Description Usage Arguments Value Note See Also Examples
xCorrelation
is supposed to calculate and visualise correlation
between a data frame and a named vector (or a list of named vectors).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
df |
a data frame with two columns ('name' and 'value') |
list_vec |
a named vector containing numeric values. Alternatively it can be a list of named vectors |
method |
the method used to calcualte correlation. It can be 'pearson' for Pearson's correlation or 'spearman' for Spearman rank correlation |
p.type |
the type of the p-value calcualted. It can be 'nominal' for nominal p-value or 'empirical' for empirical p-value |
seed |
an integer specifying the seed |
nperm |
the number of random permutations |
p.adjust.method |
the method used to adjust p-values. It can be one of "BH", "BY", "bonferroni", "holm", "hochberg" and "hommel". The first two methods "BH" (widely used) and "BY" control the false discovery rate (FDR: the expected proportion of false discoveries amongst the rejected hypotheses); the last four methods "bonferroni", "holm", "hochberg" and "hommel" are designed to give strong control of the family-wise error rate (FWER). Notes: FDR is a less stringent condition than FWER |
plot |
logical to indicate whether scatter plot is drawn |
plot.smooth |
the smooth method for the scatter plot. It can be NA (depending on correlation type), "lm" for the linear line or 'loess' for the loess curve |
point.size |
the point size |
point.color |
the point color |
a list with three componets:
df_summary
: a data frame of n x 5, where n is the number
of named vectors, and the 5 columns are "name", "num" (i.e. number of
data points used for calculation), "cor" (i.e. correlation), "pval"
(i.e. p-value), "fdr"
ls_gp_curve
: NULL if the plot is not drawn; otherwise, a
list of 'ggplot' objects for scatter plot together with an estimated
curve
ls_gp_pdf
: NULL if the plot is not drawn; otherwise, a
list of 'ggplot' objects for pdf plot for null distribution of
correlation together with a vertical line for observed correlation
none
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
## Not run:
# a) provide the seed nodes/genes with the weight info
## load ImmunoBase
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
RData.location=RData.location)
## get genes within 500kb away from AS GWAS lead SNPs
seeds.genes <- ImmunoBase$AS$genes_variants
## seeds weighted according to distance away from lead SNPs
data <- 1- seeds.genes/500000
# b) prepare a data frame
df <- data.frame(name=names(data), value=data, stringsAsFactors=FALSE)
# c) do correlation
ls_res <- xCorrelation(df, data, method="pearson", p.type="empirical",
nperm=2000, plot=TRUE)
## End(Not run)
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