single-cell transcriptomics overview
TRANSCRIPT
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Single-cell transcriptomics overview
Alejandro Reyes Huber groupCSAMA 2015
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Why?(optimistic)
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Research questions that can be answered with single-cell transcriptomics
1) Identification of cell-types from heterogeneous samples
Treutlein et al, 2014
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Research questions that can be answered with single-cell transcriptomics
2) Identification of cell-type specific markers (genes, isoforms)
Treutlein et al, 2014
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Research questions that can be answered with single-cell transcriptomics
3) Identifying highly varying genes across cells
Shalek et al, 2013
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Research questions that can be answered with single-cell transcriptomics
4) Study kinetics of transcription
Stegle et al, 2013
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Research questions that can be answered with single-cell transcriptomics
5) Allelic expression heterogeneity
Deng et al, 2014
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Research questions that can be answered with single-cell transcriptomics
5) Transcript isoform expression heterogeneity
Shalek et al, 2013
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Protocols and noise(pesimistic)
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Single-cell transcriptomics protocols overview
Kolodziejczyk et al, 2013
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Single-cell transcriptomics protocols overview
Kolodziejczyk et al, 2013
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Single cell protocols
Klein et al, 2015
Thousands of cells!
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Single-cell transcriptomics protocols overview
Kolodziejczyk et al, 2013
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Single-cell transcriptomics protocols overview
Kolodziejczyk et al, 2013
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Analysis
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Observed read counts are a combination of different factors
counts = cell state + cell cycle + cell size + apoptosis + …+ technical noise
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Small amounts of starting material impact on technical noise
Brennecke, Anders et al, 2013
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Detection problems
cells
Nor
mal
ized
num
ber o
f fra
gmen
ts
1050
500
5000
5000
0
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Accounting for technical noise using spike-in sequences
Method by Brennecke, Anders et al, 2013 Data from Brennecke, Reyes et al, 2015
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Accounting for technical noise by considering “dropout” events
Kharchenko et al, 2013
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Accounting for technical noise using unique molecular identifiers
Islam et al, 2014
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Accounting for “biological” confounders
Stegle et al, 2015
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scLVM is useful to regress out variation explained by latent variables
Buettner et al, 2014
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scLVM is useful to regress out variation explained by latent variables
Observed expression for gene g
Given H hidden factors,
Variance attributed to
hidden factors
Technical variance Residual
“biological” variance
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Buettner et al, 2015
scLVM is useful to regress out variation explained by latent variables
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Cluster stability analysis
Ohnishi, Huber, et al, 2013
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Dimensionality reduction
Multidimensional scaling* Isomap* t-SNE*
Diffusion maps
*Bioc Package: sincell
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Cell hierarchy reconstruction
Minimum Spanning Tree (MST)* Maximum Similarity Spanning Tree (SST)* Iterative Mutual Clustering Graph (IMC)*
Wanderlust
*Bioc Package: sincell
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Construction of cell state hierarchies
Minimum Spanning Tree Concept (wikipedia)
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Construction of cell state hierarchies
Bendall et al, 2014
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Wanderlust algorithm
Bendall et al, 2014
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Validate!
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K-medoids clustering suggested non-random gene expression patterns
Genes
Gen
es
Genes
Cel
ls
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Co-expression of genes was confirmed using independent analytical and experimental
validations
What are the genes co-expressed with the selected gene? (FDR 10%)
203 cellsselected cells FACS or qPCR
Are the co-expression patterns consistent?
gene mRNAs?
yesno
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Klk5
Co-expression of genes was confirmed using independent analytical and experimental
validations
~6%
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Thanks!