1 Caroline Jakuba 2 Ion mandoiu 1 Craig Nelson 2 Gene Expression Deconvolution with Singlecell Data University Of Connecticut 1 Department of Computer Science and Engineering 2 Department of Molecular and Cell Biology ID: 257750
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James Lindsay1Caroline Jakuba2Ion mandoiu1Craig Nelson2
Gene Expression Deconvolution with Single-cell Data
University Of Connecticut
1
Department of Computer Science and Engineering
2
Department of Molecular and Cell BiologySlide2
Mouse Embryo
Somites
POSTERIOR / TAIL
ANTERIOR / HEAD
Node
Neural tube
Primitive streakSlide3
Unknown Mesoderm ProgenitorWhat is the expression profile of the progenitor cell type?NSB=node-streak border; PSM=presomitic mesoderm; S=somite; NT=neural tube/
neurectoderm; EN=endodermSlide4
Characterizing Cell-typesGoal: Whole transcriptome expression profiles of individual cell-typesTechnically challenging to measure whole transcriptome expression from single-cellsApproach: Computational Deconvolution of cell mixturesAssisted by single-cell qPCR expression data for a small number of genes Slide5
Modeling Cell MixturesMixtures (X) are a linear
combination of signature matrix (S) and concentration matrix (C)
mixtures
genes
cell types
genes
mixtures
cell typesSlide6
Previous WorkCoupled DeconvolutionGiven: X, Infer: S, C NMF Repsilber, BMC Bioinformatics, 2010Minimum polytope Schwartz, BMC Bioinformatics, 2010Estimation of Mixing ProportionsGiven: X, S Infer: CQuadratic Prog Gong, PLoS One, 2012LDA Qiao, PLoS Comp Bio, 2o12
Estimation of Expression SignaturesGiven: X, C Infer: ScsSAM Shen-Orr, Nature Brief Com, 2010Slide7
Single-cell Assisted DeconvolutionGiven: X and single-cells qPCR data Infer: S, C Approach:Identify cell-types and estimate reduced signature matrix using single-cells qPCR dataOutlier removal K-means clustering followed by averagingEstimate mixing proportions C using Quadratic programming, 1 mixture at a timeEstimate full expression signature matrix S using CQuadratic programming , 1 gene at a time
Slide8
Step 1: Outlier Removal + ClusteringunfilteredfilteredRemove cells that have maximum P
earson correlation to other cells below .95Slide9
Step 2: Estimate Mixture Proportions
For a given mixture i:Slide10
Step 3: Estimating Full Expression Signaturess: new gene to estimate signatures
mixtures
genes
cell types
genes
mixtures
cell types
Now solve:
C: known from step 2
x: observed signals from new geneSlide11
Experimental DesignSimulated ConcentrationsSample uniformly at random [0,1]Scale column sum to 1.
Simulated MixturesChoose single-cells randomly with replacement from each cluster
Sum to generate mixture
Single Cell Profiles92 profiles31 genes
Actual Mixtures
12 mixtures
31 genes
Dimensions
k = 3
m = 31
n = 92, 12
# mixtures = {10…300}Slide12
Data ProcessingRT-qPCR CT values are the cycle in which gene was detectedRelative Normalization to house-keeping genesHouseKeeping genes gapdh, bactin1geometric meanVandesompele, 2002dCT(x) = geometric mean – CT(x)expression(x) = 2^dCT(x)Slide13
Accuracy of Inferred Mixing ProportionsSlide14
Concentration Matrix: ConcordancepredictedSlide15
Leave-one-out Accuracy of Inferred Gene Expression SignaturesSlide16
Future WorkApply gene signature estimation technique using more genes in mixed samplesIdentify PSM-Pr SignatureConfirm the anatomical location of the putative PSM-Pr cell population through exhaustive ISHSlide17
ConclusionSpecial Thanks to:Ion MandoiuCraig NelsonCaroline JakubaMathew GajdosikJames.Lindsay@engr.uconn.edu