Institute for Quantitative and Computational Biosciences

QCBio Research Seminar in collaboration with B.I.G. Summer: Hilary Coller
2021-07-16 14:00 - 14:30 (America/Los_Angeles) | ZOOM, CA, United States
TITLE: “Is there a Quiescence Histone Code?”
ABSTRACT: Many of the cells in our bodies are quiescent, that is, temporarily not dividing. Under certain physiological conditions such as during tissue repair and maintenance, quiescent cells receive the appropriate stimulus and are induced to enter the cell cycle. The ability of cells to successfully transition into and out of a quiescent state is crucial for many biological processes including wound healing, stem cell maintenance, and immunological responses. Histone modifications have been shown to play a role in chromatin packing and accessibility, nucleosome mobility, gene expression, and chromosome arrangement. We have been testing the role of the H4K20me3 mark as a regulator of quiescence in cultured cells and in mice. Our data implicate this histone mark in chromatin compaction, cell proliferation, chromosome positioning, expression of critical cell cycle regulatory proteins, and mouse growth during development. Moving forward, our goal is to test whether H4K20me3 is part of a histone code that ensures proper chromosome compaction and positioning to establish and maintain a quiescent state.

QCBio Research Seminar in collaboration with B.I.G. Summer: Harold Pimentel
2021-07-24 14:00 - 14:30 | ZOOM, CA, United States
TITLE: “Model driven design and analysis of quantitative phenotype screens”
ABSTRACT: Increasingly, CRISPR screens are coupled with flow cytometry (FACS) to sort cells and quantify the
impact of genetic perturbations on a continuous phenotype. While FACS provides much more
quantitative information than simple survival screens, it introduces a number of experimental and
statistical challenges. Furthermore, as experimentalists push these screens in limited primary cells
or in vivo, they lack principled guidelines on how experimental parameters including multiplicity of
infection, guide RNA coverage, or FACS bin cutoffs affect statistical power. We present models for
both experimental design and inference of gene regulation in these screens. We show that
commonly used parameters are far from optimal and screens can be performed with a 20 times
reduction in cells at comparable accuracy. Our inference procedure models biological replicates,
infers the latent protein distribution, and infers experimental parameters. Together these analyses
provide a holistic framework for designing and analyzing highly parallel FACS screens.

https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/07/Harold-Pimentel-edited.mp4

QCBio Research Seminar in collaboration with B.I.G. Summer: David Wong
2021-07-30 14:00 - 14:30 | ZOOM, CA, United States
TITLE: “Salivary exRNA for Gastric Cancer Detection”
ABSTRACT: Biomarkers are needed for non-invasive early detection of gastric cancer (GC). We investigated whether salivary extracellular RNA (exRNA) biomarkers can be used as an efficient and economical clinical evaluation tool for GC. Unstimulated whole saliva samples were prospectively collected from 294 subjects (163 GC and 131 non-GC controls) who underwent endoscopic evaluation at the Samsung Medical Center in Korea. The salivary transcriptomes of 63 GC and 31 non-GC controls were profiled, and mRNA biomarker candidates were verified with RT-qPCR. In parallel, microRNA biomarkers were profiled and verified with saliva samples from 10 GC and 10 controls. Candidate biomarkers were validated with RT-qPCR in an independent cohort of 100/100 saliva samples from GC and non-GC patients. Validated individual markers were configured into a best performance panel. We identified 30 mRNA and 15 miRNA candidates whose expression pattern associated with the presence of gastric cancer. Among them, 12 mRNA and 6 miRNA candidates were verified with the discovery cohort by RT-qPCR and further validated with the independent cohort (n=200). The configured biomarker panel consisted of 3 mRNAs (SPINK7, PPL and SEMA4B) and 2 miRNAs (miR-140-5p and miR-301a), which were all significantly down-regulated in GC group, yielded an AUC of 0.81 (95%CI, 0.72-0.89). When combined with demographic factors, the performance of the panel reached an AUC of 0.87 (95%CI, 0.80-0.93). We have discovered and validated a panel of salivary exRNA biomarkers with credible clinical performance for the detection of GC. Our study demonstrates the credential of salivary exRNA biomarker as a potential screening and risk-assessing tool for GC.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/07/David-Wong-edited.mp4

