Extending Spatial Analysis with R/Bioconductor and beyond Spatial Biology US 2021 Leonardo Collado Torres @lcolladotor 2021-09-29 Slides: https://speakerdeck.com/lcolladotor

R/Bioconductor & beyond

LIEBER INSTITUTE for BRAIN DEVELOPMENT About Bioconductor Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. http://bioconductor.org/

LIEBER INSTITUTE for BRAIN DEVELOPMENT R/Bioconductor packages (BioC 3.12 from October 2020) ● Software: 1975 (2041 in 05/2021) ● Annotation: 971 ● Experimental Data: 398 ● Workflow guides: 28 http://bioconductor.org/packages/release/BiocViews.html#___Software

5 SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor Righelli, Weber, Crowell, et al, bioRxiv, 2021 DOI 10.1101/2021.01.27.428431 Dario Righellli Helena L Crowell @drighelli @CrowellHL Lukas M Weber @lmwebr

@lmwebr Lukas M Weber @lcolladotor Leonardo Collado-Torres @abspangler Abby Spangler @PardoBree Brenda Pardo ● Opportunity to lead ● SpatialExperiment: common infrastructure ● STexampleData: example datasets ● spatialLIBD: visualization functions & shiny app ● ggspavis: visualization ● Pre-prints for SpatialExperiment & spatialLIBD ● Some conversations with other developers to bring them into the Bioconductor world

bioconductor.org/packages/spatialLIBD Pardo et al, bioRxiv, 2021 DOI 10.1101/2021.04.29.440149 Maynard, Collado-Torres, Nat Neuro, 2021 Brenda Pardo Abby Spangler @PardoBree @abspangler

Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Our spatial data Hodge et al, Nature, 2019 Maynard, Collado-Torres, Nat Neuro, 2021

LIEBER INSTITUTE for BRAIN DEVELOPMENT http://bioconductor.org/packages/ExperimentHub/

10 Data-driven clustering: BayesSpace Zhao et al, Nature Biotechnology, 2021 https://doi.org/10.1038/s41587-021-00935-2

Visium Image Processing Pipeline : VistoSeg STEPS (MATLAB) 1. Import 2. Split 3. Segment 4. Count 5. Visualize @MadhaviTippani Madhavi Tippani

Preprint: VistoSeg: a MATLAB pipeline to process, analyze and visualize high resolution histology images for Visium spatial transcriptomics data https://doi.org/10.1101/2021.08.04.452489 Tutorial: http://research.libd.org/VistoSeg Pipeline: https://github.com/LieberInstitute/VistoSeg Work in progress: 1. Modify pipeline to import and process multichannel Immuno Fluorescence images. 2. Extract cell type and cell morphology information and use it for downstream transcriptomics analysis. Spot Barcode X coordinate Y coordinate Tissue Present - 1 Absent - 0 Cell count %Spot covered spot1 1 2 1 3 90% spot2 1 4 0 0 0%

OSTA: https://lmweber.org/OSTA-book/ @lmwebr Lukas M Weber @lcolladotor Leonardo Collado-Torres @abspangler Abby Spangler @HeenaDivecha Heena R Divecha @MadhaviTippani Madhavi Tippani @stephaniehicks Stephanie C Hicks

OSTA: https://lmweber.org/OSTA-book/ ● Similar to OSCA https://bioconductor.org/books/release/OSCA/ ○ Bioconductor-friendly (CRAN/BioC, not GitHub) or available via pip ○ Centered around SpatialExperiment and Visium by 10x Genomics ○ macOS / winOS / linux ● Main chapters for: ○ Pre-processing steps: outside R ○ Analysis steps ● Workflow chapters: ○ Illustrate the commands discussed earlier adapted for a few datasets ○ Not standalone Bioconductor workflows http://bioconductor.org/packages/release/BiocViews.html#___Workflow ○ One more detailed workflow on using spatialLIBD ● Paper / pre-print coming soon

Visium IF: image segmentation + spot ID data

Visium * ~5k spots in honeycomb * gene expression per spot * tissue (H&E staining) Immunofluorescence (IF) * multi-channel (6) images * identifies morphological features of interest * large: might be broken in tiles Channel 1 * triangle feature Channel 2 * cloud feature Channel 6 * xyz feature Tissue (bright field image) Visium spot Channel 1 feature Channel 2 feature + Visium IF raw data: 2 types

Spot ID Gene 1 Gene 2 Gene X spot0001 0 12 39 spot0002 4 0 27 Spot ID Gene 1 Gene 2 Gene X In Tissue # cells spot0001 0 12 39 true 3 spot0002 4 0 27 false 0 * spaceranger * Loupe Browser * VistoSeg on H&E bright field image Visium Analysis @MadhaviTippani @HeenaDivecha cell

Feature ID X center Y center type intensity feat0001 5 102 triangle 130.4 feat0002 10 30 cloud 99.1 Max (X, Y) Min (X, Y) Area ... IF Image Analysis * segment each channel * find features Challenges: * morphological features can be quite diverse * images are large * multiple tiles +

Feature ID X center Y center type intensity Spot ID(s) feat0001 5 102 triangle 130.4 spot0001 feat0002 10 30 cloud 99.1 spot0001 spot0002 Spot ID # Triangle # Cloud % triangle % cloud spot0001 0 12 0 17 spot0002 4 0 27 0 Merge Visium & IF Align & use Visium spot design info Add export by spot ID capability basic data: number of features advanced data: %, intensity, co-localization (features overlap), ...

Spot ID # Triangle # Cloud % triangle % cloud spot0001 0 12 0 17 spot0002 4 0 27 0 Merge Visium & IF IF Spot ID Gene 1 Gene 2 Gene X In Tissue # cells spot0001 0 12 39 true 3 spot0002 4 0 27 false 0 Visium downstream * QC * analyses @lmwebr @stephaniehicks @abspangler

@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 In collaboration with Thomas M Hyde

22 Interested in working with us? Let us know! https://www.stephaniehicks.com/join/ https://www.libd.org/careers/