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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.

 Posted in Research

heatmap of genes pseudotime in monoclle3

 August 12, 2021

heatmap of genes pseudotime in monoclle3

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Tagged monoclle3, pseudotime

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How to trim a GFF3 file based on specific coordinates? →
← extracting a gene from a gmt file

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bgen BGmix BiGG bigWig Bing 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 bsc BSgenome BsmBI BUB1B BUBR1 Bug#991859 bulkRNAseq BUSCO C1QBP C57BL/6J CAGE-SEQ cageminer CAIX-NFS1 CalculateContamination CalculateHsMetrics CareDx Cas7-11 Cas13 CAS13A Cas13d CasMINI CASP14 CBEs CCA CcTop CD-HIT CDKN2A cdx centos CentOS 7 CentriMo cgMLST ChEBI chi2 ChimeraX ChIPComp ChIPpeakAnno ChIPseeker ChiPseq chRDNA ChromHMM Chromium chromoMap chrX CIBERSORTx Cinnamon CIRI-full CITE-seq CITEseq ClinGen CLISH cLOD CLUE ClustalO ClustalW clustprofiler CMT2E CNA cnetplot Co-op COBRA codeml COG commonjs compareCluster ComplexHeatmap comp_chem Comsol config.txt ConsensusClusterPlus Control-FREEC CookHLA corr.test COSMIC CP033719 CPEC CPF Cr2O3 cram Cre-driver CRIMS CRISPOR CRISPRa CRISPRko CRLMM cRNA CROP-seq CRyPTIC CSI cto Cubase Cutadapt CWL CXCR4 cygwin CYP2D6 CyTOF CyVerse DADA2 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 DMSO DNA-seq DNAseq dnasp dNdScv DoBISCUIT DOCK2 DRG DRIMseq DROP-seq DSB dseq2 dsQTL dsRed dtu DuoClustering2018 EB eb-eye Eclipse IDE EcoR1 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 EpCAM EPN ER-lysosome ers ERSSA ESBL-EC ESGCT eureKARE eureKAWARDS ex-psu exceRpt ExPASy EzTaxon fabfilter FAP FAQs FAST5 fasterq-dump fastp FASTY FBXW7 fcScan fData featureSet feature_db FEBS fetchChromSize ffmpeg FGFR3 Ficoll file.ht2 fimo findMotifsGenome.pl Finite Element Method flagstat 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 GCA gcta GDAC gDNA GeCKO geecc GeForce GTX gemBS GEMMA genblastA Gene-Ontology GeneDX GeneGA geneMANIA GenePattern GeneRfold GenGIS genomeGenerate GenomeInfoDb genomelink genomeLoad Genomespace GenomeStudio genomica GenomicsDBImport GENtle Gentoo GenXys GEO2R GEOexplorer GEOquery GEO_OPT getEAWP getfasta getGEO Geworkbench GEX gff3 gffread ggalt GGBase GGG gGmbH GGtools ggtree GIAB GISTIC GISTIC2.0 gitlab gja5 glmLRT global25 GM12891 GM12892 GMOD gnomAD gnomADc gnomix GOChord GOI golubEsets GOPATH GOrilla GOSemSim GOseq Grain GraphQL GRCH37 GRCm38 GRCm39 grenits GRM grompp 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 HaplotypeCaller Hasura Console HCC HDCytoData HDMI HDR HDR-CRISPR heatmaps HEK293T HeLa HepG2 HGNC HGT HGU133Plus2 HiC-Pro HiFiBiO HilbertVis hiPSCs hipSYCL HiTC HitTable hmmer Hmox1 HNC homeoboxB9 HOMER HPC-reditools HPLC HPV 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 ILMN ImdSession Impl 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-mer