QCBio Research Seminar in collaboration with B.I.G. Summer: Valerie Arboleda
2021-08-06 14:00 - 14:30 | ZOOM, CA, United States
TITLE: “From Bench to Vending Machine: Development and Deployment of Novel Diagnostic testing for SARS-CoV-2 using Genomic Technology”
ABSTRACT: Frequent and widespread testing of members of the population who are asymptomatic for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for the mitigation of the transmission of the virus. Despite the recent increases in testing capacity, tests based on quantitative polymerase chain reaction (qPCR) assays cannot be easily deployed at the scale required for population-wide screening. Here, we show that next-generation sequencing of pooled samples tagged with sample-specific molecular barcodes enables the testing of thousands of nasal or saliva samples for SARS-CoV-2 RNA in a single run without the need for RNA extraction. The assay, which we named SwabSeq, incorporates a synthetic RNA standard that facilitates end-point quantification and the calling of true negatives, and that reduces the requirements for automation, purification and sample-to-sample normalization. We used SwabSeq to perform 80,000 tests, with an analytical sensitivity and specificity comparable to or better than traditional qPCR tests, in less than two months with turnaround times of less than 24 h. SwabSeq could be rapidly adapted for the detection of other pathogens.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/07/Valerie-Arboleda-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Karen Mohlke, Professor, Genetics, School of Medicine, UNC-Chapel Hill
2021-09-27 11:00 - 12:00 | ZOOM, CA, United States
“Genetic and environmental effects on chromatin accessibility at cardiometabolic trait loci”
Hosted by Paivi Pajukanta

Bioinformatics/Human Genetics Seminar Series: Abigail Bigham, Associate Professor, Anthropology, UCLA
2021-10-04 11:00 - 12:00 | ZOOM, CA, United States
“Omic insights into Andean high-altitude adaptation”
Hosted by Jason Ernst

Bioinformatics/Human Genetics Seminar Series: Maria C. Avila Arcos, Assistant Professor, Internat. Laboratory for Human Genome Research, Universidad Nacional Autonoma de Mexico
2021-10-11 11:00 - 12:00 | ZOOM, CA, United States
“The genetic legacy of the trans-atlantic slave trade into New Spain”
Hosted by Kirk Lohmueller

Bioinformatics/Human Genetics Seminar Series: Doug Speed, Professor, Center for Quantitative Genetics and Genomics, Aarhus University
2021-10-18 11:00 - 12:00 | ZOOM, CA, United States
“New methods for analyzing genome-wide association study data, including improved tools for computing polygenic risk
scores”
Hosted by Maria Izabel Cavassim

Bioinformatics/Human Genetics Seminar Series: Fabian Theis, Head of the Institute of Computational Biology at Gelmholtz Munich, Professor, Technical University of Munich
2021-10-25 11:00 - 12:00 | ZOOM, CA, United States
TITLE: “Learning cellular state and dynamics in single cell genomics”
Hosted by Jessica Li

QCBio Research Seminar: Giovanni Quinones Valdez (Xiao), Grad student in Bioengineering
2021-10-27 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “scAllele, a versatile tool for the detection and analysis of variants in scRNA-seq.”
ABSTRACT: Single-cell RNA sequencing (scRNA-seq) data contain rich information at the gene, transcript, and nucleotide levels. Most analyses of scRNA-seq have focused on gene expression profiles, and it remains challenging to extract nucleotide variants and isoform-specific information. Here, we present scAllele, an integrative approach that detects single nucleotide variants, insertions, deletions, and their allelic linkage with splicing patterns in scRNA-seq. We demonstrate that scAllele achieves better performance in identifying nucleotide variants than other commonly used tools. The read-specific variant calls by scAllele enables allele-specific splicing analysis. Applied to a lung cancer scRNA-seq data set, scAllele identified variants with strong allelic linkage to alternative splicing, some of which being cancer-specific. scAllele represents a versatile tool to uncover multi-layer information and novel biological insights from scRNA-seq data.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Giovanni-Valdez.mp4

QCBio Research Seminar: Ulzee An (Sankararaman), Grad student in Computer Science
2021-10-27 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “AutoComplete: Deep Learning-based Phenotype Imputation”
ABSTRACT: Health data has become increasingly available, vast in scale, and highly missing. For many downstream applications, the ability to accurately impute missing features in health records may tap into additional analytical power which would be unrealized otherwise. While existing imputation methods are applicable, many fall short in one or more aspects of being reliable or scalable in the domain of massive, highly incomplete, and heterogenous population-scale data. We propose AutoComplete, a deep learning-based imputation method that extends with ease to incomplete datasets with millions of entries and handles heterogeneous data of continuous and categorical format. In imputing phenotypes for a collection of half-million individuals from the UK Biobank, AutoComplete significantly improved imputation accuracy for several phenotypes in comparison to best-performing low-rank factorization and deep learning methods.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Ulzee-An.mp4

