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Towards Whole- Transcriptome Deconvolution with Single-cell Data

Towards Whole- Transcriptome Deconvolution with Single-cell Data. James Lindsay 1 Ion mandoiu 1 Craig Nelson 2. University Of Connecticut 1 Department of Computer Science and Engineering 2 Department of Molecular and Cell Biology. Mouse Embryo. ANTERIOR / HEAD. Neural tube. Somites.

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Towards Whole- Transcriptome Deconvolution with Single-cell Data

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  1. Towards Whole-TranscriptomeDeconvolutionwith Single-cell Data James Lindsay1 Ion mandoiu1 Craig Nelson2 University Of Connecticut 1Department of Computer Science and Engineering 2Department of Molecular and Cell Biology

  2. Mouse Embryo ANTERIOR / HEAD Neural tube Somites Node Primitive streak POSTERIOR / TAIL

  3. Unknown Mesoderm Progenitor What is the expression profile of the progenitor cell type? NSB=node-streak border; PSM=presomitic mesoderm; S=somite; NT=neural tube/neurectoderm; EN=endoderm

  4. Characterizing Cell-types • Goal: Whole transcriptome expression profiles of individual cell-types • Technically challenging to measure whole transcriptome expression from single-cells • Approach: Computational Deconvolution of cell mixtures • Assisted by single-cell qPCR expression data for a small number of genes

  5. Modeling Cell Mixtures Mixtures (X) are a linear combination of signature matrix (S) and concentration matrix (C) cell types mixtures mixtures cell types genes genes

  6. Previous Work • Coupled Deconvolution • Given: X, Infer: S, C • NMF Repsilber, BMC Bioinformatics, 2010 • Minimum polytope Schwartz, BMC Bioinformatics, 2010 • Estimation of Mixing Proportions • Given: X, S Infer: C • Quadratic Prog Gong, PLoS One, 2012 • LDA Qiao, PLoS Comp Bio, 2o12 • Estimation of Expression Signatures • Given: X, C Infer: S • csSAMShen-Orr, Nature Brief Com, 2010

  7. Single-cell Assisted Deconvolution Given: X and single-cells qPCR data Infer: S, C Approach: • Identify cell-types and estimate reduced signature matrix using single-cells qPCR data • Outlier removal • K-means clustering followed by averaging • Estimate mixing proportions C using • Quadratic programming, 1 mixture at a time • Estimate full expression signature matrix S using C • Quadratic programming , 1 gene at a time

  8. Step 1: Outlier Removal + Clustering Remove cells that have maximum Pearson correlation to other cells below .95 unfiltered filtered

  9. Step 1: PCA of Clustering

  10. Step 2: Estimate Mixture Proportions For a given mixture i: Reduced signature matrix.Centroid of k-means clusters

  11. Step 3: Estimating Full Expression Signatures cell types mixtures mixtures cell types genes genes C: known from step 2 x: observed signals from new gene s: new gene to estimate signatures Now solve:

  12. Experimental Design • Single Cell Profiles • 92 profiles • 31 genes • Simulated Concentrations • Sample uniformly at random [0,1] • Scale column sum to 1. • Simulated Mixtures • Choose single-cells randomly with replacement from each cluster • Sum to generate mixture

  13. Data: RT-qPCR • CT values are the cycle in which gene was detected • Relative Normalization to house-keeping genes • HouseKeeping genes • gapdh, bactin1 • geometric mean • Vandesompele, 2002 • dCT(x) = geometric mean – CT(x) • expression(x) = 2^dCT(x)

  14. Accuracy of Inferred Mixing Proportions

  15. Concentration Matrix: Concordance

  16. Concentration by # Genes: Random

  17. Concentration by # Genes: Ranked

  18. Leave-one-out: Concentration: 50 mix 2^dCT RMSE Missing Gene

  19. Leave-one-out: Signature: 10 mix 2^dCT RMSE Missing Gene

  20. Leave-one-out: Signature: 50 mix 2^dCT RMSE Missing Gene

  21. Future Work • Bootstrapping to report a confidence interval of each estimated concentration and signature • Show correlation between large CI and poor accuracy • Mixing of heterogeneous technologies • qPCR for single-cells, RNA-seq for mixtures • Normalization (need to be linear) • Whole-genome scale • # genes to estimate 10,000+ signatures • Data!

  22. Conclusion Special Thanks to: • Ion Mandoiu • Craig Nelson • Caroline Jakuba • Mathew Gajdosik James.Lindsay@engr.uconn.edu

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