k-smoothing K562 KaryoploteR KeyError Kibana kissplice Kraken2 KRASG12C kselftest KSpace kubectl L2FC L4D2 label_format LABGeM LabKey Server Lambdas LAMP LaTeX LD50 LDDT LEfse LentiCRISPR LentiCRISPRv2 leukocyt libgromacs5 Liftoff ligand-interaction LilyPond LIMS LiP-MS lme4 LMGene locus_tag LoF log2FoldChange logCPM logFC LRRK2 lumiHumanIDMapping LwaCas13a Lynch LZMA m10kcod.db MACS macs2_bdgdiff MAGeCK MAGs maizeprobe Makeblastdb MALT MAplot MAPQ MarkduplicatesSpark marr MARS-seq Maschine MashupMD MBEDTLS 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 MinION MINSEQE miQC MiRBase mirdeep2 miRNAseq mixcr mkref MLE MMBIR MMR mnp modelr MOFA MoGene-1_0-st-v1 MOI MongoDB monocle2 monocle3 monoclle3 montana Monterey moog MOPC mothur motIV Mpeg MPH mphil mpileup mps MQTT MRC MRD MRSA msa MSGFgui MSI MSigDB msk MSMC MTB MTHFD1 multi-omic MULTI-seq MUMmer mummerplot MuTect MUTECT2 MXene mydb MYO10 NBAMSeq nc1700sJORDAN NCC NCFamilyGenetic NEB neofart NestJs netbenchmark NetCoMi NetMHCpan netprioR NewStem next-gen Next.js NextDenovo Nextseq 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 OpenMP openSUSE OpenWrt Opto orf 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 Phoenix PHRED phyloseq PI3 PICS PIK3CA PIM Pindel platypus pLentiCRISPRv2 plotMA PLS-DA POLG1 polyphen2 PopGen PopGenome popmax PostgreSQL PowerBI PPI ppinfer PrCa pre-IPO PredicineCARE primer3 PrognomiQ Promethease PROPER-seq ProteoDisco provean PRS 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 QA qctool v2 QIAseq qqplot QUAL 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 relatedness REVIGO RFM RFP RFS RfxCas13d RGBA rGREAT rgsepd 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 RT-qPCR RTMP rtracklayer rTRMui Ruby RUnit RUNX2 rust-bio rvtest SageMath sagenome SAIGE sambamba samblaster SAMD9 samtool SBS SBT 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 SiFive sift Single.mTEC.Transcriptomes SingleR SingscoreAMLMutations sjdbOverhang SKAT-O SKCM SMAD4 smallRNA smallRNAseq SMAP smartPCA SmartSeq SMRT snap snoRNP SnpEff SNPRelate snpsift snRNA-seq snsplot SNV SOAP SOAP Suite SOS SPACA4 SPAdes SpCas9 Spike-Ins SQLite sra-toolkit SRF SRP SRR ssDNA SSL ssODNs ssRNA Stackdriver Stackify Staden Package stats4 Steam STF STP StrainPhlAn stref_00240 StringTie SunTag SV40 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 TP53 tracrRNA traefik TransposonPSI treat_pileup trimAL tRNAdbImport tRNAleu-cox2 TruSeq tSNE TSS Ttc30a ttgsea typescript Ublast UCD UCSF UGENE UH UMAP UMItools Unipept UniProtKB UniRef90 unity3d uORF useMart USU UTP utr UTSW ValueWalk VanillaICE Varscan VCFtools vcfutils VDB VENTUS verilog VG viennaRNA ViPR VirtualBox VMD vodosp.ru VOTCA VP64 VQSR VSCode VTx WASP Waveform WDNA WebRTC WebSockets WebStorm WGBS whg WMD WNN WT1 WW2 WXS xCT XDR xenografts Xfce xFire XGBoost xGEN XML Schema xpehh Xpert XRCC1 XS Y-DNA Yandex Yogscast YPR193C 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Videos

