BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Guocheng Yuan DTSTART:20200615T113000Z DTEND:20200615T123000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/1 DESCRIPTION:Title: Keynote Talk (Cortex seq-FISH study)\nby Guocheng Yuan as part of Mathematical Frameworks for Integrative Analysis of Emerging Biological D ata Types\n\n\nAbstract\nDr Guocheng Yuan (Dana-Farber Cancer Institute\, Harvard TH Chan School of Public Health) Lab Website: http://bcb.dfci.harv ard.edu/~gcyuan GC will present the SeqFish hackathon study\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Alexis Coullomb DTSTART:20200615T130000Z DTEND:20200615T132000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/3 DESCRIPTION:Title: CORTEX seq-FISH: clustering\nby Alexis Coullomb as part of Mathem atical Frameworks for Integrative Analysis of Emerging Biological Data Typ es\n\n\nAbstract\nAlexis Coullomb is a member of Vera Pancaldi's Lab at Ca ncer Research Centre of Toulouse\, INSERM\, France\, https://www.crct-inse rm.fr/personne/alexis-coullomb/ Alexis Coullomb will present analysis in w hich addressed the questions: * Can scRNA-seq data be overlaid onto seqFIS H for resolution enhancement? * What is the minimal number of genes needed for data integration? Alexis Coullomb was also interested in how could we detect specific spatial areas given the seqFISH gene expression data and by reconstruction the spatial network of cells. Code is available at: http s://github.com/AlexCoul/multiOmics_integration\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Hang Xu DTSTART:20200615T132000Z DTEND:20200615T134000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/4 DESCRIPTION:Title: CORTEX seq-FISH: selection of spatial coherent genes\nby Hang Xu as part of Mathematical Frameworks for Integrative Analysis of Emerging Bi ological Data Types\n\n\nAbstract\nDr Hang Xu is a postdoctoral Fellow in Christina Curtis's Lab (Stanford). Hang obtained her PhD in bioinformatics at the University of Nottingham\, UK. She then trained as a postdoctoral research fellow in the Francis Crick Institute with Charles Swanton. Hang is interested in studying cancer evolutionary dynamics. Her research asked the following questions\; 1. Can scRNA-seq data be overlaid onto seqFISH for resolution enhancement? 2. What is the minimal number of genes needed for data integration? She followed the approaches that described in Zhu's paper (Zhu et al 2018) which integrated scRNAseq and smFISH data. By follo wing the approach\, she randomly selected a subset of differently expresse d genes and applied a SVM model to estimated the minimal number of genes t hat are required data integration. Code is available at https://github.com /gooday23/smfishscRNAHackathon/\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Dario Righelli DTSTART:20200615T134000Z DTEND:20200615T140000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/5 DESCRIPTION:Title: CORTEX seq-FISH: software structure and data integration\nby Dari o Righelli as part of Mathematical Frameworks for Integrative Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Dario Righelli is a post doctoral fellow in the Department of Statistics\, University of Padua\, It aly (https://www.researchgate.net/profile/Dario_Righelli) in the lab of Dr . Davide Risso His work focused on the software infrastructure needed to e asily analyze spatial datasets (such as seqFISH\, 10X Visium\, etc.) and t o integrate spatial and non-spatial datasets Code is available at https:// github.com/drighelli/SpatialAnalysis\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Joshua Sodicoff DTSTART:20200615T144000Z DTEND:20200615T150000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/6 DESCRIPTION:Title: CORTEX seq-FISH: integration with scRNA-seq data\nby Joshua Sodic off as part of Mathematical Frameworks for Integrative Analysis of Emergin g Biological Data Types\n\n\nAbstract\nMr. Joshua Sodicoff is a Data Scien ces BS Student in the lab of Joshua Welsh at the University of Michigan Me dical School (https://welch-lab.github.io/people/ ). He addressed the firs t two questions listed on the github page for the seqfish data. Our primar y goal was to integrate seqFISH data with scRNA-seq data to increase resol ution and utilize LIGER to account for dataset-specific differences in exp ression. We also attempted to determine how the number of genes reported i n the spatial data impacts the quality of the integrated data and of cell type mappings generated by our method. To address these questions\, we ana lyzed both the provided seqFISH and scRNA-seq