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\begin{document}

\title{HCA}



\author[1]{Rahul Satija}%
\author[2]{marioni}%
\author[2]{Oliver Stegle}%
\affil[1]{New York Genome Center}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 25, 2024}


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Aligning Biological Manifolds for Integrated Analysis of Single Cell
Data

\textbf{1. Summary}

The ability to integrate information across multiple datasets represents
an enormous opportunity for the human cell atlas (HCA), yet presents
substantial computational challenges. Even for individual tissues, the
HCA will be constructed from datasets produced across multiple
technologies, with samples taken from multiple individuals. However, in
order to comprehensively define a set of human cellular phenotypes, the
community cannot build a separate atlas for each technology, but instead
must be capable of unifying together findings from diverse experiments.
Here we aim to address a fundamental question for the human cell atlas:
how can we integrate a diverse community effort to study a complex human
system effectively in order to construct a coherent atlas? We propose
that powerful machine-learning techniques based on `joint manifold
learning', often used in the `alignment' of massive imaging datasets to
recognized shared high-dimensional features, can be used to recognize
shared cellular phenotypes across single cell datasets.~

This proposal will establish a new collaboration between the Satija Lab
at New York Genome Center, and the Marioni and Stegle Labs at EMBL/EBI,
who all have leading expertise in computational integration for
scRNA-seq, but have not previously worked together. We will
collaboratively develop a set of methods and best practices for data
integration, alongside novel metrics and benchmarks that are of
significant importance and value to the community. We will apply these
approaches to fully integrate seven human neuronal scRNA-seq datasets,
combining data from shallow, deep, cytoplasmic, and nuclear scRNA-seq
technologies. Finally, we will extend these approaches to integrate
datasets generated across different developmental timepoints, species,
and spatial technologies. Our work will generate not only benchmarks and
metrics, but also a clear roadmap for how the HCA can assemble an
integrated atlas from diverse data types from virtually any tissue.

\textbf{2. Project Aims, and how they address program goals}

\textbf{Aim 1} : We will collaboratively derive best~practices, metrics,
and benchmarks for integrating heterogeneous scRNA-seq datasets.~

Accompanied by new metrics and quantitative benchmarks, this aim will
facilitate the development and the comparison of alternative methods for
scRNA-seq data integration.

\textbf{Aim 2~}: To demonstrate the power of this approach, we will
integrate all available transcriptomic datasets profiling the human
nervous system, including sparse, deep, cytoplasmic, and nuclear single
cell transcriptomics.~

This will provide a clear roadmap for HCA to construct a coherent atlas
of cell states from diverse community-generated datasets, demonstrating
the strengths of integrated analysis for cellular phenotyping.

\textbf{Aim 3}~\textbf{(Rahul)} : We will extend our analysis to
integrate neuronal datasets across species, mapping human cell types to
their mouse counterparts, and demonstrating a path towards functionally
characterization of human cell states.~

Here, we will demonstrate that cross-species comparisons can map human
discoveries onto experimentally tractable models, analagous to the
essential role of comparative genomics for interpreting the human genome
project.

\textbf{Aim 3}~\textbf{(John)}: We will extend the methods developed in
Aims 1 and 2 to integrate data from sequential stages of development,
thus providing the ability to stitch together datasets from multiple
time points while controlling for confounding effects. This approach
will help us understand cell fate decisions and provide insight into
developmental processes. While initially using data from mouse
development, the methods developed will be applicable to in vitro
differentiation protocols that use organoid development as a basis on
which to study human development.~

\textbf{Aim 3 (Oliver):} We will extend these approaches derived in Aim
2 to integrate spatial single-cell readouts with conventional scRNA-seq
collected from disassociated cell populations. This is a particular
important instance of data integration that will be required for
aligning spatial tissue contexts and single-cell RNA-seq, a critical
analysis aspect of the HCA. The approaches we seek to derive will
provide the computational foundation for the joint analysis of multiple
spatial datasets coupled with conventional scRNA-seq.

