Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Crick-2023

 Crick-2023

"Lessons from working on the edge of human brain transcriptomics with spatially-resolved transcriptomics and deconvolution" seminar on 2023-05-23 at The Francis Crick Institute

Tweet

More Decks by Leonardo Collado-Torres

Other Decks in Science

Transcript

  1. @lcolladotor
    lcolladotor.github.io
    lcolladotor.github.io/bioc_team_ds
    Lessons from working on the edge
    of human brain transcriptomics
    with spatially-resolved
    transcriptomics and deconvolution
    Leonardo Collado Torres, Investigator
    The Francis Crick Institute
    May 23 2023
    Slides available at speakerdeck.com/lcolladotor

    View full-size slide

  2. doi.org/10.1016/j.biopsych.2020.06.005
    Michael Gandal
    @mikejg84
    Transcriptomic
    Insight Into the
    Polygenic
    Mechanisms
    Underlying
    Psychiatric
    Disorders

    View full-size slide

  3. Background: Human DLPFC
    3
    slideshare.net
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  4. doi.org/10.1186/s12859-021-04142-3
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    Upcoming LIBD Data portal built on top of SPEAQeasy & PopTop & BiocMAP

    View full-size slide

  5. https://twitter.com/lcolladotor/status/1506998412809412612
    Keri Martinowich
    @martinowk

    View full-size slide

  6. 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

    View full-size slide

  7. What is Deconvolution?
    ● Inferring the composition of
    different cell types in a bulk
    RNA-seq data
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  8. Interaction eQTLs with cell type proportions
    github.com/LieberInstitute/goesHyde_mdd_rnaseq/tree/master/eqtl/code

    View full-size slide

  9. Reference Single Cell
    Data
    9
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  10. 10x snRNA-seq Reference Data
    Tran, Maynard et al., Neuron, 2021
    AMY DLPFC HPC NAc sACC
    Astro 1638 782 1170 1099 907
    Endo 31 0 0 0 0
    Macro 0 10 0 22 0
    Micro 1168 388 1126 492 784
    Mural 39 18 43 0 0
    Oligo 6080 5455 5912 6134 4584
    OPC 1459 572 838 669 911
    Tcell 31 9 26 0 0
    Excit 443 2388 623 0 4163
    Inhib 3117 1580 366 11476 3974
    @mattntran
    Matthew N Tran

    View full-size slide

  11. Marker Finding
    11
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  12. 1vAll Markers vs. Mean Ratio Markers
    12
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/

    View full-size slide

  13. 1vAll Markers vs. Mean Ratio Markers
    13
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/

    View full-size slide

  14. Methods
    14
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  15. Method Summary
    Method Regression
    Correction for
    Technical
    Variation
    Other Features
    MuSiC
    Wang et al, Nature
    Communications, 2019
    W-NNLS regression
    (Weighted -
    Non-negative least
    squares)
    None
    Tree guided deconvolution,
    good for closely related cell
    types
    Bisque
    Jew et al, Nature
    Communications, 2020
    NNLS regresion
    Gene specific
    transformation of
    bulk data
    Leverage overlapping bulk &
    sc data
    SCDC
    Dong et al, Briefings in
    Bioinformatics, 2020
    W-NNLS framework
    proposed by MuSiC
    Option for Gene
    specific
    transformation of
    bulk data (from
    Bisque)
    Multiple reference datasets
    can be used, results
    combined with ENSEMBL
    weights
    DWLS
    Tsoucas, Nature
    Communications, 2019
    Dampened Weighted
    least squares
    None
    15

    View full-size slide

  16. Method
    Regression
    Method
    Run
    Time
    Marker
    Evaluation
    Adjust for
    snRNA-seq
    vs. Bulk
    Tissues
    Tested
    Consider Cell
    Size
    Reference
    Set
    MuSiC W-NNLS Min.
    Internal
    Weighting
    No
    Pancreatic
    Islet, Rat &
    Mouse Kidney
    Yes
    Bisque NNLS Min. No Yes
    Adipose,
    DLPFC
    Recommend
    3+ donors
    SCDC
    W-NNLS
    Min.
    Internal
    Weighting
    Yes
    Pancreatic
    Islet, mouse
    mammary
    Can input
    multiple
    references
    DWLS DWLS Hours
    Internal
    Selection
    No
    Mouse kidney,
    lung, liver,
    small intestine
    16

