2023-07-18_Verge_Genomics

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
   Verge Genomics
   July 18 2023
   Slides available at speakerdeck.com/lcolladotor
   

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

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

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4. 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
   

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

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

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7. Reference Single Cell
   Data
   7
   deconvolution(Y, Z) = Proportion of Cell Types
   Louise A Huuki-Myers
   @lahuuki
   

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8. 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
   

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

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10. 1vAll Markers vs. Mean Ratio Markers
    10
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/
    

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11. 1vAll Markers vs. Mean Ratio Markers
    11
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/
    

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12. Methods
    12
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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13. 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
    13
    

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14. 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
    14
    

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15. 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)
    15
    Louise A
    Huuki-Myers
    @lahuuki
    

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16. Results + Validation
    16
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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

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

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19. ● 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
    

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

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21. #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
    

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

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23. 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
    

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24. 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
    

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25. 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
    

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26. 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
    

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

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28. #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
    

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

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

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31. 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
    31
    

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32. 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
    

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33. 2 pairs spatial adjacent replicates x subject = 12 sections
    33
    Subject 1
    Subject 2
    Subject 3
    Adjacent spatial replicates (0μm) Adjacent spatial replicates (300μm)
    PCP4
    Maynard, Collado-Torres, et al, Nat Neuro, 2021
    

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34. “Pseudo-bulking” collapses data: spot to layer level
    34
    Maynard, Collado-Torres, et al, Nat Neuro, 2021
    

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35. 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
    

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36. 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
    

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37. 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
    

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38. #spatialDLPFC
    38
    doi.org/10.1101/2023.02.15.528722
    Louise A
    Huuki-Myers
    @lahuuki
    Abby Spangler
    @abspangler
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    

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39. BayesSpace clustering with batch correction worked
    best for multiple samples
    39
    doi.org/10.1101/2023.02.15.528722
    twitter.com/CrowellHL/status/1597579271945715717
    

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40. 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
    40
    More Clusters = More Complexity
    doi.org/10.1101/2023.02.15.528722
    

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41. 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
    41
    Sp
    k
    D
    d
    ~L
    doi.org/10.1101/2023.02.15.528722
    

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42. Spatial Registration of
    Spatial Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    42
    doi.org/10.1101/2023.02.15.528722
    

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43. Identify Layer Associated Neuron Populations
    43
    ● Apply Spatial Registration with
    manual layers
    ● 13 layer-level cell types
    ○ Assign Excitatory Neurons
    histological layers
    ○ Pool other cell type groups
    Kelsey D Montgomery
    

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44. Layer-level Cell Type Marker genes
    44
    Louise A
    Huuki-Myers
    @lahuuki
    

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45. Spot Deconvolution
    45
    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)
    

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46. 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
    46
    Excit L5 Counts
    

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47. 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
    47
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    doi.org/10.1101/2023.02.15.528722
    

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48. Visium Spatial
    Proteogenomics
    (SPG) Images as an
    Orthogonal
    Measurement
    48
    

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

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50. 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
    50 Sang Ho Kwon
    Visium-SPG = Visium SRT + immunofluorescence
    (using identical tissue samples)
    Fluorescent Protein Cell Type
    TMEM119 Microglia
    Neun Neurons
    OLIG2 Oligodendrocytes
    GFAP Astrocytes
    

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51. 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
    51
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    doi.org/10.1101/2023.01.28.525943
    

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52. 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
    52
    Annie B. Nguyen
    

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53. Constructing Dataset of Labeled Cells
    53
    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
    

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54. 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
    54
    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
    600 old cells
    + 320 new cells
    = 920 total cells
    Cell Type Probability
    Astro 0.01
    Oligo 0.02
    Micro 0.01
    Neuron 0.93
    Other 0.03
    Less-confident neuron More-confident neuron
    Confidence = 0.45 Confidence = 0.93
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    

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55. Training Cell-Type Classifier
    55
    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
    

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56. Benchmark Results: Make Orthogonal Measurements
    56
    Neuron
    Layer Broad
    Decision
    Tree
    - Sum across finer cell types to
    compare against broader
    - Drop EndoMural for
    comparison to decision tree
    Software Predictions
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    

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57. Benchmark Results: Make Orthogonal Measurements
    57
    Micro
    (Br6522_Ant_IF)
    All points in A -> one point in B
    

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58. Benchmark Results: Make Orthogonal Measurements
    58
    Broad cell type level Layer level (Excit by layer)
    

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59. 59
    Max across layers
    Not max
    Benchmark Results:
    Leverage Prior Knowledge
    

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60. Benchmark Results: Leverage Prior Knowledge
    60
    

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61. 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!
    61
    

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62. Benchmark Summary
    62
    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
    

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63. 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.
    63
    

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64. Viewing Spot
    Deconvolution
    Results: spatialLIBD
    apps
    - View:
    - spot deconvolution results
    - spatial domains/ clusters
    - gene expression
    - Huge amount of
    aesthetic customization
    64
    https://libd.shinyapps.io/spatialDLPFC_Visium_SPG/
    

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65. 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
    65
    Boyi Guo Melissa Grant-Peters
    

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

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

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

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

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

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71. Some challenges ⚠ with
    Visium
    71
    

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

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

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74. Diffusion issues: could be related to
    permeabilization step
    

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75. 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
    

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

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77. Software keeps evolving and as leaders in the field we aim to
    use the best methods
    77
    Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19,
    534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2
    

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78. 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)
    

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79. 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)
    

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80. Documentation + wrapper functions + tests (GitHub Actions +
    Bioconductor)
    80
    bioconductor.org/packages/spatialLIBD
    

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81. 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
    

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82. 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?
    

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83. 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!)
    

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84. 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
    

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85. 20 chapters and counting!
    lcolladotor.github.io/bioc_team_ds
    

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86. View Slide

87. lcolladotor.github.io/pkgs
    lcolladotor.github.io/biocthis
    

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88. @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
    

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89. lcolladotor.github.io
    @lcolladotor
    

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