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Correcting for Cell Type RNA Fractions in MDD RNA-seq Data

Correcting for Cell Type RNA Fractions in MDD RNA-seq Data

Progress finding cell type specific marker genes and estimating cell type promotions in MDD bulk RNAseq data, utilizing the deconvolution algorithm MuSiC (Wang et al, Nat. Comms., 2019), with complementary snRNA seq data (Tran et al, bioRxiv, 2020) on MDDseq data.

Presentation from EuroBioC2020.

Louise Huuki-Myers

December 15, 2020
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  1. Correcting for Cell Type RNA Fractions
    in MDD RNA-seq Data
    Louise Huuki
    Research Associate
    @lahuuki

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  2. Major Depressive Disorder (MDD)
    ● Wide range of symptoms, can include depressed mood, reduced energy
    and concentration, or suicidal thoughts
    ● Among largest cause of world wide disability
    ● Lifetime prevalence of 17%
    ● Heritability estimated to be 30-40%

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  3. Data
    Bulk RNA seq Data
    ● 1091 samples from 595 individuals and 2
    brain regions
    ● We want to explore differences in
    proportions of cell types between samples
    ○ Are there differences between Dx?
    ○ Control for differences in other analysis
    ● Data set be available on psychENCODE
    knowledge portal
    Single Nucleus RNA seq data
    ● Tran et al., bioRxiv, 2020 10.1101/2020.01.19.910976
    ● Identified 10 specific/6 broad cell types in
    sACC, 12 specific/6 broad in Amygdala
    Amygdala sACC
    MDD 231 228
    Control 187 200
    Bipolar 122 123
    Deconvolution Methodology
    ● Sosina et al., bioRxiv, 2020 10.1101/2020.10.07.329839
    ● Highest accuracy with MuSiC + snRNA seq
    reference data from same region + filtered
    markers

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  4. ● Marker genes evaluated by a 1vAll
    t-test with findMarkers
    ● Top 5 genes for each broad cell
    type selected, ranked by standard
    fold change
    ● Lots of noise between some cell
    types
    scran::findMarkers
    pheatmap::pheatmap
    Finding Marker Genes

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  5. ● Checked expression of
    markers by cell type
    ● Observed outliers in one or
    more non-target cell type
    causing noise
    scater::plotExpression
    Exploring Marker Expression

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  6. ● Checked expression of
    markers by cell type
    ● Observed outliers in one or
    more non-target cell type
    causing noise
    ● Developed metric “mean
    ratio” : ratio of mean target
    expression and highest
    mean non-target
    expression
    scater::plotExpression
    Exploring Marker Expression

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  7. Mean Ratio vs. Fold Change
    ● Top mean ratio genes
    also have high fold
    changes
    ● This metric helps
    remove “noisy” genes

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  8. Original Markers vs. New Markers: Much Cleaner

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  9. Apply to Specific Cell Types

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  10. Broad Cell Types vs. Specific: Noisier but Usable

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  11. MuSiC results MuSiC::music_prop

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  12. Whats next?
    ● Apply same methodology to Amygdala data
    ○ Proving to be more complex
    ● Check for biological reasoning on marker
    genes
    ● Assess marker finding method with
    external single nucleus data
    ● Control for differences in sample cell type
    proportions in MDDseq project

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  13. Acknowledgements
    LIBD
    ● Leonardo Collado Torres
    ● Keri Martinowich
    ● Kristen Maynard
    ● Andrew Jaffe
    ● Matt Tran
    Get in touch
    ● Any advice for deconvolution?
    ● Early bioinformatics career advice?
    Johns Hopkins University
    ● Fernando Goes
    ● Stephanie Hicks
    ● Patricia Braun
    Funding
    ● LIBD
    ● NIH/NIMH R01MH111721-04
    @lahuuki

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  14. Expression Plots for sACC - specific cell types

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  15. Expression Plots for sACC - specific cell types

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  16. Expression Plots for sACC - specific cell types

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  17. Expression Plots for sACC - specific cell types

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  18. Expression Plots for sACC - specific cell types

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