SNPsea
  • Introduction
  • Visual Summary
    • Cartoon
    • Flow Chart
  • Algorithm Details
    • Step 1: Assigning genes to each SNP
      • Two score options
    • Step 2: Calculating specificity scores
      • Specificity for a matrix of continuous values
      • Locus scores for a matrix of continuous values
      • ’--score single’ (default)
      • ’--score total’
      • Locus scores for a matrix of binary values
      • Condition specificity scores
    • Step 3: Testing significance
      • Analytical p-values
      • Permutation p-values
    • Example
  • Installation
    • C++ Libraries
    • Python Packages
    • R Packages
  • Data
    • SNP sets
      • Celiac_disease-Trynka2011-35_SNPs.gwas
      • HDL_cholesterol-Teslovich2010-46_SNPs.gwas
      • Multiple_sclerosis-IMSGC-51_SNPs.gwas
      • Red_blood_cell_count-Harst2012-45_SNPs.gwas
    • Gene matrices
      • GeneAtlas2004.gct.gz
      • GO2013.gct.gz
      • ImmGen2012.gct.gz
      • FANTOM2014.gct.gz
    • LD-pruned SNPs and Genomic Intervals
      • Lango2010.txt.gz
      • NCBIgenes2013.bed.gz
      • TGP2011.bed.gz
  • Usage
    • Options
      • Required
      • Optional
    • Input File Formats
      • --snps ARG
      • --gene-matrix ARG
      • --condition ARG (Optional)
      • --gene-intervals ARG
      • --snp-intervals ARG
      • --null-snps ARG
    • Output Files
      • args.txt
      • condition_pvalues.txt
      • null_pvalues.txt
      • snp_genes.txt
      • snp_condition_scores.txt
  • Output Visualizations
    • View enrichment of tissue-specific gene expression
    • View the most specifically expressed gene for each SNP-tissue pair
    • View the type 1 error rate estimates for each tissue
  • Supplementary Figures
    • Supplementary Figure 1: Determining SNP linkage intervals
    • Supplementary Figure 2: Counting genes in GWAS SNP linkage intervals
    • Supplementary Figure 3: Choosing the \(r^{2}\) threshold for linkage intervals
    • Supplementary Figure 4: Each trait-associated locus harbors a single associated gene
    • Supplementary Figure 5: Type 1 error estimates
      • Additional Examples
    • Supplementary Figure 6: Red blood cell count GO enrichment
    • Supplementary Figure 7: Multiple sclerosis
    • Supplementary Figure 8: Celiac disease
    • Supplementary Figure 9: HDL cholesterol
  • References
 
SNPsea
  • Docs »
  • SNPsea Manual
  • Edit on GitHub

SNPsea Manual¶

Github project: https://github.com/slowkow/snpsea

  • Introduction
  • Visual Summary
    • Cartoon
    • Flow Chart
  • Algorithm Details
    • Step 1: Assigning genes to each SNP
    • Step 2: Calculating specificity scores
    • Step 3: Testing significance
    • Example
  • Installation
    • C++ Libraries
    • Python Packages
    • R Packages
  • Data
    • SNP sets
    • Gene matrices
    • LD-pruned SNPs and Genomic Intervals
  • Usage
    • Options
    • Input File Formats
    • Output Files
  • Output Visualizations
    • View enrichment of tissue-specific gene expression
    • View the most specifically expressed gene for each SNP-tissue pair
    • View the type 1 error rate estimates for each tissue
  • Supplementary Figures
    • Supplementary Figure 1: Determining SNP linkage intervals
    • Supplementary Figure 2: Counting genes in GWAS SNP linkage intervals
    • Supplementary Figure 3: Choosing the \(r^{2}\) threshold for linkage intervals
    • Supplementary Figure 4: Each trait-associated locus harbors a single associated gene
    • Supplementary Figure 5: Type 1 error estimates
    • Supplementary Figure 6: Red blood cell count GO enrichment
    • Supplementary Figure 7: Multiple sclerosis
    • Supplementary Figure 8: Celiac disease
    • Supplementary Figure 9: HDL cholesterol
  • References
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© Copyright 2014, Kamil Slowikowski.

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