\par\null

\textbf{3. Prior Contributions and Preliminary Data}

Our groups have been at the forefront of designing integrated learning
strategies for scRNA-seq, including published and impactful methods to
infer spatial localization based on gene expression, and to quantify and
disentangle confounding sources of variation.~Most recently, the Satija
and Marioni labs published the first analytical strategies to learn
biological manifolds jointly across scRNA-seq datasets generated from
different individuals, technologies, and species.~While developed
independently, these methods leverage distinct yet complementary~machine
learning approaches based on `manifold alignment' (including canonical
correlation analysis (CCA), non-linear warping algorithms, cosine
normalization, factor analysis and mutual nearest neighbor
identification), to align subpopulations that are shared across datasets
(Haghverdi et al.; Butler and Satjia). Notably, combining datasets
significantly boosts statistical power compared to independent analyses,
and can lead to the discovery of new subpopulations and differentially
expressed genes. The Stegle lab has also recently introduced a novel
statistical framework based on scalable latent-factor variable models to
decompose technical sources of variation from biological heterogeneity,
a crucial step for data integration (Buettner et al.).~

While promising, however, these methods represent only initial solutions
to key challenges. Improved performance will require an exchange of
ideas, collaborative optimization, and rigorous benchmarking on both
simulated and diverse real datasets.~We note that instead of pursuing
these goals independently, this RFA provides an opportunity to work
towards these goals together, and we welcome additional collaboration
with other groups across the HCA community interested in these
challenges.

\par\null

\textbf{4. Proposed Work and Deliverables}

\textbf{Aim 1} : Collaborative development of methods, metrics, and
benchmarks for scRNA-seq integration.

We will aim to collaboratively develop best-in-class methods for
aligning distinct datasets, building upon our previous work. ~First, we
will combine innovative aspects of both approaches to improve
performance, for example, utilizing CCA and factor models in combination
with cosine normalization to help define mutual nearest neighbors across
datasets. ~Second\textbf{,~}we will extend the methods to perform true
multiple alignment across all input datasets, instead of pairwise
alignment to a reference. Here, we will exploit ideas originating from
multiple sequence alignment, designing and optimizing an objective
function that aligns shared cell phenotypes across all datasets by
identifying different aspects of common variation, aided by principles
borrowed from multi-CCA and agglomerative
clustering.~Third\textbf{,~}existing metrics designed to evaluate batch
effect correction are poorly suited to heterogeneous scRNA-seq data.
Consequently, we will develop entropy-based metrics that reward a shared
manifold across all datasets, without sacrificing structure (blurring
cell types) during the alignment. These metrics will provide the
necessary means for objectively comparing and assessing alternative
methods in different contexts.~

To assess performance, we will use both synthetic and real datasets.
Synthetic data will be generated using Splatter (Zappia et al.), and
will be designed to focus especially on the setting where a rare
sub-population of cells is present in only one batch. This situation is
potentially problematic for any data integration strategy: we have
noticed that our existing methods tend to collapse rare populations.
Given this, we will ensure that the identifiability of these populations
is properly reflected in the new metrics we define, thus motivating the
development of integration methods that do not obscure such signals.
Additionally, five recently published scRNA-seq of human pancreatic
islets encompass multiple technologies
(SMART-Seq2/CelSeq/CelSeq-2/inDrop), and feature abundant and rare
populations with canonical markers, thus forming an ideal human dataset
for benchmarking.~

Deliverables from this Aim will be a GitHub repository, containing code
for the newly devised integration strategies, alongside clear and
reproducible examples. This repository will also contain simulated data
generated to reflect a variety of real life settings, and the
newly-defined metrics for assessing the performance of integration
strategies. Benchmark datasets and tests cases will be distributed using
the HCA data infrastructure and using accessible web portals (see
Dissemination).~

