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.

Transcript

1. Correcting for Cell Type RNA Fractions in MDD RNA-seq Data Louise Huuki Research Associate @lahuuki
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%
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
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
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
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
7. Mean Ratio vs. Fold Change • Top mean ratio genes also have high fold changes • This metric helps remove “noisy” genes
8. Original Markers vs. New Markers: Much Cleaner
9. Apply to Specific Cell Types
10. Broad Cell Types vs. Specific: Noisier but Usable
11. MuSiC results MuSiC::music_prop
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
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
15. Expression Plots for sACC - specific cell types
16. Expression Plots for sACC - specific cell types
17. Expression Plots for sACC - specific cell types
18. Expression Plots for sACC - specific cell types
19. Expression Plots for sACC - specific cell types