datasets\, as well as additi onal scRNA-seq data (from the more recent Tasic visual cortex publication) and STARmap data Code is available at https://github.com/jsodicoff/birs_s patial_integration\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Singh Amrit DTSTART:20200615T142000Z DTEND:20200615T144000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/7 DESCRIPTION:Title: CORTEX seq-FISH: integration with scRNA-seq data\nby Singh Amrit as part of Mathematical Frameworks for Integrative Analysis of Emerging Bi ological Data Types\n\n\nAbstract\nDr. Amrit Singh is a postdoctoral fello w working with Professor Bruce McManus at the PROOF Centre of Excellence a nd The University of British Columbia and this work was done in collaborat ion with Prof Kim-Anh Le Cao (University of Melbourne) . Dr. Amrit Singh w ork addressed the question if scRNA-seq data be overlaid onto seqFISH for resolution enhancement? The published approach trained a multiclass SVM on the scRNAseq data and applied it to the seqFISH data to estimate the cell -types labels. My approach uses a penalized regression method (glmnet) wit h a semi-supervised approach in order to build a model using both the scRN Aseq+seqFISH data. This strategy uses a recursive approach that involves m ultiple rounds of training glmnet models using labeled data (label and imp uted) and predicting the cell-type labels of unlabeled data. At each itera tion\, cell-type labels with high confidence (probability > 0.5) are retai ned for the next iteration\, where a new glmnet model is trained with the scRNAseq data and seqFISH data with imputed cell-type labels with high con fidence. This process is repeated until all cell-types in the seqFISH data have been labeled or until 50 iterations have been reached (in order to r educe compute times). The advantage of this approach is that more data in used for model training such that the resulting model may generalize bette r to new data. The performance of this approach was estimated using cross- validation\, using only the scRNAseq data as the test set. Code is availab le at https://github.com/singha53/ssenet\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Bernd Bodenmiller DTSTART:20200616T113000Z DTEND:20200616T123000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/9 DESCRIPTION:Title: Keynote Talk (single cell proteomics)\nby Bernd Bodenmiller as pa rt of Mathematical Frameworks for Integrative Analysis of Emerging Biologi cal Data Types\n\n\nAbstract\nhttp://www.bodenmillerlab.com/ will present recent work single cell proteomics work from his lab\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/9/ END:VEVENT BEGIN:VEVENT SUMMARY:Yingxin Lin DTSTART:20200616T130000Z DTEND:20200616T132000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/10 DESCRIPTION:Title: sc targeted proteomics: Predicting outcome\, survival from 3 proteom ics datasets (Keren\, Jackson\, Wagner)\nby Yingxin Lin as part of Mat hematical Frameworks for Integrative Analysis of Emerging Biological Data Types\n\n\nAbstract\nMs. Yingxin Lin is a PhD candidate in Statistics unde r the supervision of Prof. Jean Yang\, Dr. John Ormerod and Dr. Rachel Wan g at the University of Sydney. Her main research interest is in normalisat ion and statistical modelling of single-cell RNA-seq data. https://yingxin lin.github.io/ She analyzed the three sc targeted proteomics datasets (Ker en et al.\, Jackson et al.\, and Wagner et al.) which all presented compre hensive portraits of breast cancer tumor immune microenvironment\, utilizi ng different methods to identify and characterize different subtypes of pa tients with the evidence associated with survival. Code is available at ht tps://yingxinlin.github.io/BIRS_analysis\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/10/ END:VEVENT BEGIN:VEVENT SUMMARY:Chen Meng DTSTART:20200616T132000Z DTEND:20200616T134000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/11 DESCRIPTION:Title: sc targeted proteomics: comparing multi-block PCA\, linear regressio n\nby Chen Meng as part of Mathematical Frameworks for Integrative Ana lysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Chen Meng is He ad of Bioinformatics at the Bavarian Center for Biomolecular Mass Spectrom etry\, TU Munich\, Freising\, Germany. (https://www.baybioms.tum.de/about- us/people/) Dr. Chen Meng mainly worked approach integrating partially ove rlapping proteomic data collected on different patients with similar pheno types using two methods: simple linear regression (as a baseline/control) and multi-block PCA (MBPCA\; including multiple co-inertia\, multiple