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Open Source Biology & Genetics Interest Group

Open source scripts, reports, and preprints for in vitro biology, genetics, bioinformatics, crispr, and other biotech applications.

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 October 3, 2021

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Rainbow Genomics Launches Pan-Cancer Test to Detect Genomic Rearrangements and Genetic Disorders Associated with Cancers →
← Reading microarray data from the Gene Expression Omnibus

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Videos

High-Performance Computing in R for Genomic Research** Author(s): Jiefei Wang Affiliation(s): University at Texas Medical Branch High-performance computing(HPC) has become an essential topic for handling large high-throughput data and bringing complex algorithms to life. However, the intricate nature of parallelization structures often hinders people from implementing the correct parallel computing cluster in R. In this talk, we will introduce the modern parallel framework package BiocParallel and its utility packages SharedObject and RedisParam. During our exploration of High-performance computing in R, we will dive into a variety of important topics, covering both foundational principles and practical applications. These include: 1. Understanding the core concepts of parallel computing 2. Creating a mini computing cluster utilizing home and office computing resources 3. Enhancing computational efficiency, managing errors, and debugging code 4. Harnessing the power of cloud computing Several real-study examples will be provided to illustrate the power of parallel computing. By the end of the talk, attendees should have a foundational understanding of parallel computing and be capable of creating a simple cluster for research purposes. We warmly invite R users of all skill levels to join us in expanding their knowledge in this dynamic field.
Workshop: High-Performance Computing in R for Genomic Research
https://youtu.be/zcUMq9ZAOnw?feature=shared&t=82 Unraveling Immunogenomic Diversity in Single-Cell Data https://youtu.be/zcUMq9ZAOnw?feature=shared&t=864 Model-based Dimensionality Reduction for Single-cell RNA-seq with Generalized Bilinear Models https://youtu.be/zcUMq9ZAOnw?feature=shared&t=1723 Interactive analysis of single-cell data using flexible workflows with SCTK2.0 https://youtu.be/zcUMq9ZAOnw?feature=shared&t=2542 cytofQC: A better way to clean cytof data See https://bioc2023.bioconductor.org/abstracts/track10/ for more information
Short Talks: Single cell
Voyager: Exploratory spatial data analysis from geospatial to spatial -omics** Author(s): Lambda Moses,Kayla Jackson,Laura Luebbert,Pétur Helgi Einarsson,Pall Melsted,Lior Pachter Affiliation(s): California Institute of Technology Social media: https://twitter.com/LambdaMoses With the rise of spatial transcriptomics, many methods have been written for specialized tasks in spatial transcriptomics data analysis, such as finding spatially variable genes, finding spatial regions, deconvoluting Visium spots, data integration with other modalities and with multiple tissue slices, and identifying interactions between cell types. Some of these methods adopted methods from geospatial data analysis, as spatial data analysis mostly in geographical space has existed for decades before the rise of spatial transcriptomics. However, there is a rich exploratory spatial data analysis (ESDA) tradition from the geospatial tradition not yet well-utilized in spatial -omics. The SpatialFeatureExperiment (SFE) package brings Simple Feature to SingleCellExperiment to represent and operate on geometries such as cell segmentation polygons and Visium spot polygons bundled with gene expression data. Voyager performs ESDA and spatial data visualization to SFE objects. Voyager implements univariate ESDA methods beyond the commonly used Moran's I, including the correlogram to study length scales of spatial autocorrelation and local spatial analysis methods giving a result for each cell such as local Moran's I and local spatial heteroscedasticity to study local variations in spatial autocorrelation. These analyses can be performed on gene expression, cell metadata, and attributes of geometries. Voyager also implements multivariate spatial data analysis, such as a scalable implementation of MULTISPATI PCA, a form of spatially informed PCA previous used in ecology, which can give more spatially coherent clustering and shed light on negative spatial autocorrelation, which is often neglected in spatial analyses. Both SFE and Voyager are available on Bioconductor. In addition, we have written comprehensive tutorials for Voyager, performing ESDA on data from technologies including Visium, Xenium, CosMX, slide-seq, and MERFISH, with up to about 400,000 cells. These tutorials are built on GitHub Actions to ensure reproducibility and scalability. Example datasets used in the tutorials are available in Bioconductor package SFEData. Finally, we have a Python implementation of core functionalities and have written compatibility tests to ensure that the R and Python implementations give consistent results.
