xGR2GeneScoresR Documentation

Function to identify likely modulated seed genes given a list of genomic regions together with the significance level


xGR2GeneScores is supposed to identify likely modulated seed genes from a list of genomic regions (GR) 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".


significance.threshold = 5e-05,
score.cap = 10,
build.conversion = c(NA, "hg38.to.hg19", "hg18.to.hg19"),
distance.max = 50000,
decay.kernel = c("slow", "linear", "rapid", "constant"),
decay.exponent = 2,
GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
scoring.scheme = c("max", "sum", "sequential"),
verbose = T,
RData.location = "http://galahad.well.ox.ac.uk/bigdata",
guid = NULL



a named input vector containing the sinificance level for genomic regions (GR). For this named vector, the element names are GR, in the format of 'chrN:start-end', where N is either 1-22 or X, start (or end) is genomic positional number; for example, 'chr1:13-20'. 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 GR, 2nd column for the significance level.


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 GR into scores. If given, those GR below this are considered significant and thus scored positively. Instead, those above this are considered insignificant and thus receive no score


the maximum score being capped. By default, it is set to 10. If NULL, no capping is applied


the conversion from one genome build to another. The conversions supported are "hg38.to.hg19" and "hg18.to.hg19". By default it is NA (no need to do so)


the maximum distance between genes and GR. 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 GR per gene


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'


a numeric specifying a decay exponent. By default, it sets to 2


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


the method used to calculate seed gene scores under a set of GR. 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: \sum_{i=1}{\frac{R_{i}}{i}}, where R_{i} is the i^{th} rank (in a descreasing order)


logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display


the characters to tell the location of built-in RData files. See xRDataLoader for details


a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details


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


This function uses xGRscores and xGR2nGenes to define and score nearby genes that are located within distance window of input genomic regions.

See Also

xGRscores, xGR2nGenes, xSparseMatrix


## Not run: 
# Load the XGR package and specify the location of built-in data
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"

# a) provide the seed SNPs with the significance info
## load ImmunoBase
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
## get lead SNPs reported in AS GWAS and their significance info (p-values)
gr <- ImmunoBase$AS$variant
df <- as.data.frame(gr, row.names=NULL)
chr <- df$seqnames
start <- df$start
end <- df$end
sig <- df$Pvalue
GR <- paste(chr,':',start,'-',end, sep='')
data <- cbind(GR=GR, Sig=sig)

# b) define and score seed geens
mSeed <- xGR2GeneScores(data=data, RData.location=RData.location)

## End(Not run)