Skip to content

Open Source Biology & Genetics Interest Group

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

  • About
  • XML/JSON
  • Filtering
  • Suggestions
  • Internships
  • Postdocs

Open Source Biology & Genetics Interest Group

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

 Posted in Research

how to draw 3d histogram with errorbar using R

 September 22, 2021

how to draw 3d histogram with errorbar using R

0

Hi, I have a dataset of the gene expression after different drug treatment in different hours, each treatment has 6 values. I want to plot a 3d histogram using R. I know I can use some pachages, but they may not have errorbar function. How could I do that? Thanks!


histogram


errorbar


3D


R

• 34 views

Read more here: Source link

Tagged R

Post navigation

How to avoid over-correction by using harmony or CCA to batch correction in scRNA-seq? →
← Using VG to genotype a GAM using a graph constructed from GFA

Tags

.bbs .bim .csv .evec .faa .fam .Gbk .gmt .NET Bio .PDBQT .tar.gz A375 AACR-NCI-EORTC ABEs ABL-21058B ACADVL AccuraDX ACE2 aCGH ACHL ACLAME ACTB ACTREC Adobe Audition adonis ADPribose Advantech AfterQC AGAT AI-sandbox Airbnb ajax AJOU Alaskapox ALDEx2 Alevin ALOT AlphaDesign AMOS AMPHORA Amplicon-Seq Ampure XP Amyloidosis ancestryDNA ANCOM-BC ANGeS ANGPTL8 ANGSD anitaokoh annotateMyIDs annotationHub annovar ANSYS anti-aliasing anti-BCMA antiSMASH Apache Kafka apollo ARACNE-AP ArcGIS archlinux Arduino arm64 ARRmNormalization Ascalaph Designer ascat ASM212806v1 ASM238634v1 ASM287662v1 ASM298219v1 ASM314399v1 ASM350094v1 ASM1227490v1 ASoC aspera ASVs ATDBio ATG Athaliana atpB-rbcL AUTOR AVENGER Avogadro AZ Azure Active Directory Azure SQL BAC BALB bamdst Barracuda baseml BasicSTARRseq BATF3 BattleMetrics BBDuk BBMap BBMerge BBSL BBTools bcl2fastq BCR bdgcmp beagle BEAST bedGraph BEDtools bfiles BGCs bgen BGmix BiGCaT BiGG bigWig Bio-DB-HTS Bioclipse bioconda biohaskell BioImageDbs BioJava BioJS BioLabDonkey BioMOBY BIONET BioPerl biorender BioRevolution BioRuby Biosample BioSpace BioSpectrum BioViz biowasm birchhomedir Bismark Bitnami BL21 blaCTX-M-55 blaNDM-5 blaNDM-7 BOLT-LMM Bombyx bowtie2-align Brunello BsmBI BUB1B BUBR1 Bug#991859 bulkRNAseq BUSCO BWS C1QBP C57BL/6J CAGE-SEQ cageminer CAIX-NFS1 CalculateContamination CalculateHsMetrics CareDx Cas7-11 Cas13 CAS13A Cas13d CasMINI CASP14 CBEs CCA CcTop CD-HIT CDKN2A centos CentOS 7 CentriMo cgMLST ChEBI chi2 ChimeraX ChIPComp ChIPpeakAnno ChIPseeker chRDNA ChromHMM Chromium chromoMap chrX CIBERSORTx Cinnamon CIRI-full CITE-seq CITEseq ClinGen CLISH cLOD ClustalO ClustalW clustprofiler