[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Lytal et al., 2020 - Google Patents

Normalization methods on single-cell RNA-seq data: an empirical survey

Lytal et al., 2020

View HTML
Document ID
12816633040900060641
Author
Lytal N
Ran D
An L
Publication year
Publication venue
Frontiers in genetics

External Links

Snippet

Data normalization is vital to single-cell sequencing, addressing limitations presented by low input material and various forms of bias or noise present in the sequencing process. Several such normalization methods exist, some of which rely on spike-in genes, molecules added …
Continue reading at www.frontiersin.org (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/22—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or SNP [Single-Nucleotide Polymorphism] discovery or sequence alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/20—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting

Similar Documents

Publication Publication Date Title
Lytal et al. Normalization methods on single-cell RNA-seq data: an empirical survey
Freytag et al. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
Poirion et al. Single-cell transcriptomics bioinformatics and computational challenges
Pearman et al. Testing the advantages and disadvantages of short-and long-read eukaryotic metagenomics using simulated reads
Krzak et al. Benchmark and parameter sensitivity analysis of single-cell RNA sequencing clustering methods
Lamarre et al. Optimization of an RNA-Seq differential gene expression analysis depending on biological replicate number and library size
Dal Molin et al. Single-cell RNA-sequencing: assessment of differential expression analysis methods
Wu et al. PROPER: comprehensive power evaluation for differential expression using RNA-seq
Marco et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
Seita et al. Gene Expression Commons: an open platform for absolute gene expression profiling
Wei et al. Comparison of methods for picking the operational taxonomic units from amplicon sequences
Ha et al. Performance evaluation of SpliceAI for the prediction of splicing of NF1 variants
Schwabe et al. Contributions of cell growth and biochemical reactions to nongenetic variability of cells
Petereit et al. petal: Co-expression network modelling in R
Michaelson et al. Data-driven assessment of eQTL mapping methods
Iqbal et al. Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data
Talevich et al. CNVkit-RNA: copy number inference from RNA-sequencing data
Zhu et al. Sc-gpe: a graph partitioning-based cluster ensemble method for single-cell
Song et al. Systematic comparisons for composition profiles, taxonomic levels, and machine learning methods for microbiome-based disease prediction
Mu et al. Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets
Alexander et al. Capturing discrete latent structures: choose LDs over PCs
Klemm et al. Proteinortho6: pseudo-reciprocal best alignment heuristic for graph-based detection of (co-) orthologs
Zhou et al. Transformation and differential abundance analysis of microbiome data incorporating phylogeny
Kang et al. Reprocessing 16S rRNA gene amplicon sequencing studies:(Meta) data issues, robustness, and reproducibility
Rahmani et al. Recursive indirect-paths modularity (RIP-M) for detecting community structure in RNA-Seq co-expression networks