CRISPR Web Analyser

Göknur Giner (WEHI, Australia)

4:30 PM - 4:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

CRISPR/Cas-based genome editing systems have revolutionised the field of genome engineering with applications in a plethora of research fields, including medicine, biology, and biotechnology in only a few years. Here we present a newly developed CRISPR web analyser that offers a few modules to understand the count data obtained from next-generation sequencing (NGS) technology of a pooled CRISPR experiment. These modules include Data Quality Check, Data Preprocessing, and Fitting a Statistical Model.

Moderator: Peter Hickey (WEHI, Australia)
CRISPR Web Analyser
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples

Ramyar Molania (WEHI, Australia)

4:15 PM - 4:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can signifi- cantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

Moderator: Peter Hickey (WEHI, Australia)
Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples
Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver

Ignatius Pang (Childrens Medical Research Institute, Australia)

4:00 PM - 4:15 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Identifying phosphorylation sites and how they change in abundance under different environmental conditions are important for elucidating the role of signal regulations in cellular processes. ProteomeRiver is a novel pipeline that facilitates the analysis of differential abundance of proteins and their phosphorylation events. The pipeline enables the batch analysis of many pairwise treatment versus control comparisons and subsequent pathways overrepresentation analysis. To enable differential abundance analysis of mono- and multi-phosphorylation events, ProteomeRiver incorporates missing values imputation (PhosR*, Kim et al. Cell Rep., 34(8), 108771), removal of unwanted variation (ruv*, Molania et al. 2019 Nucleic Acids Res. 47:6073 - 6083), linear models (limma*, Ritchie et al. 2015 Nucleic Acids Res. 43(7), e47), kinase-substrate enrichment (KinSwingR*), and pathways analysis (clusterProfiler*, Wu et al. The Innovation, 2(3), 100141). This pipeline uses modular components, which allows the modules to be substituted and/or extended with novel tools as they become available. This pipeline also uses a small set of configuration files and scripts to store all instructions necessary for data analysis, which could be shared publicly on code repositories to support reproducibility. The impact of applying remove unwanted variation and normalising the changes in abundance of phosphorylation events by the changes in host protein abundance was demonstrated through the re-analysis of a published dataset of synapses proteome and phosphoproteome during homeostatic up- and down-scaling (Desch et al. 2021 Cell Rep. 36:109583). Compared with the results from the original publication, the use of ProteomeRiver resulted in the identification of novel pathways and upstream kinases associated with homeostatic up- and down-scaling. The pipeline is currently under development and will be available as a R package via https://bitbucket.org/cmri-bioinformatics/proteomeriver. (*) denotes R or Bioconductor packages.

Moderator: Peter Hickey (WEHI, Australia)
Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver
Notes: The audio for the first 6 minutes of this talk was not recorded due to an error with the hybrid meeting. 

Extension of scPipe Bioconductor Package for scATAC-seq Data

Shanika Amarasinghe (ARMI/WEHI, Australia)

3:30 PM - 3:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enables genome-wide profiling of chromatin accessibility that can be used to improve gene expression modelling at single-cell resolution. scATAC-seq data are intrinsically sparse, since the signals detected at a genomic position are limited by DNA copy number, thereby making it difficult to discriminate signals from noise. Therefore, thorough and clear pre-processing steps are needed prior to downstream analyses. Furthermore, these pre-processing steps need to be sufficiently adaptable to user requirements. Currently available scATAC-seq pre-processing tools are either not fully R-based or have limited flexibility. We have extended scPipe, our current pre-processing tool for single-cell RNA data, to be able to process scATAC-seq data from a variety of different protocols. We take the power and flexibility of the R environment and create a workflow that combines multiple Bioconductor packages to achieve this. We also evaluated the performance of scPipe’s scATAC-Seq module on an in-house benchmarking dataset to assess its speed and ability to recover known population structure.

Moderator: Peter Hickey (WEHI, Australia)
Extension of scPipe Bioconductor Package for scATAC-seq Data
Matilda for Single-cell Multi-omics Data Integration

Chunlei Liu (Childrens Medical Research Institute, Australia)

2:55 PM - 3:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
PACKAGE DEMO

Single-cell multimodal omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of single-cell multimodal omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of single-cell multimodal omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular single-cell multimodal omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative single-cell multimodal omics data analysis.

Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
Matilda for Single-cell Multi-omics Data Integration
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data

Melody Jin (WEHI, Australia)

2:15 PM - 2:30 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Single cell RNA-seq is a revolutionary technology that has enabled the discovery of novel cell types and has facilitated deeper insight into normal tissue development. Single cell data is highly noisy, and extensive research focuses on quality control strategies. We develop a package cellXY (https://github.com/phipsonlab/cellXY) with pre-trained machine learning models to qualify the data from the cell sex perspective. We define super genes (superX and superY) to capture the expression pattern of male and female cells. Several models are tuned to predict the sex label for human and mouse cells, including logistic regression, support vector machines, random forest and gradient boosting machine models. The performance on a pooled human PBMC dataset with available ground truth reaches an overall sensitivity of 0.9016 and a precision of 0.9053. Besides, we test the developed model on a public mouse heart dataset without true labels, and successfully predict 37% more cells compared to a simple thresholding method originally adopted by the researchers. 
Additionally, we rely on sex-related features to train models identifying Male/Female doublets with specially generated doublet-singlet training sets. We achieve optimal performances in the training phase, and the identified Male/Female doublets in public datasets exhibit clear evidence based on feature distributions. The package is publically available and can contribute to the quality control process in single-cell data analysis. It can be used in conjunction with the demultiplexing techniques to assist the cell-to-sample allocation. 