\textbf{Aim 2} : Construction of an integrated atlas of the human brain
from diverse, community-generated datasets

The human brain consists of billions of cells exhibiting extraordinary
heterogeneity in molecular composition, localization, electrophysiology,
and function. This remarkable diversity, coupled with challenges in
sample dissociation and processing, has led to a rich variety of
scRNA-seq datasets of the human brain. These include human neurons from
fresh tissue (Darmanis et al., 2015), laser-capture microdissection
(Nichterwitz et al. 2016), Patch-seq (Lin et al. 2016), and nuclear
profiling with plate-based (Lacar et al, 2016) ~microfluidic (Lake et
al., 2016) and droplet-based (Habib et al, 2017; Lake et al 2017), yet
all eight datasets measure the same underlying tissue as input.
Therefore, the human brain represents an ideal tissue for development
and benchmarking of methods for scRNA-seq integration.

We will therefore aim to combine all of these datasets together and
construct a coherent atlas of cellular phenotypes in the human brain,
based on our best-practice methods and metrics. While ambitious, our
preliminary data indicate that this goal is fully achievable, and we
welcome the inclusion of additional neuronal datasets that become
available during the project period. We~will perform a single
graph-based clustering analysis on the fully integrated, and strongly
expect the increased cell numbers to dramatically boost our ability to
detect rare and subtle neuronal cell states.

We will aim to deliver : 1) A catalogue of cell states and genetic
markers that is robust to differences in technology alongside rare cell
types which are only found in a subset of datasets, 2) Systematic
comparison of transcriptomic profiles for the same cell state across
technologies, with a particular focus on identifying gene sets that are
enriched in either scRNA-seq or scNuc-seq experiments, 3) Jupyter
notebooks with reproducible workflows, laying out a clear roadmap for
similar analyses in diverse tissues. We will assess the performance of
our methods using the benchmarks developed above, but will leverage
longstanding collaborations with neuroscientists (Tom Maniatis; Gord
Fishell; Steve Mccaroll, Evan Macosko) to assist in interpretation and
exploration of our findings.

\textbf{Aim 3}~\textbf{(Rahul)} : Integrate neuronal datasets across
species, mapping human cell types to their mouse counterparts

HCA aims to identify hundreds to thousands of cell types in the human
brain based on molecular and spatial characterization, but functional
characterization or perturbation of these cells cannot be performed in
humans. Understanding the human genome faces similar challenges, and
comparative genomics represents an invaluable tool to identify conserved
signals, highlight important differences, and map human sequences onto
tractable model systems. We propose that cross-species analysis will
perform a similar essential role for HCA, and play a crucial step in
connecting a catalog of cell types towards deeper biological
understanding.

We recently published the first integrated analysis of human and mouse
pancreatic atlases, identifying ten shared cell types despite
significant evolutionary divergence. We will extend this analysis here
to create an integrated atlas of the mammalian nervous system,
leveraging landmark neuronal datasets in the adult mouse (Zeisel, Allen,
Macosko). Our successful alignment of pancreatic islet cells
demonstrates that this goal is feasible, even when only a subset of
transcriptomic markers are shared across species . We will also
rigorously test strategies for mapping gene ontologies between species
to see how different strategies affect performance. Our deliverables
will be as stated in Aim 2, but here, we will also focus on reporting
the best transcriptomic markers that are shared between species,
potentially enabling the construction of murine Cre driver lines for
functional characterization. In addition, we expect that our initial
characterization of cell states that are shared across species, or
unique to either, will be of significant value to the neuroscience
community, and will begin to establish the power of applying lessons
from comparative genomics to HCA.

\textbf{Aim 3 (John)}:

The approaches outlined in Aim 1 focus on improving the performance of
data integration methods when combining multiple datasets generated from
the same underlying population of cells (e.g., a specific tissue) but
using either different technologies or where cells are collected from
different individuals. In the context of developmental biology and when
comparing tissues across species, the assumption that we are considering
the same underlying population of cells does not hold. For example, in
the context of tissue differentiation, cells collected at different
stages of development will consist of a mix of common cell types (e.g.,
precursor populations present at different time points) as well as
transitional populations present at only specific time points as well
as, ultimately, terminally differentiated cells.