    View full-size slide

  17. Which Method is the Most Accurate?
    ● Benchmarking shows that different methods perform best on
    different data sets (Cobos et al, Nature Communications, 2020)
    ● Benchmarking results from different papers on “real” data
    ○ MuSiC paper: MuSiC > NNLS > BSEQ-sx > CIBERSORT
    ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 2a)
    ○ Bisque paper: Bisque > MuSiC > CIBERSORT
    ■ DLPFC: Microglia vs. Braak stage, Neuron vs. Cognitive diagnostic category
    (Fig 4)
    ○ SCDC paper: SCDC > MuSiC > Bisque > DWLS > CIBERSORT
    ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 4b)
    ○ Cobos benchmark: DWLS > MuSiC > Bisque > deconvoSeq
    ■ Human PMBC flow sorted (Fig 7)
    17
    Louise A
    Huuki-Myers
    @lahuuki

    View full-size slide

  18. Results + Validation
    18
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  19. Mean Proportions By Region: Tran et al, bioRxiv, 2020 (5 donors, 6 cell types)
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  20. Peric =
    Mural + Endo
    Mean Proportions By Region: Tran et al, Neuron, 2021 (8 donors, 10 cell types)
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  21. ● Run with set of 20
    & 25 marker genes
    per cell type
    ● Bisque is more
    robust to changes
    in the marker set
    than MuSiC
    Method Sensitivity to Marker Set
    25 vs. 20 Genes
    Louise A Huuki-Myers
    @lahuuki

    View full-size slide

  22. Sean Maden
    @MadenSean
    Sang Ho Kwon
    @sanghokwon17
    #deconvochallenge doi.org/10.48550/arXiv.2305.06501

    View full-size slide

  23. #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

    View full-size slide

  24. Sean Maden
    @MadenSean
    Sang Ho Kwon
    @sanghokwon17
    #deconvochallenge
    doi.org/10.48550/arXiv.2305.06501

    View full-size slide

  25. Motivation
    ● Improve Deconvolution algorithms by considering differences in size and RNA
    content between cell types
    ● Use smFISH with RNAscope to establish data set of:
    ○ Cellular composition
    ○ Nuclei sizes of major cell types
    ○ Average nuclei RNA content of major cell types
    How do we measure total RNA content of a cell if we can only observe a few
    genes at a time? Use a TREG
    Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA
    Abundance in Heterogeneous Cell Types
    research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923
    Louise A Huuki-Myers
    @lahuuki
    #TREG

    View full-size slide

  26. What is a TREG?
    ● Total RNA Expression Gene
    ● Expression is proportional to the
    overall RNA expression in a nucleus
    ● In smFISH the count of TREG
    puncta in a nucleus can estimate
    the RNA content
    Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA
    Abundance in Heterogeneous Cell Types
    research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923
    #TREG

    View full-size slide

  27. Validate with RNAscope
    ● DLPFC from control, sectioned at 10μm
    ● 3 slides with 3 sections each
    ○ TREG candidate + cell type marker genes
    ● Images analyzed with HALO
    TREG Gene AKT3 ARID1B
    MALAT1/
    POLR2A
    Cell Type
    Markers
    GAD1, SLC17A7,
    MBP
    GAD1, SLC17A7,
    MBP
    SLC17A7, MBP
    Cell Type Marker
    Excit SLC17A7
    Inhib GAD1
    Oligo MBP
    Kelsey D Montgomery Sang Ho Kwon
    research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923

    View full-size slide

  28. Patterns of Observed Puncta
    ● TREGs were expressed in most
    cells
    ● AKT3 tracks really well with
    pattern of expression seen in
    snRNA-seq (ARID1B is also
    pretty good)
    snRNA-seq RNAscope
    Gene
    Mean Prop.
    Cells with
    Expression
    Prop. non-zero
    in DLPFC snRNA
    Standardized β
    (95% CI)
    AKT3 0.948 0.92 -1.38 (-1.39,-1.37)
    ARID1B 0.908 0.94 -0.62 (-0.62,-0.61)
    MALAT1 0.910 1.00 -0.11 (-0.12,-0.11)
    POLR2A 0.853 0.30 -0.98 (-0.99,-0.98)
    snRNA-seq NA NA -1.33 (-1.35,-1.31)
    Remember: MALAT1’s puncta data is unreliable
    research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923