Workshop: Voyager: Exploratory spatial data analysis from geospatial to spatial -omics
demuxSNP: supervised demultiplexing of scRNAseq data using cell hashing and SNPs Author(s): Michael P Lynch,Laurent Gatto,Aedin C Culhane Affiliation(s): University of Limerick Social media: https://twitter.com/AedinCulhane Sequencing at a single-cell resolution allows unprecedented understanding of biologically relevant differences between individual cells compared to previous bulk methods. Though the cost of sequencing has dropped considerably, multiplexing, that is loading multiple biological samples into each sequencing lane, is widely used to further reduce costs. The obtained sequencing reads must then be demultiplexed or assigned to their respective biological sample. Experimental and computational methods have been proposed to facilitate demultiplexing. We present our approach and its corresponding Bioconductor package ‘demuxSNP’ which overcomes current challenges in demultiplexing scRNAseq reads from genetically distinct biological samples. Demultiplexing is usually done through cell tagging or exploiting genetic differences between donor groups using SNPs. Tagging methods work by experimentally tagging cells in a biological sample with a different HTO (hashtag oligonucleotide) or LMO (lipid modified oligonucleotide) tag prior to sequencing. These tags are then sequenced to form a counts matrix. Due to non-specific binding, counts of a given cell tag form a bimodal distribution consisting of a higher signal distribution and lower background distribution. The performance of these algorithms is highly dependent on the tagging quality. Lower tagging quality is associated with greater overlap in these bimodal distributions and poorer demultiplexing performance. Alternatively, single nucleotide polymorphisms (SNPs) variation between biological samples can be used to perform computational demultiplexing with genotype information (Demuxlet) or genotype free (Vireo, Souporcell). SNPs methods require no cell tagging but require genetically distinct samples. Supervised methods perform well but require the genotype is known which has an associated cost. To address this, genotype free methods were developed which do not require prior knowledge of the SNPs in a biological sample, however struggle to identify rare samples. Performance of SNPs methods are dependent on sequencing depth and decrease with a high presence of ambient RNA. We propose a method utilising data from both tags and SNPs to increase the performance of cell tagging methods. Using cell tagging methods we can confidently demultiplex some but not all cells due to issues with tagging quality. We can train a classifier using the SNP profiles of singlet cells assigned with high confidence from the genetically distinct biological samples. In addition to high confidence singlets, demuxSNP combines singlet SNP profiles from different singlet groups to simulate doublets and includes these in the training data. We can then assign low confidence cells (doublets or singlets which we could not confidently call using cell tagging methods). demuxSNP uses a subset of SNPs in a computationally efficient and cell-type unbiased algorithm. This method overcomes several limitations of current methods. Unlike Demuxlet, there is no additional genotyping required. Unlike genotype free SNPs methods, rare samples can be assigned provided a proportion of the group has adequate tagging quality. The usage of a subset of SNPs reduces computational cost relative to other SNP based methods. The demuxSNP package is submitted to Bioconductor.
Package demo: demuxSNP: supervised demultiplexing of scRNAseq data using cell hashing and SNPs
Differential Expression Analysis using Limma and qsvaR Author(s): Hedia Tnani,Joshua M. Stolz,Leonardo Collado-Torres,Louise A. Huuki-Myers,Leonardo Collado Torres Affiliation(s): Lieber Institute for Brain Development Social media: https://twitter.com/TnaniHedia RNA-seq and its expression levels are vulnerable to both environmental and technical influences. In previous studies that explored gene expression data from bulk RNA-seq of postmortem human brain samples, degradation was identified as a crucial and often overlooked issue, particularly when comparing patients to controls. Analyses that do not adjust for degradation effects can lead to false positive differentially expressed genes due to this strong confounder. Furthermore, previous attempts to model RNA quality failed to remove the effects of degradation. To address this problem, the quality surrogate variable analysis (qSVA) method to remove the RNA quality confounding was developed (Jaffe et al., PNAS, 2017). To make it applicable to more brain regions as well as make it user friendly, we developed the qsvaR Bioconductor package. During the workshop, we will provide a step-by-step explanation of the qsvaR Bioconductor package (http://www.bioconductor.org/packages/release/bioc/html/qsvaR.html) for entry-level users and apply it to a publicly available example dataset. We will describe how using qsvaR can improve the reproducibility of DE analyses across datasets. This workshop will also be useful for those interested in learning the basics of limma and differential expression analysis, with a focus on postmortem human brain data.