cano nical correspondence analysis as special cases). In theory\, MBPCA should outperform simple linear regression because it finds the correlated patter n across multiple datasets\, preventing the potential problem of overfitti ng to one dataset. Code is available at https://github.com/mengchen18/BIRS BioIntegrationWorkshop\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/11/ END:VEVENT BEGIN:VEVENT SUMMARY:Pratheepa Jeganathan DTSTART:20200616T134000Z DTEND:20200616T135500Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/13 DESCRIPTION:Title: sc targeted proteomics:Stan model for latent Dirichlet allocation\nby Pratheepa Jeganathan as part of Mathematical Frameworks for Integrat ive Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Pratheep a Jeganathan received her masters (2013) and PhD (2016) from Texas Tech Un iversity and is currently a postdoctoral research fellow working with Prof Susan Holmes at Stanford University (https://profiles.stanford.edu/prathe epa-jeganathan) Her work considered solutions for 1) how should we approac h integrating partially-overlapping proteomic data collected on different patients with similar phenotypes? 2) Without including the spatial x-y coo rdinate data\, how well can we predict cell co-location? She will illustra te the topic modeling on discretized targeted proteomics data and the meth od to infer cell co-location. We integrated the two SingleCellExperiment u sing MultiAssayExperiment class in the R/Bioconductor package. We converte d the normalized data to original protein expression and discretized (for the preliminary analysis\, we added a minimum of the normalized value for each marker\, but we need to know the sample mean and standard deviation o f marker expressions in the MIBI data). We considered each cell is a docum ent and wrote a Stan model for latent Dirichlet allocation. Using posterio r samples of topic proportions\, we inferred the latent topics with a high er proportion in each cell. We proposed a solution to the alignment issue. Code is available at https://github.com/PratheepaJ/Banff_proteomics\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/13/ END:VEVENT BEGIN:VEVENT SUMMARY:Duncan Forster DTSTART:20200616T144000Z DTEND:20200616T150000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/14 DESCRIPTION:Title: Networks - learning salient gene and protein features from network t opologies\nby Duncan Forster as part of Mathematical Frameworks for In tegrative Analysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Du can Forster is postdoctoral fellow in Molecular Genetics co-supervised by Prof Gary Bader and Charlie Brown at the University of Toronto. https://ba derlab.org/Members His work has addressed the following questions. Firstly \, we wanted to determine whether recent deep learning architectures (name ly graph neural networks/graph convolutional networks) could be used to le arn salient gene and protein features from network topologies. If so\, the se features could be integrated in a trainable\, end-to-end fashion allowi ng for effective integration of biological networks. These recent deep lea rning architectures have shown substantial improvements over previous netw ork feature learning approaches on a range of tasks\, which motivates thei r use in biological domains. Secondly\, we wanted to determine more effect ive evaluation strategies in order to compare integration approaches. This is a challenging task due to differences in input network sizes and stand ard coverage\, biases and quality of the standards\, differences in method outputs (networks vs. features)\, and biases in the current evaluation st rategies themselves. Code is available at https://github.com/bowang-lab/BI ONIC\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/14/ END:VEVENT BEGIN:VEVENT SUMMARY:Oliver Stegle DTSTART:20200617T113000Z DTEND:20200617T123000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/15 DESCRIPTION:Title: Keynote Talk (scNMT-seq study)\nby Oliver Stegle as part of Math ematical Frameworks for Integrative Analysis of Emerging Biological Data T ypes\n\n\nAbstract\nDr. Oliver Stegle is a group leader in Statistical gen omics and systems genetic at the European Bioinformatics Institute\, Cambr idge\, UK (https://www.ebi.ac.uk/research/stegle/) His lab published Argel aguet et al. 2019 Multi-omics profiling of mouse gastrulation at single-ce ll resolution Nature volume 576\, pages487–491(2019) https://www.nature. com/articles/s41586-019-1825-8 which was the basis of the scNMT-seq study challenge https://github.com/BIRSBiointegration/Hackathon/tree/master/scNM