    View full-size slide

  29. research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923
    Louise A
    Huuki-Myers
    @lahuuki

    View full-size slide

  30. #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

    View full-size slide

  31. Sean Maden
    @MadenSean
    #deconvochallenge
    doi.org/10.48550/arXiv.2305.06501

    View full-size slide

  32. Zoom in: spatial omics
    Kristen R Maynard
    @kr_maynard
    Keri Martinowich
    @martinowk
    Stephanie C Hicks
    @stephaniehicks
    Andrew E Jaffe
    @andrewejaffe
    Stephanie C Page
    @CerceoPage

    View full-size slide

  33. 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
    33

    View full-size slide

  34. 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

    View full-size slide

  35. 2 pairs spatial adjacent replicates x subject = 12 sections
    35
    Subject 1
    Subject 2
    Subject 3
    Adjacent spatial replicates (0μm) Adjacent spatial replicates (300μm)
    PCP4
    Maynard, Collado-Torres, et al, Nat Neuro, 2021

    View full-size slide

  36. “Pseudo-bulking” collapses data: spot to layer level
    36
    Maynard, Collado-Torres, et al, Nat Neuro, 2021

    View full-size slide

  37. 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

    View full-size slide

  38. 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

    View full-size slide

  39. 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

    View full-size slide

  40. #spatialDLPFC
    40
    doi.org/10.1101/2023.02.15.528722
    Louise A
    Huuki-Myers
    @lahuuki
    Abby Spangler
    @abspangler
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)

    View full-size slide

  41. BayesSpace clustering with batch correction worked
    best for multiple samples
    41
    doi.org/10.1101/2023.02.15.528722
    twitter.com/CrowellHL/status/1597579271945715717

    View full-size slide

  42. Different Resolutions of
    BayesSpace Clustering
    k = number of clusters
    ● k=2: separate white vs. grey matter
    ● k=9: best reiterated histological layers
    ● k=16: data-driven optimal k based on
    fast H+ statistic
    42
    More Clusters = More Complexity
    doi.org/10.1101/2023.02.15.528722

    View full-size slide

  43. 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
    43
    Sp
    k
    D
    d
    ~L
    doi.org/10.1101/2023.02.15.528722

    View full-size slide

  44. Spatial Registration of
    Spatial Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    44
    doi.org/10.1101/2023.02.15.528722

    View full-size slide

  45. Identify Layer Associated Neuron Populations
    45
    ● Apply Spatial Registration with
    manual layers
    ● 13 layer-level cell types
    ○ Assign Excitatory Neurons
    histological layers
    ○ Pool other cell type groups
    Kelsey D Montgomery

    View full-size slide

  46. Layer-level Cell Type Marker genes
    46
    Louise A
    Huuki-Myers
    @lahuuki

    View full-size slide

  47. Spot Deconvolution
    47
    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)

    View full-size slide

  48. 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
    48
    Excit L5 Counts

    View full-size slide

  49. Benchmarking Spot Deconvolution Software: Theory
    - How do we measure performance or accuracy of cell-type
    predictions?
    - Make orthogonal measurements*: image-derived counts
    - Leverage prior knowledge: neurons localize to gray matter?
    - Self-consistency of results: broad vs. fine cell-type results
    49
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    doi.org/10.1101/2023.02.15.528722

    View full-size slide

  50. Visium Spatial
    Proteogenomics
    (SPG) Images as an
    Orthogonal
    Measurement
    50

    View full-size slide

  51. Visium Spatial Proteogenomics (Visium-SPG)
    Visium-SPG = Visium SRT + immunofluorescence
    (using identical tissue samples)
    Sang Ho Kwon
    @sanghokwon17

    View full-size slide

  52. Visium-SPG for Paired Imaging and Gene Expression
    - Channels measure proteins marking for broad cell types: microglia, neurons,
    oligodendrocytes, and astrocytes
    Kristen R. Maynard
    doi.org/10.1101/2023.02.15.528722

