 Note on Electronic Access to Journals
The UW Library is generally a paid subscriber to non-open-access journals we cite. You can freely access these
articles from on-campus computers. For off-campus access, follow the "[offcampus]" links below or look at the
library "proxy server" instructions. You will be
prompted for your UW net ID and password.
03/29: -- ---- Organizational Meeting ----
04/05: Linking T cell receptor sequence to transcriptional profiles with clonotype neighbor graph analysis (CoNGA) -- Dr. Phil Bradley -- FHCRC
Authors:
Stefan A. Schattgen, Kate Guion, Jeremy Chase Crawford, Aisha Souquette, Alvaro Martinez Barrio,
Michael J.T. Stubbington, Paul G. Thomas, Philip Bradley
Abstract:
Multi-modal single-cell technologies capable of simultaneously assaying gene expression and surface
phenotype across large numbers of immune cells have described extensive heterogeneity within these complex
populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to
profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX)
profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell
populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR
sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order
to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph
analysis or âCoNGAâ---that identifies correlations between GEX profile and TCR sequence through statistical analysis
of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another
in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell
datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the
classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the
transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a
potential previously undescribed lineage of ânatural lymphocytesâ; a striking association between usage of a
specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR
sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets
continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between
TCR sequence and cellular state in single-cell applications.
DOI: https://doi.org/10.1101/2020.06.04.134536 [offcampus]
04/12: Joint identification of neuron types and type-specific activity-regulated genes with coupled autoencoders -- Dr. Yeganeh Marghi -- Allen Institute
Abstract:
Recent advances in single-cell transcriptomics revealed an enormous diversity among neuronal cells. While
the previous studies confirmed the existence of broad neuron classes, they also pointed towards a complicated
landscape, where neuron types often appear to overlap or form gradients in gene expression. Therefore, a crucial
step toward elucidating the neuronal identity is to jointly identify the discrete and the continuous factors of
variability. Taking advantage of deep learning approaches, we study this problem in a variational framework by
utilizing multiple interacting autoencoders, designed to disentangle the discrete and continuous aspects of neuronal
diversity. We demonstrate the application of our method to a stand-alone single cell RNA sequencing dataset, which
defined over 100 transcriptomic neuron types in the mouse cortex. Our results suggest that the proposed method can
refine the existing classifications of neurons by joint identification of discrete types and type-specific,
activity-regulated genes.
04/19: Hybrid Molecular-Electronic Computing // Interpreting Neural Networks for Bio
Sequences -- Alyssa & Jason // Alyssa
Two short talks this week:
Alyssa La Fleur & Jason Hoffman -- CSE, "Hybrid Molecular-Electronic Computing for
Ubiquitous Biological Sensing Applications"
Alyssa La Fleur -- CSE, "Interpreting Neural Networks for
Biological Sequences by Learning Masks"
04/26: Mapping and navigating the human regulatory genome -- Dr. Wouter Meuleman -- Altius Institute
Abstract:
This year marks the 20 year anniversary of the sequencing of the human genome in 2001. Since then, many
large-scale data generation and analysis efforts have built upon this work by producing genome-wide maps and
annotations. I will highlight several of our own efforts in this area, in particular of maps that describe the human
genome in terms of functional and contextual regulatory annotations. Despite the increasing comprehensiveness,
quality and utility of genomic maps over the last two decades, systems to efficiently navigate them at scale have
remained lacking. This is at least in part due to the enormous growth of available genomic data, resulting in a vast
underutilization of data by a confined focus on the data directly at hand. I will outline ideas for how to address
this challenge in a way that combines human expertise with machine-assisted intelligence.
05/03: Large deviations theory: Data, single-cell biomarkers, and Fisher's fundamental
theorem of natural selection -- Prof. Hong Qian -- AMath
Abstract:
Genomics and the physical chemistry of biomolecules are two pillars of molecular biology. One of the key
branches of the latter is chemical thermodynamics, which, curiously, is quite marginalized in the current research
on computational molecular biology (CMB). I shall revisit this subject, but no starting from physics nor chemistry,
but rather through a result from probability, beyond the law of large numbers and central limit theorem, call large
deviations theory. I shall show how our theory can be applied to Fisher's FTNS as well as to analyzing data from
single cells.
05/10: May the Methanotrophs be with you -- Erin Wilson -- CSE
05/17: Relative Abundance Estimation with Microbiome Data -- David Clausen -- Biostatistics
05/24: Similarity Search with DNA -- Lee Organick -- CSE
Abstract:
In the Molecular Systems Information Lab, we use molecules to do things that silicon computers
traditionally do. Recently, Callie Bee successfully designed and implemented similarity search with DNA (1). This
talk will discuss her work, as well as potential future directions and implications of this work.
(1) https://www.biorxiv.org/content/10.1101/2020.05.25.115477v1.full.pdf
05/31: -- Holiday
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