T-seq\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/15/ END:VEVENT BEGIN:VEVENT SUMMARY:Al J Abadi DTSTART:20200617T130000Z DTEND:20200617T132000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/16 DESCRIPTION:Title: scNMT-seq: multivariate integrative analyses\nby Al J Abadi as p art of Mathematical Frameworks for Integrative Analysis of Emerging Biolog ical Data Types\n\n\nAbstract\nDr. Al J Abadi is a Research Fellow and sof tware developer in Computational Genomics in the lab of Prof Kim-Anh Lê C ao at the University of Melbourne\, Australia (https://lecao-lab.science.u nimelb.edu.au/) He addressed the challenges of i) Identification of multi- omics signatures that characterize lineage\, stage or both: We applied a r egularised partial least square analysis which can find key markers which characterize the coordinated lineage and stage-specific changes in differe nt modalities ii) Dealing with missing values: In integrative analyses\, w e applied an iterative algorithm which can handle missing values without p otentially inducing spurious correlations in the datasets while allowing f or select for variables that are correlated across data modalities and cha racterize the stage and/or lineage of the cells\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/16/ END:VEVENT BEGIN:VEVENT SUMMARY:Joshua Welch DTSTART:20200617T132000Z DTEND:20200617T134000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/17 DESCRIPTION:Title: scNMT-seq: LIGER\nby Joshua Welch as part of Mathematical Framew orks for Integrative Analysis of Emerging Biological Data Types\n\n\nAbstr act\nDr. Joshua Welch\, is Assistant Professor of Computational Medicine a nd Bioinformatics in Department of Computational Medicine and Bioinformati cs\, University of Michigan (https://welch-lab.github.io/). Most recently\ , his lab has focused on developing open-source software for the processin g\, analysis\, and modeling of single-cell sequencing data. Key contributi ons in this area include SingleSplice\, the first computational method for single-cell splicing analysis\; SLICER\, an algorithm for inferring devel opmental trajectories\; and LIGER\, a general approach for integrating sin gle-cell transcriptomic\, epigenomic and spatial transcriptomic data. We u sed our previously published algorithm LIGER for this analysis. The advant age of our method is that it can integrate different single-cell modalitie s measured on different single cells. The corresponding disadvantage is th at we do not leverage the known correspondence information from true multi -omic measurements. We tried multiple data processing strategies for the s cNMT accessibility data. We observed limited alignment with all processing strategies\, but the more differentiated cell types showed more correspon dence. We also analyzed a different single-cell multi-omic dataset\, SNARE -seq (RNA+ATAC) from mouse frontal cortex. LIGER was able to effectively i ntegrate this dataset\, finding corresponding cell types between RNA and A TAC data without using the known cell correspondences. We are further inve stigating the possible biological and technical explanations for these dif ferences. Code is available https://github.com/jw156605/scNMT\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/17/ END:VEVENT BEGIN:VEVENT SUMMARY:Arshi Arora DTSTART:20200617T134000Z DTEND:20200617T140000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/18 DESCRIPTION:Title: scNMT-seq:MOSAIC\, or Multi-Omic Supervised Integrative Clustering\nby Arshi Arora as part of Mathematical Frameworks for Integrative Anal ysis of Emerging Biological Data Types\n\n\nAbstract\nArshi Arora is a Res earch Biostatistician in Dr. Ronglai Shen's lab at Memorial Sloan Ketterin g Cancer Center\, https://www.mskcc.org/profile/arshi-arora Her research a ddressed the following question\; We wish to address the problem of identi fying localized molecular signatures with respect to an outcome of interes t such as stage and lineage. This poses an interesting challenge in unders tanding heterogeneity in cell populations across multiple data modalities. We aim to illustrate that the application of a supervised integrative clu stering will provide a more accurate delineation of cell subpopulation acr oss genomic\, epigenomic\, and transcriptomic landscape that is directly r elevant to the biological outcome of interest. Code is available at https: //github.com/arorarshi/scNMT_seq_MOSAIC\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/18/ END:VEVENT BEGIN:VEVENT SUMMARY:Wouter Meuleman DTSTART:20200617T141000Z