CMT2E CNA cnetplot Co-op COBRA codeml commonjs compareCluster ComplexHeatmap comp_chem Comsol config.txt ConsensusClusterPlus Control-FREEC CookHLA corr.test CP033719 CPEC CPF Cr2O3 cram Cre-driver CRIMS CRISPOR CRISPRa CRISPRko CRLMM cRNA CROP-seq CSI Cubase CWL CXCR4 cygwin CyTOF CyVerse DAPARS2 darkRP DAW dba.plotProfile DBHI DBMR dbNSFP dCas9 dCas9-KRAB-MeCP2 dCas9-vp64 dCas9-VPR ddATP DdCBE-mediated ddCTP ddGTP ddH2O ddTTP DE-analysis DE-kupl deepTools degradome-seq Demovir DESC DESE2 DGE dgelist DiffBind directx Diva Django DMR DMRs DMRScan DNA-seq DNAseq dnasp dNdScv DoBISCUIT DOCK2 DRG DRIMseq DROP-seq DSB dseq2 dsQTL dsRed dtu DuoClustering2018 eb-eye Eclipse IDE EcoR1 EDGE-pro EditText eGFP-Puro EggNOG egsea EGT2 EHMT2 eisa electrotransfection ELN EMBOSS ENA EndeavourOS eNeuro enformer enhancedVolcano ENmix enrichGO enrichkegg enrichr ENSCAFG1 ENSEMBL ID ENSG entrezID EOF EPN ER-lysosome ers ERSSA ESBL-EC ESGCT eureKARE eureKAWARDS ex-psu exceRpt ExPASy EzTaxon fabfilter FAP FAST5 fastboot fasterq-dump FASTY FBXW7 fcScan fData featureSet feature_db fetchChromSize ffmpeg FGFR3 Ficoll file.ht2 fimo findMotifsGenome.pl Finite Element Method flagstat flanders.bio Flavivirus-GLUE flowStats flowTrans FoldGO FPKM fq.gz FreeBayes FreeBSD FRiP FSHD FSTAT FuGene6 FunciSNP G2Net Gag/Pol GAM GApps gaschYHS Gatsby gBlock GBM GBR GBS GC-content gcta GDAC gDNA GeCKO geecc GeForce GTX gemBS GEMMA genblastA Gene-Ontology GeneDX GeneGA geneMANIA GenePattern GeneRfold GenGIS genomeGenerate genomelink genomeLoad Genomespace GenomeStudio genomica GenomicsDBImport Gentoo GenXys GEO2R GEOexplorer GEOquery GEO_OPT getEAWP getfasta getGEO Geworkbench GEX gffread ggalt GGBase GGG gGmbH GGtools ggtree GIAB GISTIC GISTIC2.0 gja5 glmLRT global25 GM12891 GM12892 gnomADc gnomix GOChord GOI golubEsets GOPATH GOrilla GOSemSim GOseq GraphQL GRCH37 GRCm38 GRCm39 grenits GRM grompp groupGO GRRDUser GSA GSCs GSE66099 GSE172016 gseGO gsekegg GSM-SAMPLES GSTAr,GSTA Gstreamer gsva GTA GtRNAdb guideRNA GWASpower H3K9me3 H3K27ac H5AD H358 H1299 H2122 haplogroup s Hasura Console HDCytoData HDMI HDR-CRISPR HEK293T HeLa HepG2 HFSP HGNC HGT HGU133Plus2 HiC-Pro HiFiBiO HilbertVis hiPSCs hipSYCL HiTC HitTable hmmer Hmox1 HNC homeoboxB9 HOMER HPC-reditools HPLC HRS hs37d5 HT-Seq HTqPCR HTSeq-count HTSlib HTTP API HuGene-1_0-st HVS hypergravitropic Hyphy I-TASSER IC50 ICA ICAR ICheckpointHelperClient iCOMIC IDbyDNA IDR IEEE IGM ignoreTxVersion IJMS IL-2R ImdSession IMPUTE2 IncRNA InferCNV InfluxDB iNMF IntegraGen Integrated Genome Browser InterMine InterPro InterProScan intronsByTranscript IOError ION torrent IP-MS