Moderator: Peter Hickey (WEHI, Australia) and Anna Quaglieri (Mass Dynamics, Australia)
cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data

Taylor Tian (BGI, Australia)

2:45 PM - 2:55 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
PACKAGE DEMO

Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data
Spectre Toolkit for Rapid Analysis of Cytometry Data

Givanna Putri (WEHI, Australia)

2:00 PM - 2:15 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Cytometry is a technology which simultaneously, rapidly, and economically quantifies the expression of up to 50 proteins of millions of cells at single-cell resolution. It is routinely used in studies to investigate immune system, infectious diseases, and cancer. While cytometry and scRNAseq share few common data analysis steps, computational approaches specific for cytometry data are not as well established as those for scRNAseq data, and that it is non-trivial for scRNAseq tools to process cytometry data due to the sheer volume of the data on hand (millions vs thousands of cells) requiring excessive processing time and computing resources. Importantly, it is often difficult to integrate existing computational tools in an analysis workflow, as they frequently operate on custom complex data formats. Existing toolkits are inflexible in that they also use custom overly complicated data structures and often mandate a strict order of analysis operations, some of which are not required for cytometry data. To address this, we developed Spectre, a flexible R toolkit for rapid analysis of cytometry data. Spectre facilitates the creation of a highly modular analysis workflow by employing data.table (an extension of R’s native data.frame format) for its data management and operations, and by implementing wrapper functions for select essential computational tools to perform data pre-processing, batch alignment, clustering, dimensionality reduction, plotting, and simple statistical analyses, such that they can be easily chained together in any given order in a workflow. Further, Spectre also facilitates cell type annotation label transfer across datasets using classical machine learning classification algorithms. Since its inception, Spectre has been used in various biological studies, including one which studied the immune response to COVID-19 infection. Here, we illustrate the use of Spectre by analysing cytometry data elucidating the immune response to the West Nile virus infection in mice. In summary, Spectre facilitates the analysis of complex high-dimensional cytometry data by enabling seamless integration of computational methods through strategic use of data.table and wrapper functions. Further work is currently underway to develop the infrastructure and wrapper functions to analyse Imaging Mass Cytometry data and to integrate cytometry with CITEseq or AbSeq data. Spectre is freely available on GitHub https://github.com/immunedynamics/spectre. 

Moderator: Anna Quaglieri (Mass Dynamics, Australia)
Spectre Toolkit for Rapid Analysis of Cytometry Data
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis

Ellis Patrick (The University of Sydney, Australia)

1:45 PM - 2:00 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022 
LONG TALK

Understanding the interplay between different types of cells and their immediate environment is critical for understanding the mechanisms of cells themselves and their function in the context of human diseases. Recent advances in high dimensional in situ cytometry technologies have fundamentally revolutionized our ability to observe these complex cellular relationships providing an unprecedented characterisation of cellular heterogeneity in a tissue environment. 

We have developed an analytical framework for analysing data from high dimensional in situ cytometry assays including CODEX, CycIF, IMC and High Definition Spatial Transcriptomics. Implemented in R, this framework makes use of functionality from our Bioconductor packages spicyR, lisaClust, scFeatures, treekoR, FuseSOM, simpleSeg and ClassifyR. We will provide an overview of key steps which are needed to interrogate the comprehensive spatial information generated by these exciting new technologies including cell segmentation, feature normalisation, cell type identification, microenvironment characterisation, spatial hypothesis testing and patient classification. Ultimately, our modular analysis framework provides a cohesive and accessible entry point into spatially resolved single cell data analysis for any R-based bioinformaticians.

Moderator: Anna Quaglieri (Mass Dynamics, Australia)
A Bioconductor Framework for High-dimensional in situ Cytometry Analysis
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