At present, technological limitations mean that cells are sampled
sequentially, meaning that to construct pseudotemporal differentiation
trajectories for many biological processes it is necessary to combine
information across batches. This applies both in the context of
developmental biology (e.g., sampling different stages of early mouse
development) and when modelling the development of human cell types in
vitro using organoid based systems. To integrate data collected from
sequential stages of development, we propose to jointly learn the
biological manifold while correcting for batch effects.

To this end, we propose to extend the Mutual Nearest Neighbor approach
by employing a more formal factor analysis framework. Specifically, we
will assume that variability in the expression profiles within the
combined dataset (i.e., considering cells from all timepoints) can be
explained by a series of hidden factors that we want to infer. Each
factor will be ``active'' for a given set of genes that co-vary
consistently across the entire dataset.

To disentangle batch effects from biological signal we will assume that
batch effects apply to large numbers of genes and will thus be captured
by dense factors with large numbers of active genes (including
housekeeping genes). Importantly, we will also assume that technical
effects are orthogonal between pairs of batches and, crucially, that
they are always orthogonal to the biological signal of interest. To
identify this biologically meaningful signal we will identify
informative factors, which will generally have a smaller number of
active genes. Additionally, prior information, corresponding to the
stages when samples are collected, can help guide the choice of factors.

\par\null

\textbf{Aim 3 (Oli):}

The methods developed in aim 2 allow for integrating different scRNA-seq
datasets by modelling shared sources of gene expression covariation. We
here seek to extend these methods to additional single-cell
technologies, most notably expression assays that deliver spatially
resolved expression levels, a critical component of the HCA.

An important of conventional scRNA-seq of disassociated populations of
cells is that the~natural tissue contexts of of the cells is lost.
Complementary data from spatial profiling methods ~provide indispensable
data to fill in these gaps, allowing to place single-cell RNA-seq
profiles into the context of tissue coordinates. While the generation of
these dataset is already underway in different contexts and a major
component of several HCA projects, there is lack of computational
strategies for integrating spatial expression data and scRNA-seq
datasets. To address this, we will here extend the methods derived in
aim 2 to account for spatial information of the cells. These approaches
will allow for obtaining new insights into spatial components of gene
expression variation, including predictions of spatially expression
coordination of cells from scRNA-seq. At the core of our approach will
be the development of new factor models that use spatial Gaussian
processes priors on the inferred factors. We have recently proposed one
of the first methods for~modelling spatially resolved expression
datasets using this class of model (Sveensson et al., 2017). By
connecting these different models it will be possible to infer factors
that explain co-expression clusters with and without a spatial
underpinning, which can be integrated with scRNA-seq data from
disassociated cells.~

-- more here --

\par\null

\textbf{5. Dissemination of Methods, Collaboration with CZI, Commitment
to Sharing}~

Our laboratories have been at the forefront of methods development for
single cell data analysis and integration. In 2015, the Marioni and
Satija groups independently published the first analytical methods to
integrate scRNA-seq datasets with in-situ hybridization databases,
enabling the inference of a cell's spatial localization based on its
gene expression. All groups also have created and maintained powerful,
widely used, and fully open-source scRNA-seq analytical toolkits, scran
(Marioni), scater (Stegle) ~and Seurat (Satija), demonstrating our deep
commitment to fully sharing analytical methods with the community.

\par\null

\textbf{BRIEF PROJECT SUMMARY (250 words; currently~exactly 250)}

The opportunity of integrating information across multiple datasets will
maximise the power of the human cell atlas (HCA). However, this is a
substantial computational challenge. Even for individual tissues, the
HCA will not be constructed from a single dataset, but will exploit
multiple technologies and samples from different individuals. The former
is particularly important for single cell RNA-sequencing, where
comparative studies have consistently found that no single approach is
optimal, with each technology having distinct strengths and weaknesses.

