xSNP2GeneScores: Function to identify likely modulated seed genes given a list...

In XGR: Exploring Genomic Relations for Enhanced Interpretation Through Enrichment, Similarity, Network and Annotation Analysis

Description

xSNP2GeneScores is supposed to identify likely modulated seed genes from a list of SNPs together with the significance level (measured as p-values or fdr). To do so, it defines seed genes and their scores that take into account the distance to and the significance of input SNPs. It returns an object of class "mSeed".

Usage

xSNP2GeneScores(
data,
include.LD = NA,
LD.customised = NULL,
LD.r2 = 0.8,
significance.threshold = 5e-05,
score.cap = 10,
distance.max = 50000,
decay.kernel = c("slow", "linear", "rapid", "constant"),
decay.exponent = 2,
GR.SNP = c("dbSNP_GWAS", "dbSNP_Common", "dbSNP_Single"),
GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
include.TAD = c("none", "GM12878", "IMR90", "MSC", "TRO", "H1", "MES",
"NPC"),
scoring.scheme = c("max", "sum", "sequential"),
verbose = T,
RData.location = "http://galahad.well.ox.ac.uk/bigdata",
guid = NULL
)

Arguments

data	a named input vector containing the sinificance level for nodes (dbSNP). For this named vector, the element names are dbSNP ID (or in the format such as 'chr16:28525386'), the element values for the significance level (measured as p-value or fdr). Alternatively, it can be a matrix or data frame with two columns: 1st column for dbSNP, 2nd column for the significance level
include.LD	additional SNPs in LD with Lead SNPs are also included. By default, it is 'NA' to disable this option. Otherwise, LD SNPs will be included based on one or more of 26 populations and 5 super populations from 1000 Genomics Project data (phase 3). The population can be one of 5 super populations ("AFR", "AMR", "EAS", "EUR", "SAS"), or one of 26 populations ("ACB", "ASW", "BEB", "CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH", "GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL", "PJL", "PUR", "STU", "TSI", "YRI"). Explanations for population code can be found at http://www.1000genomes.org/faq/which-populations-are-part-your-study
LD.customised	a user-input matrix or data frame with 3 columns: 1st column for Lead SNPs, 2nd column for LD SNPs, and 3rd for LD r2 value. It is designed to allow the user analysing their precalcuated LD info. This customisation (if provided) has the high priority over built-in LD SNPs
LD.r2	the LD r2 value. By default, it is 0.8, meaning that SNPs in LD (r2>=0.8) with input SNPs will be considered as LD SNPs. It can be any value from 0.8 to 1
significance.threshold	the given significance threshold. By default, it is set to NULL, meaning there is no constraint on the significance level when transforming the significance level of SNPs into scores. If given, those SNPs below this are considered significant and thus scored positively. Instead, those above this are considered insigificant and thus receive no score
score.cap	the maximum score being capped. By default, it is set to 10. If NULL, no capping is applied
distance.max	the maximum distance between genes and SNPs. Only those genes no far way from this distance will be considered as seed genes. This parameter will influence the distance-component weights calculated for nearby SNPs per gene
decay.kernel	a character specifying a decay kernel function. It can be one of 'slow' for slow decay, 'linear' for linear decay, and 'rapid' for rapid decay. If no distance weight is used, please select 'constant'
decay.exponent	a numeric specifying a decay exponent. By default, it sets to 2
GR.SNP	the genomic regions of SNPs. By default, it is 'dbSNP_GWAS', that is, SNPs from dbSNP (version 146) restricted to GWAS SNPs and their LD SNPs (hg19). It can be 'dbSNP_Common', that is, Common SNPs from dbSNP (version 146) plus GWAS SNPs and their LD SNPs (hg19). Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to dbSNP IDs. Then, tell "GR.SNP" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
GR.Gene	the genomic regions of genes. By default, it is 'UCSC_knownGene', that is, UCSC known genes (together with genomic locations) based on human genome assembly hg19. It can be 'UCSC_knownCanonical', that is, UCSC known canonical genes (together with genomic locations) based on human genome assembly hg19. Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to Gene Symbols. Then, tell "GR.Gene" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
include.TAD	TAD boundary regions are also included. By default, it is 'NA' to disable this option. Otherwise, inclusion of a TAD dataset to pre-filter SNP-nGene pairs (i.e. only those within a TAD region will be kept). TAD datasets can be one of "GM12878" (lymphoblast), "IMR90" (fibroblast), "MSC" (mesenchymal stem cell) ,"TRO" (trophoblasts-like cell), "H1" (embryonic stem cell), "MES" (mesendoderm) and "NPC" (neural progenitor cell). Explanations can be found at http://dx.doi.org/10.1016/j.celrep.2016.10.061
scoring.scheme	the method used to calculate seed gene scores under a set of SNPs. It can be one of "sum" for adding up, "max" for the maximum, and "sequential" for the sequential weighting. The sequential weighting is done via: ∑_{i=1}{\frac{R_{i}}{i}}, where R_{i} is the i^{th} rank (in a descreasing order)
verbose	logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
RData.location	the characters to tell the location of built-in RData files. See xRDataLoader for details
guid	a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details

Value

an object of class "mSeed", a list with following components:

• SNP: a matrix of nSNP X 4 containing SNP information, where nSNP is the number of SNPs, and the 3 columns are "SNP" (Lead and/or LD SNPs), "Score" (the scores for SNPs calculated based on p-values taking into account the given threshold of the significant level), "Pval" (the input p-values for Lead SNPs or R2-adjusted p-values for LD SNPs), "Flag" (indicating as Lead or LD SNPs)

• Gene: a matrix of nGene X 3 containing Gene information, where nGene is the number of seed genes, and the 3 columns are "Gene" (gene symbol), "Score" (the scores for seed genes), "Pval" (pvalue-like significance level transformed from gene scores)

• call: the call that produced this result

Note

This function uses xSNPscores and xSNP2nGenes to define and score nearby genes that are located within distance window of input and/or LD SNPs.

See Also

xSNPscores, xSNP2nGenes, xSparseMatrix

Examples

## Not run: 
# Load the XGR package and specify the location of built-in data
library(XGR)

## End(Not run)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"

## Not run: 
# a) provide the seed SNPs with the significance info
## load ImmunoBase
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
RData.location=RData.location)
## get lead SNPs reported in AS GWAS and their significance info (p-values)
gr <- ImmunoBase$AS$variant
data <- GenomicRanges::mcols(gr)[,c(1,3)]

# b) define and score seed geens
mSeed <- xSNP2GeneScores(data=data, include.TAD="GM12878",
RData.location=RData.location)

# c) extract SNP info
head(mSeed$SNP)

# d) extract gene info
head(mSeed$Gene)

## End(Not run)

XGR documentation built on Jan. 8, 2020, 5:06 p.m.