Bioinformatics/Human Genetics Seminar Series: Anya Prince, Associate Professor of Law, Genetic Cluster, University of Iowa College of Law
2021-11-01 11:00 - 12:00 | ZOOM, CA, United States
“Regulating Insurer Use of Genetic Information”
Hosted by Christina Palmer

QCBio Research Seminar: Alexander Markowitz (Boutros), Postdoc in Human Genetics
2021-11-03 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “A pan-cancer multi-omic analysis of tumor proliferation.”
ABSTRACT: The underlying mechanisms of dysregulated cellular proliferation in cancer remain unclear; however, it is hypothesized that specific mutations, mutational signatures, evolutionary trajectories and other global (epi)genomic features may be linked to differing rates of proliferation. In this presentation, I will showcase a landscape analysis of cellular proliferation across primary cancers and cell lines using genome, transcriptome and proteome data from 11,597 primary tumors and 1,804 cell lines across six major consortia. The approach is to develop computational pipelines that will take in next-generation sequenced, multi-omic datasets from an expanding source of consortia, apply rigorous statistical methods to test multiple hypotheses on the global, functional and pharmacogenomics of tumor proliferation and output detailed integration analyses on the underlying genomics of tumor proliferation. By evaluating proliferation at these molecular levels, key insights will be yielded including expansive new resources on the variation of proliferation rate across and within cancer types.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

QCBio Research Seminar: Ricky Wolff (Garud), Graduate Student in Ecology & Evolutionary Biology
2021-11-03 13:00 - 13:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome.”
ABSTRACT: The human gut microbiome is a complex community that harbors substantial ecological diversity at the species level, as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome community are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of co-colonizing lineages. In this study, we sought to characterize the typical strain-level dynamics of the healthy human gut microbiome on timescales ranging from days to years. We show that genetic diversity within almost all species is stationary, tending towards a long-term typical value within hosts over time scales of several years, despite fluctuations on shorter timescales. Moreover, the abundance fluctuations of strains can be sufficiently described by a stochastic logistic model (SLM) – a model used to describe abundance fluctuations among species around a fixed carrying capacity – in the vast majority of cases, suggesting that strains are dynamically stable. Lastly, we find that strain abundances follow the same macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Ricky-Wolff.mp4

Bioinformatics/Human Genetics Seminar Series: Eimear Kenny, Founding Director, Institute for Genomic Health, Mount Sinai School of Medicine
2021-11-08 11:00 - 12:00 | ZOOM, CA, United States
“Population genetics in an era of genomic health”
Hosted by Christa Caggiano

QCBio Research Seminar: Mariana Harris Heredia (Garud), Grad student in Biomathematics
2021-11-10 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Differences in the Signatures of Selection Between the Autosomes and the X Chromosome in Drosophila melanogaster.”
ABSTRACT: Sex chromosomes in Drosophila are found in a hemizygous state in males. Thus, deleterious mutations may be purged more rapidly on the X chromosome than on the autosomes. This purging can result in less available standing variation that can seed adaptation on the X chromosome. Here, we investigate how differences in the recessivity of alleles on the X chromosome versus autosomes impact the mode and tempo of adaptation. Specifically, we test the hypothesis that hard sweeps, in which a single adaptive mutation rises to high frequency, are more common on the X chromosome, while soft sweeps, in which multiple haplotypes rise to high frequency simultaneously, are more common on the autosomes. We analyze D. melanogaster genomic data from two populations, North Carolina, and Zambia, and find evidence that suggests that indeed, hard sweeps are more prevalent on the X chromosome and soft sweeps are more common on the autosomes.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

QCBio Research Seminar: Guo Xiaohui (Pinter-Wollman), Postdoc in Ecology & Evolutionary Biology
2021-11-10 13:00 - 13:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Decoding alarm signal propagation of seed-harvester ants, Pogonomyrmex californicus.”
ABSTRACT: Alarm signal propagation through social-insect colonies provides an empirically tractable context for analyzing information flow through a natural system, with useful insights for network dynamics in other social groups, including human social networks. Here, I develop a methodological approach to track alarm spread within the group of harvester ants, Pogonomyrmex californicus. I alarmed initial 3 individuals and tracked subsequent signal transmission through the group. Because there was no actual threat, the alarm was false, allowing us to assess amplification and adaptive damping of collective alarm response. I trained a Random-Forest machine learning regression model to quantify alarm behavior of individual workers from multiple movement features associated with alarm behavior in this species. This approach provides reliable continuous assessments of an individual’s behavioral state at much finer temporal scales and more consistently than can be achieved visually. I combined the ML alarm state assessments with proximity data from tracking software (ABCTracker) to construct a propagation network of alarm spread. Using this system, alarm propagation can be manipulated and assessed to ask and answer a wide range of questions on information and misinformation flow in social networks.
For lunch at 12:30pm with the speaker, please RSVP at qcboffice@lifesci.ucla.edu