DTEND:20200617T143000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/19 DESCRIPTION:Title: Integration sc chromatin : DNase-seq data across 733 biosamples\ nby Wouter Meuleman as part of Mathematical Frameworks for Integrative Ana lysis of Emerging Biological Data Types\n\n\nAbstract\nDr. Wouter Meuleman is an investigator at the Altius Institute for Biomedical Sciences. Woute r Meuleman’s research focuses on the organization of regulatory elements in the human genome and their relation to cellular state and gene regulat ion. Prior to joining Altius\, Wouter did postdoctoral work at MIT and the Broad Institute. He obtained his PhD in Computational Biology from Delft University of Technology\, the Netherlands.(https://www.meuleman.org/) Dr Meuleman analyzed a large DNase I chromatin accessibility dataset ( 733 bi osamples) which are extremely rich and complementary to other commonly use d data types. As such\, they are a perfect candidate for integrative analy ses. Although chromatin accessibility data result in rich genome-wide maps of putative regulatory elements\, these elements remain largely unannotat ed and therefore these maps remain hard to use for downstream analyses. We wanted to provide a comprehensive annotation for each individual element. Code is available at https://github.com/Altius/DHSVocabulary\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/19/ END:VEVENT BEGIN:VEVENT SUMMARY:Susan Holmes DTSTART:20200618T120000Z DTEND:20200618T130000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/20 DESCRIPTION:Title: Keynote Talk: Computational Challenges\nby Susan Holmes as part of Mathematical Frameworks for Integrative Analysis of Emerging Biological Data Types\n\n\nAbstract\nProfessor Susan Holmes is a Professor of Statis tics and member of BioX\, at Stanford University\, a John Henry Samter Uni versity Fellow in Undergraduate Education\, a Fellow of the Fields Institu te. Moderator for the stat.AP arxiv. Slides at https://spholmes.github.io/ \n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/20/ END:VEVENT BEGIN:VEVENT SUMMARY:Michael Love DTSTART:20200618T133000Z DTEND:20200618T140000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/21 DESCRIPTION:Title: Benchmarking\nby Michael Love as part of Mathematical Frameworks for Integrative Analysis of Emerging Biological Data Types\n\nAbstract: T BA\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/21/ END:VEVENT BEGIN:VEVENT SUMMARY:Vincent Carey DTSTART:20200619T120000Z DTEND:20200619T130000Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/22 DESCRIPTION:Title: Software Infrastructure\nby Vincent Carey as part of Mathematica l Frameworks for Integrative Analysis of Emerging Biological Data Types\n\ n\nAbstract\nVincent Carey is Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine\, Brigham and Women’s Hospital\, Harvard Medical School. He is former Editor-in-Chief of The R Journal. He is Scientific Director of Bioinformatics in the National Institute of Alle rgy and Infectious Diseases Immune Tolerance Network\, and is a member of the Scientific Advisory Board of the Vaccine and Immunology Statistical Ce nter of the Collaboration for AIDS Vaccine Discovery. Vince is a co-founde r of the Bioconductor project.\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/22/ END:VEVENT BEGIN:VEVENT SUMMARY:Elana Fertig DTSTART:20200615T112500Z DTEND:20200615T112900Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/23 DESCRIPTION:Title: CORTEX seq-FISH session\nby Elana Fertig as part of Mathematical Frameworks for Integrative Analysis of Emerging Biological Data Types\n\n \nAbstract\nDr. Fertig is a co-chair of this meeting and is an Associate P rofessor of Oncology and Assistant Director of the Research Program in Qua ntitative Sciences at Johns Hopkins University\, with secondary appointmen ts in Biomedical Engineering and Applied Mathematics and Statistics\, affi liations in the Institute of Computational Medicine\, Center for Computati onal Genomics\, Machine Learning\, Mathematical Institute for Data Science \, and the Center for Computational Biology. Homepage: https://fertiglab.c om Twitter: @FertigLab\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/23/ END:VEVENT BEGIN:VEVENT SUMMARY:Aedin Culhane DTSTART:20200616T110000Z DTEND:20200616T110500Z DTSTAMP:20260422T185556Z UID:BIRS_20w5197/24 DESCRIPTION:Title: sc targeted proteomics session\nby Aedin Culhane as part of Math ematical Frameworks for Integrative Analysis of Emerging Biological Data T ypes\n\nAbstract: TBA\n LOCATION:https://researchseminars.org/talk/BIRS_20w5197/24/ END:VEVENT END:VCALENDAR