ipdsummary IRI ischemia-reperfusion ISD iseq iso-seq iTOL IVT JASPAR jbrowse jetbrains ide JETPEI jobRxiv journalctl Jquery jsonpath JSON Schema JunctionSeq junk DNA k-smoothing K562 KaryoploteR KeyError Kibana kissplice Kraken2 KRASG12C kselftest KSpace kubectl L2FC L4D2 label_format LABGeM LabKey Server Lambdas LAMP Largo LaTeX LD50 LDDT LEfse lenti-guide-puro LentiCRISPR LentiCRISPRv2 lentiGuide-puro leukocyt libgromacs5 Liftoff ligand-interaction LilyPond LIMS LiP-MS lme4 LMGene locus_tag LoF log2FoldChange logCPM LRRK2 lumiHumanIDMapping LwaCas13a LXDE LZMA m10kcod.db macs2_bdgdiff MAGeCK maizeprobe Makeblastdb MALT MAplot MAPQ MarkduplicatesSpark marr MARS-seq Maschine MashupMD MCF10A mcr-1 mCRC mCRPC MCScanX MECP2 medulloblastoma mega-x Megahit MemVerge MergeSamFiles mESC mESCs MeSH.Pto.eg.db MetaCyc metagenemark MetaGxPancreas MetaPhlAn2 MetaProdigal metaSeq metawrap METHPED MethylationEPIC methylclockData methylkit MetID MG-rast mg1655 MGEs MHC-I MIC-Drop MicroGenDX mig MINSEQE miQC MiRBase mirdeep2 miRNAseq mixcr mkref MLE MMBIR MMR mnp modelr MOFA MoGene-1_0-st-v1 MOI monocle2 monocle3 monoclle3 montana Monterey moog MOPC mothur motIV Mpeg mphil mpileup mps MQTT MRC MRSA msfGFP MSGFgui MSI MSigDB msk MSMC MTB MTHFD1 MULTI-seq MUMmer mummerplot MuTect MUTECT2 MXene mydb MYO10 NBAMSeq nc1700sJORDAN NCC NCFamilyGenetic NEB neofart NestJs netbenchmark NetCoMi NetMHCpan netprioR NewStem Next.js NextDenovo Nexus nf-core NFDI4Microbiota NfL NGG nginx NGS-Lib-Fwd NHEJ NLS Node.js NovelStem NPC NPSR1 NTLA nu Nucleosome NUMTS NuProbe odseq OMA OMIM OncoSimulR oocyte OpenKIM openSUSE OpenWrt Opto OTU OVCAR3 PACR pact_00210 PAIRADISE pairwise2 PARPis PATCH v2 pathfindR PathVisio pax3 pc016 pCE-mp53DD pd.hg.focus PDAC PDBE PDBep pdInfoBuilder PedPhase pegRNAs PER1 PERMANOVA perTargetCoverage pgdx PGGB PGS phase_trio.sh phastConsElements pheatmap phenix PhiX PHRED phyloseq PI3 PICS PIK3CA PIM Pindel PipeWire platypus pLentiCRISPRv2 plotMA PLS-DA POLG1 polyphen2 PopGen PopGenome popmax PostgreSQL PowerBI ppinfer PrCa pre-IPO PREDA PredicineCARE primer3 PrognomiQ Promethease PROPER-seq ProteoDisco provean PRSice PRSice-2 pseudotime psgendb psiblast PSMC Psme1 pSpCas9 psyt PTM PTPRR pUC19 pULA105E Pulse 2 Pulseaudio Puppeteer PuTTY PWMEnrich PX458 px459 Pybedtools PyCharm pyHAM PYMC3 pymol pymolrc PythonRepo qbittorrent qctool v2 QIAseq qqplot qualimap quant.sf Quanta Quantseq r-bioc-basilisk r-bioc-deseq2 rabbitmq RACK1 RAD-seq ragdolls ramr ranzcr RASGEF1C RASMOL raw_counts RBPs RCB RCorrector RDAVIDWebService RDocumentation React JS reactjs reactome.db ReactomePA ReadAffy readDGE