Given this, we herein address a fundamental question for the HCA: how
can we integrate a diverse community effort to effectively ~construct a
coherent atlas of human cell types? We propose that powerful
machine-learning techniques based on `joint manifold learning', often
used in the `alignment' of massive imaging datasets to recognized shared
high-dimensional features, can be used to recognize shared cellular
phenotypes across single cell datasets.

This proposal will establish a new collaboration between the Satija Lab
at New York Genome Center, and the Marioni and Stegle Labs at EMBL/EBI,
who all have expertise in computational integration for scRNA-seq, but
have not previously worked together. We will collaboratively develop a
set of methods and best practices for data integration, alongside novel
metrics and benchmarks that are of importance and value to the
community. Using these approaches we will integrate seven human neuronal
scRNA-seq datasets, combining data from shallow, deep, cytoplasmic, and
nuclear scRNA-seq technologies. Finally, we will extend these approaches
to integrate datasets generated across different developmental
timepoints, or even different species.

~

\textbf{COLLABORATIVE NETWORK (500 words - 300 below)}

Our laboratories have been at the forefront of methods development for
single cell data analysis and integration. From the very first methods
for identifying highly variable genes (Brennecke et al., 2013) from
single-cell RNA-sequencing data, through to modelling and removing
confounding variables (Buettner et al., 2015, Buettner et al., 2016) and
integration of spatial information with scRNA-seq measurements (Achim et
al., 2015; SAtija et al., 2015), to the development of machine learning
methods for interpreting single-cell variation (Angermueller et al.,
2017), ~{[}RAHUL / OLI: DO YOU WANT TO HIGHLIGHT SOMETHING ELSE
HERE??{]}, our groups have consistently pioneered methods. Importantly,
these methods are all open source and widely used by the community: for
example, the scran (Marioni), scater (Stegle) and Seurat (Satija)
packages all represent full open-source scRNA-seq analytical toolkits,
clearly demonstrating our deep commitment to fully sharing methods with
the wider community.

Additionally, over the past few months, all three of our groups have
been working on methods for integrating multiple datasets: either
different scRNA-seq datasets (Marioni, Satija) or multiple single-cell
omics experiments (Stegle). With this RFA comes the opportunity to
expand these methods so that they are suitable for the exceptionally
complex data generated by the HCA while, at the same time, developing
standards and resources that will be of high utility to the wider
scientific community.~

The collaborative network will function via interactive means (slack
channels / google docs / GitHub repos) where members from each group
will share code and ideas as they are generated. This immediate
interaction will ensure that we can hone in on the most appropriate
solutions in an expeditious manner. Additionally, we will organise
frequent in person meetings (at least thrice-yearly) where the
scientists working on each aspect of the project will meet one another
and interact. Such gatherings play an important role not only in pushing
forward the science but in building a strong and interactive community
of computational biologists.

\par\null

\textbf{KEYWORDS}

Single-cell biology

Data integration

Statistics

Machine learning

Collaboration

Open-source

\par\null

\textbf{SIGNIFICANT PUBLICATIONS RELATED TO THIS PROPOSAL (FIVE PER
GROUP)}

\textbf{JOHN:}

Achim et al., Nat Biotechnol., 2015

Haghverdi et al., Biorxiv, 2017

Lun, Bach, Marioni, Genome Biol., 2016

Lun, McCarthy, Marioni, F1000R, 2017

Scialdone et al., Nature, 2016

\textbf{Oli:}

Buettner et al., Nat Biotechnol., 2015

Buettner et al., Biorxiv 2016

Angermuelller et al., Genome Biol., 2017

Svennson et al., Biorxiv 2017

Angermueller et al., Nat Methods., 2016

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