    View full-size slide

  53. Using IF to Quantify Cell Types
    - Visium-SPG IF images mark for several
    proteins
    - Fluorescence in image channels
    correlates with counts of measured
    cell types
    Can measure 5 distinct cell types:
    53
    ● Astrocyte (GFAP)
    ● Neuron (NeuN)
    ● Oligodendrocyte
    (OLIG2)
    ● Microglia (TMEM119)
    ● Other (low signal in all
    channels) doi.org/10.1101/2023.01.28.525943
    Sriworarat, 2023. samuibrowser.com
    Sang Ho Kwon
    @sanghokwon17

    View full-size slide

  54. Segmenting Cells on Visium-SPG IF Images
    1. Segment cells on IF
    image
    2. Manually label N cells
    3. Train cell-type classifier
    and apply on remaining
    data
    Sriworarat, 2023. samuibrowser.com
    54
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    doi.org/10.1101/2023.01.28.525943

    View full-size slide

  55. Constructing Dataset of Labeled Cells
    1. Segment cells on IF
    image
    2. Manually label example
    cells
    3. Train cell-type classifier
    and apply on remaining
    data
    Image Channels
    Classified Cell Type
    Cell Mask
    55
    Annie B. Nguyen

    View full-size slide

  56. Constructing Dataset of Labeled Cells
    56
    1. Segment cells on IF image
    2. Manually label N cells
    3. Train cell-type classifier
    and apply on remaining
    data
    Annie B. Nguyen
    4 sections * 5 cell types * 30 cells = 600 manually labeled cells
    doi.org/10.1101/2023.01.28.525943
    Sriworarat, 2023. samuibrowser.com

    View full-size slide

  57. Addressing Bias in Cell Selection
    - Trained logistic regression model on 600-cell dataset
    - Broke cells into 4 quartiles based on model confidence
    - Labelled 320 more cells, evenly sampled from all 4 quartiles
    57
    Cell Type Probability
    Astro 0.2
    Oligo 0.3
    Micro 0.1
    Neuron 0.45
    Other 0.05
    4 quartiles * 4 sections * 5 cell types * 4 cells = 320 new cells total
    600 old cells + 320 new cells = 920 labeled cells
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

    View full-size slide

  58. Training Cell-Type Classifier
    58
    Model Test
    Precision
    Test Recall
    Decision tree 0.86 0.87
    Dataset # Training # Test Split
    Old 600 480 120 80/20
    New 320 240 80 75/25
    Combined 920 720 200 ~78/22
    1. Segment cells on IF image
    2. Manually label N cells
    3. Train cell-type classifier and apply on
    remaining data
    Grid search with 5-fold CV for each
    model to select hyperparameters
    Data
    Model
    Final model chosen

    View full-size slide

  59. Benchmark Results: Make Orthogonal Measurements
    59
    Micro
    (Br6522_Ant_IF)
    All points in A -> one point in B

    View full-size slide

  60. Layer-level: collapse Excit_L* into 1 for broad-level
    60
    Then combine Excitatory and Inhibitory into Neurons to
    compare vs CART-calculated counts from Visium_SPG

    View full-size slide

  61. Benchmark Results: Make Orthogonal Measurements
    61
    Broad cell type level Layer level (Excit by layer)

    View full-size slide

  62. Benchmark Results:
    Leverage Prior
    Knowledge
    - Manually annotate spots with
    histological layer
    - Explore how cell-type
    predictions map to annotated
    layers
    Kristen R. Maynard
    A B
    C

    View full-size slide

  63. Benchmark Results: Self-Consistency of Results
    Counts from software results using both broad and layer-level cell types were compared,
    by “collapsing” onto just 4 major cell types. We expect results perfectly on the diagonal!
    63

    View full-size slide

  64. Viewing Spot Deconvolution Results: Samui Browser
    - View:
    - Fluorescence
    channels
    - Spot deconvo results
    - Segmented cells
    - Gene expression
    - Interactive
    - Quickly zoom/scroll
    - Full-resolution
    images
    samuibrowser.com/from?url=data2.loopybrowser.com/VisiumIF/&s=Br2720_Ant_IF&s=Br6432_Ant_IF&s=Br6522_Ant_IF&s=Br8667_Post_IF
    Sriworarat, 2023.
    64