readDNAStringSet recount2 REditools2 regex RegExp REVIGO RFM RFP RFS RfxCas13d RGBA rGREAT rgsepd RhymeZone RIMS-seq RINalyzer rip-md RIPSeekerData RISC-V rlogTransformation RMA RNAmodR.Data RNAseP rnaturalearth rnaturalearthdata roblox ROC_AUC ROH RPKM rpoB RPPA RQN RRBS RRHO2 RSEM RSeQC rsIDs RSQLite Rsubread RTMP rtracklayer rTRMui Ruby RUnit RUNX2 rust-bio rvtest sagenome SAIGE sambamba samblaster SAMD9 samtool SBT ScaleBio ScarHRD scATAC-SEQ SCF SCID ScienceDaily SCIRP SCO-012 scVelo SCVI seer-medicare segemehl selenium seq-lang Seqio.Parse SeqIO.pm SERS SEs sEVs SFK SFS Shapeit Sibelius SiFive sift Single.mTEC.Transcriptomes SingleR SingscoreAMLMutations Sitecore sjdbOverhang SKAT-O SKCM SMAD4 smallRNA smallRNAseq SMAP smartPCA SmartSeq snoRNP SnpEff SNPRelate snpsift snRNA-seq snsplot SNV SOAP SOAP Suite SOS SPACA4 SpCas9 Spike-Ins Spire SQLite sra-toolkit SRF SRP SRR ssODNs ssRNA Stackdriver Stackify Staden Package stats4 Steam STF STP StrainPhlAn stref_00240 StringTie SunTag SV40 SVAseq SwissProt Synapse Syntekabio Synths systemPipeRdata T7EI T7RNAP TAIR tblastx TBSignatureProfiler TCC tcga-brca TensorRT TET2 textfield TFAM TFBSTools TFDP2 TIDE TIGRFAMs TK8912 TME TMM-normalization TnpB tnpR topGO tophat2 tracrRNA traefik TransposonPSI treat_pileup trimAL tRNAdbImport tRNAleu-cox2 TruSeq tSNE Ttc30a ttgsea typescript Ublast UCD UGENE UH UHS UMItools Unipept UniProtKB UniRef90 unity3d uORF useEffect useMart USU UTP UTSW ValueWalk VanillaICE Varscan VCFtools vcfutils VDB VENTUS verilog VG viennaRNA ViPR VirtualBox VMD vodosp.ru VOTCA VP64 VQSR VSCode VTx WASP Waveform WDM WDNA WebRTC WebSockets WebStorm WGBS whg WHIZZY WMD WNN WT1 WW1 WW2 WXS xCT XDR xenografts Xfce xFire XGBoost xGEN XML Schema xpehh Xpert XRCC1 XS Y-DNA Yandex Yogscast YPR193C z-score Zenodo ZNF483 zsh ZyCoV-D zygotes

Categories

  • Business (8,876)
    • Abbott (187)
    • AbbVie (250)
    • Amgen (187)
    • Bayer (185)
    • Boehringer Ingelheim (62)
    • Gilead (137)
    • GlaxoSmithKline (61)
    • Johnson & Johnson (7)
    • Merck & Co. (3)
    • Novartis (306)
    • Pfizer (460)
    • Roche (930)
    • Sanofi (171)
    • Takeda (148)
    • Teva (38)
  • Eli Lily (2)
  • Hiring (16,716)
    • Asia-Pacific (6,506)
    • Europe (6,822)
    • North America (6,957)
    • Russia (997)
  • Research (56,622)

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
Load More... Subscribe

Copyright © 2021 IRZU Inštitut za raziskovanje zvočnih umetnosti

AI Chatbot Avatar
Join us at CRISPR workshops in Koper, Slovenia in 2023. Erasmus+ funds available!
Learn more
This is default text for notification bar
Learn more