Festival Of Genomics 2016 - Brain talk

1Jean Fan / Festival of Genomics / June 2016

Jean Fan NSF GRFP | Bioinformatics and Integrative Genomics PhD Candidate Kharchenko Lab | Department of Biomedical Informatics | Harvard University

Applying single cell transcriptomics: unraveling the complexity of the brain

Motivation: Characterize heterogeneity and identify cell subpopulations with scRNA-seq

Jean Fan / Festival of Genomics / June 2016

Valent P, Bonnet D, De maria R, et al. Cancer stem cell definitions and terminology: the devil is in the details. Nat Rev Cancer. 2012;12(11):767-75.

Cancer

Kaech SM, Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat Rev Immunol. 2012;12(11):749-61.

T Cells

Greig LC, Woodworth MB, Galazo MJ, Padmanabhan H, Macklis JD. Molecular logic of neocortical projection neuron specification, development and diversity. Nat Rev Neurosci. 2013;14(11):755-69.

Single cellRNA-seq

Food For Thought◦ How can we identify transcriptional subpopulations in a way that is robust

and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out

of single-cell RNA-seq data?

Food For Thought◦ How can we identify transcriptional subpopulations in a way that is

robust and takes into consideration technical artefacts from single cell RNA-seq?

◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out

Challenges: scRNA-seq data is highly variable and noisy◦ Expect high correlation between replicates

expression in bulk replicate 1

Challenges: scRNA-seq data is highly variable and noisy◦ Expect high correlation between replicates◦ Many differences between individual cells

(even of the same type)◦ Biological vs. technical differences◦ Focus on the biological variability◦ Control for the technical variability

◦ ex. measurement failures (drop-outs)

Single Cell

Previous work: SCDE - use error models to get a better handle on technical noise

Previous work: SCDE - use error models to get a better handle on technical noise◦ Estimate true

biological variability of a gene

◦ Account for possible drop-out events

Cross-fits

Cell 1

Cross-fits Error Models

Cell 1

◦ Assess variability of expressing taking into consideration expression magnitude dependencies

Variance Normalization

Error models and normalization helps us understand the data on a probabilistic level:

What is the chance this 0 expression in this cell is due to drop-out or true non-expression?

What is the chance that this gene is really this variable given the expected variability for genes at this average expression magnitude?

PAGODA (Pathway And Geneset OverDispersion Analysis) applies error models and variance normalization to characterize heterogeneity and identify subpopulations

pklab.med.harvard.edu/scde

PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets◦ Rather than relying on a few genes, look for broader patterns of variability◦ Coordinated patterns of variability of genes linked to function/phenotype

== stronger signal -> increases statistical power

PAGODA overview: assess expression within annotated pathways and de novo gene sets

PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion

PAGODA overview: Remove redundancy pathways and gene sets, and visualize

Pathway based approach integrates prior knowledge to increase statistical power and provide interpretability of identified subpopulations

(example next)

and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most out

PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations

pathway clusters

Kun Zhang

Jerold Chun

PAGODA applied to mouse neural progenitors identifies and characterizes subpopulations

PAGODA integrated with FISH data spatially placed subpopulations

github.com/hms-dbmi/brainmapr

PAGODA integrated with FISH data spatially placed subpopulations

Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr

PAGODA identifies multiple, potentially overlapping aspects of transcriptional heterogeneity

Allen Brain Atlas; https://github.com/hms-dbmi/brainmapr

and takes into consideration technical artefacts from single cell RNA-seq?◦ What are the different ways to group and classify cells in the brain?◦ In additional to expression heterogeneity, how can we make the most

out of single-cell RNA-seq data?

out of single-cell RNA-seq data? ◦ Alternative splicing

PAGODA applied to human cortical cells identifies and characterizes subpopulations

Xiaochang Zhang

Chris Walsh

Marker genes confirm subpopulation identified by PAGODA

PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells

Needs bulk

Needs bulk -> pool single cells

Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ

out of single-cell RNA-seq data? ◦ Alternative splicing◦ Copy number alteration detection / integrative analysis

BADGER quantitatively assess posterior probabilities of copy number alterations

Bayesian Approach to CNV Detection from single cell RNA-seq (BADGER)

BADGER applied to scRNA-seq identified subclonal expansion in progressive MM

Soo Lee

Peter Park

Woong-Yang Park

Hae-Ock Lee

PAGODA integrated with BADGER connects genetic with transcriptional heterogeneity

ScRNA-seq contains (noisy) expression as well as (noisy) splicing and some (noisy) genetic information.

Novel statistical and computational methods and techniques are still needed to harness the potential of scRNA-seq data!

Thanks! Kharchenko Lab

Peter Kharchenko

Joseph Herman

Park Lab

Soo Lee

Semin Lee

Hae-Ock Lee

Walsh Lab

Xiaochang Zhang

Funding

Festival Of Genomics 2016 - Brain talk

Education

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