Package demo: Differential Expression Analysis using Limma and qsvaR
Nancy Zhang Professor of Statistics and Data Science, University of Pennsylvania Dr. Zhang is Professor of Statistics and Data Science in The Wharton School at University of Pennsylvania. Her current research focuses primarily on the development of statistical and computational approaches for the analysis of genetic, genomic, and transcriptomic data. In the field of Genomics, she has developed methods to improve the accuracy of copy number variant and structural variant detection, methods for improved FDR control, and methods for analysis of single-cell RNA sequencing data. In the field of Statistics, she has developed new models and methods for change-point analysis, variable selection, and model selection. Dr. Zhang has also made contributions in the area of tumor genomics, where she has developed analysis methods to improve our understanding of intra-tumor clonal heterogeneity. https://nzhanglab.github.io/
Keynote: Nancy Zhang, Tumor subclone detection and immune niche modeling on spatial transcriptomics
Heng Li Associate Professor, Dana-Farber Cancer Institute and Harvard Medical School Heng Li studies advanced computational algorithms to solve practical biological problems, currently with a focus on sequence alignment, variant calling, de novo assembly, data storage, and information query. He developed and maintains several widely used software packages, such as BWA, samtools, minimap2, and seqtk, for analyzing high-throughput sequencing data. He has also collaborated with multiple research groups and published work on the analysis of single-cell sequence data, chromosome conformation, cancer genomics, population genetics and species evolution. https://hlilab.github.io/
Keynote: Heng Li, Genome assembly in the telomere-to-telomere era
https://youtu.be/_peGjCcfvLQ?feature=shared&t=90 Meet the Technical Advisory Board (TAB) The Technical Advisory Board purpose is to support the Bioconductor mission by developing strategies to ensure long-term technical suitability of core infrastructure for the Bioconductor mission. Core infrastructure includes: all aspects of package addition, management, and distribution; end-user engagement (e.g., web, support site, and slack); developer support; and development of packages for use by the broader developer community and identifying and pursuing technical and scientific aspects of funding strategies for long-term viability of Bioconductor. More information at https://bioconductor.org/about/technical-advisory-board/ https://youtu.be/_peGjCcfvLQ?feature=shared&t=633 Meet the Community Advisory Board (CAB) The Community Advisory Board purpose is to support the Bioconductor mission by empowering user and developer communities by coordinating training and outreach activities and enabling productive and respectful participation by Bioconductor users and developers at all levels of experience. More information at https://bioconductor.org/about/community-advisory-board/ Meet the Core The Core Team works on developing and maintaining core Bioconductor packages for analyzing various types of genomic data, including genomics, transcriptomics, proteomics, and epigenomics, ensuring quality control and reliability of the software packages, and maintaining the Bioconductor infrastructure, including the software repositories, build systems, and documentation. More information at https://www.bioconductor.org/about/core-team/
Meet the BioC teams, TAB,
Tidy genomic and transcriptomic single-cell analyses Author(s): Stefano Mangiola,Michael I Love Affiliation(s): WEHI Social media: https://twitter.com/steman_research This workshop will present how to perform genomic and transcriptomic data analysis using the tidy data paradigm. This paradigm became the standard in R data analysis across many fields. It provides a standard way to organise data values, where each variable is a column, each observation is a row, and data is manipulated using a familiar and easy-to-understand vocabulary. The data structure remains consistent across manipulation and analysis functions. Tidy genomic analyses, such as tests for genomic enrichment, can be performed with plyranges and nullranges. You will learn techniques for tidy manipulation of genomic range data; how to generate and compare to ranges representing a particular null hypothesis; how to decide between bootstrapping versus creating a matched set from the background; and how to perform more complex operations, such as computing correlations of activity at promoters and nearby enhancers. Tidy transcriptomic analyses can be performed with tidySingleCellExperiment, tidySummarisedExperiment, tidybulk and tidyverse. You will learn the differences between SingleCellExperiment, SummarizedExperiment and their tidy representation; basic tidy operations possible with tidySingleCellExperiment; how to manipulate and visualise SingleCellExperiment in a tidy manner with a real-world case study that will include single-cell and (pseudo-)bulk analyses.
Package demo: Tidy genomic and transcriptomic single cell analyses
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