GSR: Editing - LSH-GAN Simulator

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LSH-GAN
LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data
A fundamental problem of downstream analysis of scRNA-seq data is the unavailability of enough cell samples compare to the feature size. This is mostly due to the budgetary constraint of single cell experiments or simply because of the small number of available patient samples. Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN to address this issue by producing new realistic cell samples. We update the training procedure of the generator of GAN using locality sensitive hashing which speeds up the sample generation, thus maintains the feasibility of applying the standard procedures of downstream analysis. LSH-GAN outperforms the benchmarks for realistic generation of quality cell samples. Experimental results show that generated samples of LSH-GAN improves the performance of the downstream analysis such as feature (gene) selection and cell clustering. Overall, LSH-GAN therefore addressed the key challenges of small sample scRNA-seq data analysis.
single-cell RNA sequencing, scRNA-seq, HDSS data, Machine learning, Computational models
1.0.0
Feb. 27, 2021
Jan. 25, 2022
https://github.com/Snehalikalall/LSH-GAN

Attribute Tree Control

Step 1: Use the attribute tree to add new attributes or remove pre-selected attributes to describe the simulator.
Every sub-attribute is selected
Not all sub-attributes are selected
  • Target
    • Type of Simulated Data
      • Genotype at Genetic Markers
      • Diploid DNA Sequence
      • Haploid DNA Sequence
      • RNA
      • Gene Expression
      • Sex Chromosomes
      • Mitochondrial DNA
      • Protein Sequence
      • Sequencing Reads
      • Phenotype
      • Single-Cell Sequencing
      • Bulk Sequencing
      • Proteomics
      • Chromatin Conformation
    • Variations
      • Biallelic Marker
      • Multiallelic Marker
      • Single Nucleotide Variation
      • Amino acid variation
      • Microsatellite
      • Insertion and Deletion
      • CNV
      • Inversion and Rearrangement
      • Alternative Splicing
      • Missing Genotypes
      • Genotype or Sequencing Error
      • Ionization
      • Other
  • Simulation Method
    • Standard Coalescent
    • Exact Coalescent
    • Machine Learning
    • Forward-time
    • Resample Existing Data
    • Phylogenetic
    • Gene dropping
    • Neural network
    • Other
  • Input
    • Data Type
      • Allele Frequencies
      • Empirical
      • Ancestral Sequence
      • Saved simulation
      • Reference genome
      • Other
    • File format
      • Arlequin
      • CREATE
      • Fstat
      • GDA
      • Genepop
      • MIGRATE
      • MS
      • SAM or BAM
      • NEXUS
      • Phylip
      • STRUCTURE
      • XML
      • Tree Sequence
      • Program Specific
      • Other
  • Output
    • Data Type
      • Genotype or Sequence
      • Phenotypic Trait
      • Individual Relationship
      • Phylogenetic Tree
      • Demographic
      • Mutation
      • Methylation
      • Gene Expression
      • Protein Expression
      • Linkage Disequilibrium
      • Diversity Measures
      • Fitness
      • Sequencing Reads
        • Illumina
        • Roche 454
        • SOLiD
        • IonTorrent
        • PacBio
        • Nanopore
        • Other
      • Other
    • File Format
      • Arlequin
      • Fasta or Fastq
      • Fstat
      • Genepop
      • Linkage
      • MIGRATE
      • MS
      • PED
      • Phylip
      • NEXUS
      • STRUCTURE
      • VCF
      • SAM or BAM
      • Tree Sequence
      • Program Specific
      • Other
    • Sample Type
      • Random or Independent
      • Sibpairs, Trios and Nuclear Families
      • Extended or Complete Pedigrees
      • Case-control
      • Longitudinal
      • Other
  • Phenotype
    • Trait Type
      • Binary or Qualitative
      • Quantitative
      • Multiple
    • Determinants
      • Single Genetic Marker
      • Multiple Genetic Markers
      • Sex-linked
      • Gene-Gene Interaction
      • Environmental Factors
      • Gene-Environment Interaction
  • Evolutionary Features
    • Demographic
      • Population Size Changes
        • Constant Size
        • Exponential Growth or Decline
        • Logistic Growth
        • Bottleneck
        • Carrying Capacity
        • User Defined
      • Gene Flow
        • Stepping Stone Models
        • Island Models
        • Continent-Island Models
        • Sex or Age-Specific Migration Rates
        • Influenced by Environmental Factors
        • Admixed Population
        • User-defined Matrix
        • Other
      • Spatiality
        • Discrete Models
        • Continuous Models
        • Landscape Factors
    • Life Cycle
      • Discrete Generation Model
      • Age structured
      • Overlapping Generation
      • User-Defined transition matrices
    • Mating System
      • Random Mating
      • Monogamous
      • Polygamous
      • Haplodiploid
      • Selfing
      • Age- or Stage-Specific
      • Assortative or Disassortative
      • Other
    • Fecundity
      • Constant Number
      • Randomly Distributed
      • Individually Determined
      • Influenced by Environment
      • Other
    • Natural Selection
      • Determinant
        • Single-locus
        • Multi-locus
        • Codon-based
        • Fitness of Offspring
        • Phenotypic Trait
        • Environmental Factors
      • Models
        • Directional Selection
        • Balancing Selection
        • Multi-locus models
        • Epistasis
        • Random Fitness Effects
        • Disruptive
        • Phenotype Threshold
        • Frequency-Dependent
        • Other
    • Recombination
      • Uniform
      • Varying Recombination Rates
      • Gene Conversion Allowed
    • Mutation Models
      • Two-allele Mutation Model
      • Markov DNA Evolution Models
      • k-Allele Model
      • Infinite-allele Model
      • Infinite-sites Model
      • Stepwise Mutation Model
      • Codon and Amino Acid Models
      • Indels and Others
      • Heterogeneity among Sites
      • Others
    • Events Allowed
      • Population Merge and Split
      • Varying Demographic Features
      • Population Events
      • Varying Genetic Features
      • Change of Mating Systems
      • Other
    • Other
      • Phenogenetic
      • Polygenic background
  • Interface
    • Command-line
    • Graphical User Interface
    • Integrated Development Environment
    • Script-based
    • Web-based
  • Development
    • Tested Platforms
      • Windows
      • Mac OS X
      • Linux and Unix
      • Solaris
      • Others
    • Language
      • C or C++
      • Java
      • R
      • Python
      • Perl
      • Visual Basic
      • Other
    • License
      • GNU Public License
      • BSD
      • Creative Commons
      • MIT
      • Other
  • GSR Certification
    • Accessibility
    • Documentation
    • Application
    • Support

Summary of Proposed Changes

Step 2: Review list of proposed attribute addition(s) and subtraction(s).

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      Summary of Proposed Changes

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      Current Citations/Applications

      [Pubmed ID: 35688990], Lall S, Ray S, Bandyopadhyay S, LSH-GAN enables in-silico generation of cells for small sample high dimensional scRNA-seq data., Commun Biol, 06-10-2022, https://www.ncbi.nlm.nih.gov/pubmed/?term=35688990,Primary Citation
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