Bioinformatics/Human Genetics Seminar Series: Erick Matsen, Professor, Public Health Sciences Division, Associate Program Head of Herbold Computational Biology Program, Fred Hutchinson Cancer Center
2021-11-15 11:00 - 12:00 | ZOOM, CA, United States
“Towards Bayesian phylogenetics via systematic search and gradient ascent”
Hosted by Sriram Sankararaman

QCBio Research Seminar: Chloe Yap (Gandal), Visiting Graduate Student, University of Queensland
2021-11-17 12:00 - 12:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Restricted diet drives autism gut-microbiome associations, and other tales from the Australian Autism Biobank”
ABSTRACT: The Australian Autism Biobank (AAB) is an initiative of the Autism CRC – the first national, cooperative research effort focused on autism across the lifespan. The AAB recruited a total of ~2,500 autistic children, family members, and unrelated undiagnosed children, and couples deep phenotypic information with multi-omic datasets (SNP genotyping, stool metagenomics, DNA methylation, metabolomics, WGS). One particularly controversial area in the field has been the potential causal contribution of the gut microbiome to autism. I will present results from one of the largest metagenomics study of the autism stool microbiome to date (n=246, including 99 children diagnosed with autism). We propose a model whereby microbiome changes in autistic children may reflect consequences of behaviour and dietary preferences, and we caution against undue emphasis on the microbiome having an upstream role in autism.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/10/Chloe-Yap.mp4

QCBio Research Seminar: Paheli Desai-Chowdhry (Savage), Grad student in Biomathematics
2021-11-17 13:00 - 13:30 | Boyer 159, 611 Charles E. Young Dr. E., Los Angeles, CA, 90095, United States
TITLE: “Asymmetric Branching Scale Factors as Features in Neuronal Cell-Type Classification”
ABSTRACT: Neurons are connected by complex branching processes – axons and dendrites – that process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental piece of neuroscience. In previous work, we constructed a biophysical theory that establishes a correspondence between neuron structure and function as mediated by principles such as time or power minimization, using undetermined Lagrange multipliers to predict scaling ratios for axon and dendrite sizes across branching levels. Here, we relax the assumption of symmetrical branching in the model to determine asymmetric branching powers that differ across different cell types due to functional tradeoffs. Furthermore, we use scale factors related to asymmetric branching as features in machine learning classification to distinguish between different cell types. We find significant distinctions in the asymmetric scaling ratios between Purkinje cells and motoneurons. The performance of these classification methods gives us important insights into the correspondence between structure and function across different cell types.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/09/Paheli-Desai-Chowdhri.mp4

Bioinformatics/Human Genetics Seminar Series: Katie Peichel, Head of the Division Evolutionary Ecology, University of Bern
2021-11-22 11:00 - 12:00 | ZOOM, CA, United States
“The role of chromosome evolution in adaptation and speciation in stickleback fish”
Hosted by Kirk Lohmueller

Bioinformatics/Human Genetics Seminar Series: Stephen Piccolo, Associate Professor, Biology, Member of Simmons Center for Cancer Research, Brigham Young University
2021-11-29 11:00 - 12:00 | ZOOM, CA, United States
TITLE: “TBD”
Hosted by Sarah Spendlove

Bioinformatics/Human Genetics Seminar Series: Shantanu Joshi, PhD, Assistant Professor, Neurology, Brain Mapping Center, UCLA
2022-01-03 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Aligning Shape Data from Brain Imaging: applications to fMRI time series, diffusion tractography.”
Hosted by Jason Ernst.