    View full-size slide

  65. Viewing Spot
    Deconvolution
    Results: spatialLIBD
    apps
    - View:
    - spot deconvolution results
    - spatial domains/ clusters
    - gene expression
    - Huge amount of
    aesthetic customization
    65
    https://libd.shinyapps.io/spatialDLPFC_Visium_SPG/

    View full-size slide

  66. 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
    66
    Boyi Guo Melissa Grant-Peters

    View full-size slide

  67. Visium spatial clustering works for variables with high %
    variance explained. But what about other ones?
    DOI: 10.1038/s41593-020-00787-0

    View full-size slide

  68. twitter.com/sanghokwon17/status/1650589385379962881 from 2023-04-24
    Sang Ho Kwon
    @sanghokwon17
    DOI: 10.1101/2023.04.20.537710
    #Visium_SPG_AD

    View full-size slide

  69. Experimental design & study overview
    Braak V-VI & CERAD frequent
    Sang Ho Kwon

    View full-size slide

  70. AD pathology signal is too small to detect by
    spatially-resolved gene expression alone research.libd.org/Visium_SPG_AD/

    View full-size slide

  71. Identifying transcriptional signatures of AD-related neuropathology
    Sang Ho Kwon

    View full-size slide

  72. Some challenges ⚠ with
    Visium
    72

    View full-size slide

  73. sc/snRNA-seq QC metrics such as # detected genes, # UMI,
    mitochondria expression % are likely biologically related!

    View full-size slide

  74. Prashanthi Ravichandran
    @prashanthi-ravichandran (GH)
    Artifacts in general are normalized away by library size,
    though there are caveats

    View full-size slide

  75. Diffusion issues: could be related to
    permeabilization step

    View full-size slide

  76. 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

    View full-size slide

  77. Having more data is useful to provide context!
    Those 4 samples have great median UMI counts per spot ^_^

    View full-size slide

  78. Software keeps evolving and as leaders in the field we aim to
    use the best methods
    78
    Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19,
    534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2

    View full-size slide

  79. The Development Process
    - Making a module
    - New, experimental software can change dramatically (function and
    syntax) between versions
    - Promotes collaboration by allowing two researchers to share exact
    code and instantly run software without special set-up
    SpatialExperiment release 3.14
    SpatialExperiment devel 3.15
    module load tangram/1.0.2
    module load cell2location/0.8a0
    module load spagcn/1.2.0
    https://github.com/LieberInstitute/jhpce_mod_source
    https://github.com/LieberInstitute/jhpce_module_config
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

    View full-size slide

  80. The Development Process
    - Regular interaction with software
    authors to clarify functionality and
    report bugs
    - Documentation for code and author
    responsiveness on GitHub can be
    critical in successfully applying
    software to our data
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

    View full-size slide

  81. Documentation + wrapper functions + tests (GitHub Actions +
    Bioconductor)
    81
    bioconductor.org/packages/spatialLIBD

    View full-size slide

  82. More challenges
    ahead
    Working with multiple
    capture areas per tissue
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    Prashanthi Ravichandran
    @prashanthi-ravichandran (GH)
    Spot
    diameter
    error:
    ~1.8 →
    ~1.1
    Another
    pair:
    ~2.8 →
    ~0.76

    View full-size slide

  83. 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?

    View full-size slide

  84. jhpce.jhu.edu/knowledge-base/knowledge-base-articles-from-lieber-institute/
    research.libd.org/rstatsclub/
    Join us Fridays at 9 AM (check the code of conduct
    please!)

    View full-size slide

  85. www.youtube.com/@lcolladotor/playlists
    Videos allow us to multiply
    ourselves
    We can make you custom
    selections of videos for a
    specific problem on DSgs
    sessions

    View full-size slide

  86. 20 chapters and counting!
    lcolladotor.github.io/bioc_team_ds

    View full-size slide

  87. Melissa
    Grant-Peters
    @mgrantpeters
    Endorsed by
    your UCL
    colleague:

    View full-size slide

  88. lcolladotor.github.io/pkgs
    lcolladotor.github.io/biocthis

    View full-size slide

  89. @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

    View full-size slide

  90. #GBD23 Thank you for having us over in the UK 󰏅!

    View full-size slide

  91. lcolladotor.github.io
    @lcolladotor

    View full-size slide