Computational Methods for Analysis of Single Cell RNA-Seq Data

Computational Methods for Analysis of Single Cell RNA-Seq Data
Ion Măndoiu Computer Science & Engineering Department University of Connecticut

Outline Intro to scRNA-Seq Typical analysis pipeline for scRNA-Seq
Motivation scRNA-Seq protocols Analysis challenges for single cell data Typical analysis pipeline for scRNA-Seq Conclusions

Why Single Cell RNA-Seq?
Macaulay and Voet, PLOS Genetics, 2014

Applications: Cell Differentiation

Applications: Tumor Heterogeneity

Applications: Cell Type Identification

Single Cell RNA-Seq Growth

Recent Technology Breaktroughs
DIY

Fluidigm Workflow Microfluidic chips (IFCs) with 96 and 800 capture chambers

Full-Transcript Sequencing

3’-end Sequencing (Fluidigm HT)

5’-end Sequencing w/ UMIs (STRT-C1)
Islam et al. 2013,

3’-end Sequencing w/ UMIs (10X)
Encapsulates up to 48,000 cells in 10 minutes

Challenges Low RT efficiency & sequencing depth
Results in “zero-inflated” data Hicks et al. 2015,

Challenges Low RT efficiency & sequencing depth PCR amplification bias
UMIs help Ziegenhain et al. 2017, Mol. Cel. 65(4), pp. 631–643.e4

Cell quality Live/dead Missing cells Multiple cells

Cell quality Stochastic effects Cells captured in different cell cycle phases Transcriptional bursting hard to distinguish from technical artifacts

Cell quality Stochastic effects Cell capture bias Capture rates may not representative of population frequencies

Cell quality Stochastic effects Cell capture bias Analysis tools lagging behind Protocols still evolving rapidly Standard methods do not scale well with #cells

Outline Intro to scRNA-Seq Typical analysis pipeline for scRNA-Seq
Primary analysis: reads QC, mapping, and quantification Secondary analysis: cells QC, normalization, clustering, and differential expression Tertiary analysis: functional annotation Conclusions

Differential expression
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Reads are typically processed to: Remove adapters Collapse identical sequences Filters and/or trim reads based on quality scores

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Sounds like a good idea! Improved N50 in genome assembly Decreased FPs in variant calling Higher overall read mapping rate Not all good for RNA-Seq quantification Fewer reads Shorter reads fewer unique alignments

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Trimming parameters can significantly change results! Williams et al. 2016,

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis RNA-Seq read mapping strategies: Ungapped mapping (with mismatches) to genome Cannot align reads spanning exon-junctions Local alignment (Smith-Waterman) to genome Very slow Spliced alignment to genome Computationally harder than ungapped alignment, but much faster than local alignment Mapping on transcript libraries Fastest, but cannot align reads from un-annotated transcripts Mapping on exon-exon junction libraries Cannot align reads overlapping un-annotated exons Hybrid approaches

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Comparison of spliced read mapping tools Kim et al.

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Cannot use raw read counts (why not?) Islam et al.

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis CPM = count per million Ignores multireads  underestimates expression of genes in large families Does not normalize for gene length  cannot compare CPMs b/w genes Comparing CPMs between samples assumes similar transcriptome size RPKM/FPKM = reads/fragments per kilobase per million Length for multi-isoform genes? Comparing FPKM between samples assumes similar (weighted) transcriptome size TPM: transcripts per million Still relative measure of expression, but comparable between samples Most accurate estimation methods use multireads and isoform level estimation UMI counts Absolute measure of expression?

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Gene ambiguous reads A B C D E Isoform ambiguous reads

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Expectation-maximization approach (IsoEM, RSEM) A B C i j Fa(i) Fa (j)

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis EM Algorithm 3. Compute expected #reads for each transcript 0.5 2.5 1 1.5 1. Start with random transcript frequencies 0.2 0.5 1 2. Fractionally allocate reads to transcripts

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis EM Algorithm 3. Compute expected #reads for each transcript 0.5 2.5 1 1.5 0.5/6 2.5/6 1/6 1.5/6 1. Start with random transcript frequencies 2. Fractionally allocate reads to transcripts 4. Update transcript frequencies using maximum likelihood estimates

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis EM Algorithm 3. Compute expected #reads for each transcript 0.5/6 2.5/6 1/6 1.5/6 1. Start with random transcript frequencies 2. Fractionally allocate reads to transcripts 4. Update transcript frequencies using maximum likelihood estimates 5. Repeat steps 2-4 until convergence

Accuracy comparison (Kanitz et al. 2015)
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Accuracy comparison (Kanitz et al. 2015) 30M reads

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Runtime comparison (Kanitz et al. 2015) 1 core 16 cores

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Why speed matters? Quantifying estimate uncertainty by bootstrapping M. reads M. reads M. reads

Detected genes/cell -- main population
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Detected genes/cell -- main population Detected genes/cell -- bi-modal distribution Detected genes/cell -- minor population

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Technical failures or biologically interesting cells?

