christopher Wilks @chrisnwilks Shannon Ellis @Shannon_E_Ellis Kasper Daniel Hansen @KasperDHansen Andrew E Jaffe @andrewejaffe Ph.D. co-advisor + LIBD former boss Jeff Leek @jtleek Ph.D. advisor
  7. expression data for ~70,000 human samples samples phenotypes ? GTEx

    N=9,962 TCGA N=11,284 SRA N=49,848 samples expression estimates gene exon junctions ERs Answer meaningful questions about human biology and expression slide adapted from Shannon Ellis Reproducible RNA-seq analysis using #recount2 + Improving the value of public RNA-seq expression data by phenotype prediction doi.org/10.1038/nbt.3838 doi.org/10.1093/nar/gky102
  8. recount3: over 700,000 human and mouse RNA-seq samples #recount3: summaries

    and queries for large-scale RNA-seq expression and splicing Christopher Wilks @chrisnwilks research.libd.org/recount3-docs/ doi.org/10.1186/s13059-021-02533-6
  9. Zoom in: snRNA-seq → deconvolution of bulk RNA-seq Matthew N

    Tran @mattntran Kristen R Maynard @kr_maynard Louise A Huuki-Myers @lahuuki Keri Martinowich @martinowk Stephanie C Hicks @stephaniehicks
  10. What is Deconvolution? • Inferring the composition of different cell

    types in a bulk RNA-seq data Louise A Huuki-Myers @lahuuki
  11. #deconvochallenge Challenges and opportunities to computationally deconvolve heterogeneous tissue with

    varying cell sizes using single cell RNA-sequencing datasets doi.org/10.48550/arXiv.2305.06501 Sean Maden @MadenSean
  12. Zoom in: spatial omics Kristen R Maynard @kr_maynard Keri Martinowich

    @martinowk Stephanie C Hicks @stephaniehicks Andrew E Jaffe @andrewejaffe Stephanie C Page @CerceoPage
  13. 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 23
  14. 2 pairs spatial adjacent replicates x subject = 12 sections

    24 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0μm) Adjacent spatial replicates (300μm) PCP4 Maynard, Collado-Torres, et al, Nat Neuro, 2021
  15. 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
  16. 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
  17. BayesSpace clustering with batch correction worked best for multiple samples

    31 doi.org/10.1101/2023.02.15.528722 twitter.com/CrowellHL/status/1597579271945715717
  18. Spatial Registration Adds Anatomical Context • Validate detection of laminar

    structure • Correlate enrichment t-statistics for top marker genes of reference ◦ Cluster vs. manual annotation • Annotate with strongly associated histological layer 32 Sp k D d ~L doi.org/10.1101/2023.02.15.528722
  19. Spatial Registration of Spatial Domains • Map SpDs to Maynard

    et al. manual annotated layers • Highlight most strongly associated histological layer to add biological context 33 doi.org/10.1101/2023.02.15.528722
  20. Identify Layer Associated Neuron Populations 34 • Apply Spatial Registration

    with manual layers • 13 layer-level cell types ◦ Assign Excitatory Neurons histological layers ◦ Pool other cell type groups Kelsey D Montgomery
  21. Spot Deconvolution 35 Cell 1 Cell 2 … Cell N

    Gene 1 0 0 … 0 Gene 2 2 5 … 3 … … … … … Gene i 1 0 … 0 Spot 1 Spot 2 … Spot M Gene 1 1 0 … 3 Gene 2 0 1 … 0 … … … … … Gene j 4 2 … 2 Astro Excit … Inhib Spot 1 1 1 … 1 Spot 2 … … … … … … Spot N 1 0 … 2 Single- Nucleus Spatial Deconvolved Results Spot 1 Nicholas J Eagles @Nick-Eagles (GitHub)
  22. Existing Spot Deconvolution Software - Explored 3 novel software methods

    from the literature Software name Overall approach Input Cell Counts Output Tangram (Biancalani et al.) Mapping individual cells Every spot Integer counts Cell2location (Kleshchevnikov et al.) Matching gene-expression profile Average across spots Decimal counts SPOTlight (Elosua-Bayes et al.) Matching gene-expression profile Not used Proportions 36 Excit L5 Counts
  23. Visium Spatial Proteogenomics (Visium-SPG) Visium-SPG = Visium SRT + immunofluorescence

    (using identical tissue samples) Sang Ho Kwon @sanghokwon17
  24. Visium Spatial Proteogenomics (Visium-SPG) - Gene expression captured like ordinary

    Visium - Multi-channel fluorescent images captured of the same tissue - Channels measure proteins marking for specific cell types Kristen R. Maynard 39 Sang Ho Kwon Visium-SPG = Visium SRT + immunofluorescence (using identical tissue samples) Fluorescent Protein Cell Type TMEM119 Microglia Neun Neurons OLIG2 Oligodendrocytes GFAP Astrocytes
  25. Benchmark Summary 42 Metric Tangram Cell2location SPOTlight Metric Type Avg.

    cor (spot-level) 0.31 0.30 0.21 Orthogonal measurements Avg. RMSE (spot-level) 1.35 1.24 1.3 Orthogonal measurements Overall prop.: (KL Div.) 0.44 0.49 0.41 Orthogonal measurements Overall prop.: (cor.) 0.46 0.37 0.47 Orthogonal measurements Overall prop.: (RMSE) 3020 3890 3040 Orthogonal measurements Histological mapping 0.69 0.77 0.23 Leverage known biology Broad vs. layer (cor.) 1.00 0.77 -0.36 Self-consistency of results Broad vs. layer (RMSE) 102 4200 4220 Self-consistency of results Nicholas J Eagles @Nick-Eagles (GitHub)
  26. How Spot Deconvolution Results Were Used A. Better characterize unsupervised

    spatial domains B. Cell-cell communication; cell-type-informed ligand-receptor interactions in the context of schizophrenia risk A 43 Boyi Guo Melissa Grant-Peters
  27. Visium spatial clustering works for variables with high % variance

    explained. But what about other ones? DOI: 10.1038/s41593-020-00787-0
  28. AD pathology signal is too small to detect by spatially-resolved

    gene expression alone research.libd.org/Visium_SPG_AD/
  29. 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?
  30. @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