QCBio Research Seminar: Jee Yun Han (Boutros), Graduate Student in MBIDP (Molecular Biology Interdepartmental Doctoral Program)
2022-01-05 11:30 - 12:00 | ZOOM, CA, United States
TITLE: “Comprehensive study of gene expression outliers and their regulation mechanisms in pan-cancer.”
ABSTRACT: With the ultimate aim of improving the management of cancer, many groups have studied the molecular characteristics of tumors. Typically, the heterogeneity of cancer evolves rapidly to adapt to its microenvironment, helping cancer evade selective pressures and progress. These various subclones result in resistance to anti-cancer therapies. The extreme dysregulation of some gene such as oncogene is a prominent feature of cancer that can play a critical role in cancer tumorigenesis and accelerating cancer evolution by providing cancer cells with a selective growth advantage. Further research is needed to understand cancer heterogeneity and the extreme gene dysregulation, and these studies will enable us to solve many limitations and obstacles for the inhibition and effective treatment of cancer. In previous studies, cancer outliers such as BCR-ABL and TMPRSS2-ERG were identified as cancer drivers or drug targets, and those showed strong associations with clinical outcomes. The gene expression outliers of cancer are likely to be caused by the diverse genetic and epigenetic variation frequently occurred in cancer. Despite of the important pathological function of cancer outliers, to date, the researches related to outliers have been only limited to a single type of cancer or a single gene, which is insufficient for characterizing them and comprehending their pathological roles in cancer. To fill the gap of our understanding regarding cancer outliers, this proposal will identify the outliers from various cancer types using our novel statistical method and explore the biological functions of outliers in individual cancer, figuring out the unique feature of each cancer. With integrated analysis of the clinical outcome data and the features of outliers within and across cancer types, it will show how outliers affect the progression of cancer. Comparative analysis transcriptome and proteome data analysis will address whether RNA outliers can be propagated into the protein level, prioritizing candidates. Furthermore, the molecular mechanisms for outlier generation in terms of genetic and epigenetic variation will be investigated in cancer, enabling us to understand the central pathways associated with extremely abnormal gene expression in individual cancers. The proposed study is expected to deepen our understanding of the impact of outliers on different cancers by dissecting their dysregulation, and, in turn, will allow us to identify a novel cancer driver and potential drug target.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jee-Yun-Han-edited.mp4

QCBio Research Seminar: Jianxiao Yang (Suchard), Grad Student in Biomathematics
2022-01-05 12:00 - 12:30 | ZOOM, CA, United States
TITLE: “Massive parallelization of massive sample-size survival analysis.”
ABSTRACT:
Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time andmemory efficient single-pass parallel scan algorithms for Cox proportional hazardsmodels and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders-of-magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Jianxiao-Yang-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Lauren McIntyre, PhD, Professor, Molecular Genetics & Microbiology, University of Florida
2022-01-10 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Climate change and maize response to pollutants: gene content, expression and regulation.”
Hosted by Kirk Lohmueller.

QCBio Research Seminar: Evan Maltz (Wollman), Graduate Student in BMSB (Biochemistry, Molecular and Structural Biology)
2022-01-12 11:30 - 12:00 | ZOOM, CA, United States
TITLE: “Phenotypic consequences of gene expression variability.”
ABSTRACT: The central dogma of biology proposes that information flows from genes to RNA to protein, determining protein identity and abundance. Single cell transcriptome measurements provide ample information about RNA abundance in cells. While the expression of individual genes clearly matters for determining phenotype, they do not work independently. How the information in RNA abundances is combined to determine cellular phenotype remains unclear. Here we address this question for a single cellular phenotype, the dynamic Ca2+ signaling response to a ligand. Using an information theory approach, we estimated the mutual information between the expression of subsets of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found a high degree of dependency that corresponds to 60% of Ca2+ signal entropy, yet most genes have redundant information. Furthermore, a relatively small group of genes preserve most of the dependency. Our results demonstrate a high degree of regulatory elasticity with small changes in RNA abundance informing an emergent cellular phenotype.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Evan-Maltz-edited.mp4

Videos can also be viewed on the QCBio YouTube Channel

QCBio Research Seminar: Dongyuan Song (Li JJ), Graduate Student in Bioinformatics
2022-01-12 12:30 - 13:00 | ZOOM, CA, United States
TITLE: “scDesign3: an all-in-one statistical framework that generates realistic single-cell omics data and infers cell heterogeneity structure.”
ABSTRACT: The generation of realistic synthetic data is essential for benchmarking numerous computation tools developed for single-cell omics data. Here we propose an all-in-one statistical framework that generates single-cell omics data from various cell heterogeneity structures, including discrete cell types, continuous cell trajectories, and spatial cell locations. Our framework uses a unified probabilistic model with accessible likelihood. This probabilistic formulation is advantageous in that it enables a straightforward discernment of the heterogeneity structure that best fits a single-cell omics dataset, by leveraging the statistical model selection principle.
https://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Dongyuan-Song-edited.mp4

Bioinformatics/Human Genetics Seminar Series: Jennifer Wilson, PhD, Assistant Professor, Bioengineering, UCLA
2022-01-24 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “Deriving network parameters for understanding drug effects.”
Hosted by Jason Ernst.

Bioinformatics/Human Genetics Seminar Series: Doc Edge, PhD, Assistant Professor, Quantitative and Computational Biology, USC
2022-01-31 16:00 - 17:00 | ZOOM, CA, United States
TITLE: “The new forensic genetics: ‘long-range’ search and genetic privacy.”
Hosted by Nandita Garud.