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Cellity cell-QC pipeline Ilicic et al. 2016,

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Methods developed for bulk samples commonly used, but poor fit for scRNA-Seq data… Vallejos et al. 2017,

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis .. and have major impact on downstream analyses Highly Variable Genes Vallejos et al. 2017,

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Alternative methods External controls (ERCC) Pool-based deconvolution

Typical first step is dimensionality reduction by PCA
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Typical first step is dimensionality reduction by PCA 1st component = direction of max. variance 2nd component = orthogonal on 1st, max. residual variance …

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis ZIFA: dimensionality reduction methods for zero-inflated data Pierson and Yau 2015,

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Many clustering algorithms K-means Hierarchical clustering Expectation-Maximization Graph based … scRNA-Seq clustering is an active research area Reducing effect of confounders such as detection rate & cell cycle phase Gene selection Distance metric learning Scalability

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Accuracy comparison for simulated mixtures of CD19+ B cells, CD8+ cytotoxic T cells, CD4+/CD450RO+ memory T cells, CD4+/CD25+ regulatory T cells, and CD4+ helper T cells.

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Single-cell specific methods SCDE: Single-Cell Differential Expression (Kharchenko et al. 2014) scDD: single-cell Differential Distributions (Korthauer et al. 2015) MAST: Model based Analysis of Single-cell Transcriptomics (Finak et al. 2015) D3E: Discrete Distributional Differential Expression (Delmans and Hemberg 2016)

Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis

Marker-based annotation (A. Jackson, unpublished)
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Marker-based annotation (A. Jackson, unpublished)

Matching clusters to known cell types and organism parts
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Matching clusters to known cell types and organism parts Guo M, Wang H, Potter SS, Whitsett JA, Xu Y (2015) SINCERA: A Pipeline for Single-Cell RNA- Seq Profiling Analysis. PLoS Comput Biol 11(11):e doi: /journal.pcbi

Lineage inference: SCUBA
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Lineage inference: SCUBA

Lineage inference: ECLAIR
Reads QC Read mapping Quantification Cells QC Normalization Clustering Differential expression Functional analysis Lineage inference: ECLAIR

Conclusions The range of single-cell applications continues to expand, fueled by advances in technology Primary analysis is compute intensive Requires server/cluster/cloud + linux + scripting Existing frameworks (e.g., Galaxy) lack latest SC tools and do not handle large number of files well

Conclusions Secondary/tertiary analyses can be done on PC/Mac using R
Several comprehensive packages and pipelines available: Seurat SINCERA Granatum Pipelines provide extensive parametrization, but lack support for sensitivity analysis Robust single-cell specific methods still needed

Joint analysis of bulk and scRNA-Seq
Needed to get unbiased population frequencies of cell types Can also identify cell types missed by current capture protocols

Linear model Cell signatures Cell concentrations heterogeneous mixture
cell type 1 cell type 2 cell type 3 𝑥 1 𝑥 2 𝑥 3 𝑥 4 𝑥 5 𝑥 6 gene 1 𝑠 11 𝑠 13 gene 2 𝑐 1 𝑐 2 𝑐 3 gene 3 The heterogeneous mixture is a linear combination of the canonical cell-type gene expression signatures at some set of concentrations. gene 4 gene 5 𝑠 63 gene 6 𝑠 61 Cell signatures Cell concentrations heterogeneous mixture

Estimation of mixture proportions
c min⁡( 𝑆𝑐−𝑥 2 ), 𝑠.𝑡. 𝑙=0…𝑘 𝑐 𝑙 =1 𝑐 𝑙 ≥0 ∀𝑙=0…𝑘

Simultaneous Estimation of Mixture Proportions and Missing Signature
C min 𝑆𝐶−𝑋 2 , 𝑠.𝑡. 𝑙=0…𝑘 𝑐 𝑙 𝑗 =1 ∀𝑗=0…𝑛 𝑐 𝑙 ≥0 𝑙=0…𝑘 𝑠 𝑖 ≥0 𝑖=0…𝑚 This non-negative non-linear least squares objective is a simple model that works well in practice and it is similar to the NMF approaches. The initial guess for the missing signature is set by the average of the known signatures, the concentrations are set to be uniform.

Acknowledgements

Computational Methods for Analysis of Single Cell RNA-Seq Data

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