snpScreen {GGtools} | R Documentation |
compute model fits over a sequence of SNPs
snpScreen(racExSet, snpMeta, gene, formTemplate, fitter, gran, ...) extract_p(ssr) plot_mlp(ssr, snpMeta, gchr=NULL, geneLocDF=NULL, ps = NULL, pch = 20, cex = 0.5, local=FALSE, plotf=smoothScatter, organism="human")
racExSet |
instance of racExSet-class |
snpMeta |
instance of snpMeta-class |
gene |
instance of genesym-class class identifying
the expression phenotype to be regarded as dependent variable |
gchr |
string stating chromosome on which gene is resident; looked up in geneLocDF if not supplied |
geneLocDF |
data frame that has one row for each gene symbol, in column `gene', and the chromosome on which it lives, in column `chr' |
formTemplate |
a formula having form ~. or
~factor(.) , literally, to specify additive or
nonadditive models for effects of rare allele copy number |
fitter |
R fitting function that can work with formulas
and data frames, for example lm , or, if not a
fitting function, a special function in the package, either
fastHET , or fastAGM , see details below |
gran |
a numeric convenience parameter; if not 1, SNPs will
be deterministically selected with frequency 1/gran along the
linear sequence in the racExSet racAssay structure |
... |
... – not in use |
ssr |
snpScreenResult instance |
ps |
a string with a probe set identifier |
pch |
for passage to scatterSmooth |
cex |
for passage to scatterSmooth |
local |
by default (local==FALSE) the plot is made over the entire chromosome; if local==TRUE, the plot is made over the segment of the chromosome within which the snps screened lie; this only makes a difference if the racExSet used in the screen has been SNP-filtered relative to the HapMap SNP set. |
plotf |
function to use for rendering – with really
high-density genotyping, the default works well; in sparse cases,
use plot . |
organism |
string used for plot annotation |
for snpScreen:
If options()$verbose == TRUE
then every 100th index in the
vector of snps is printed to stdout to show rate of progress.
snpScreenResult
is a container for relevant information
about a screen, including a list of fit objects.
For result processing, many SNPs have no variation in observed samples and statistical tests of association are indeterminate. NAs will be returned for tests on such SNPs.
fastAGM
is a C routine for simple least square fitting
of an additive genetic model. fastHET
will compare heterozygous
to homozygous.
plot_mlp
returns a list of x and y values for the
plotted points.
creates a list of model fit results; try
is used to
allow failure of fit (e.g., lm
may fail if a singular
model matrix is computed
Vince Carey <stvjc@channing.harvard.edu>
example(make_racExSet) dem dem = exclMono(dem) snpNames(dem)[1:4] featureNames(dem)[1:4] data(chr20GGdem) data(chr20meta) data(geneLocs_hsa) scr1 = snpScreen(dem, chr20meta, genesym("DDR1"), ~., lm, gran=100 ) scr1[[1]] scr2 = snpScreen(dem, chr20meta, genesym("DDR1"), ~factor(.), lm, gran=200 ) scr2[[1]] plot_mlp(scr1, chr20meta, geneLocDF=geneLocs_hsa) chr20GGdem = exclMono(chr20GGdem) ut = unix.time(scr2 <- snpScreen(chr20GGdem, chr20meta, genesym("CPNE1"), ~factor(.), fastAGM, 50)) ut scr2 plot_mlp(scr2, chr20meta, geneLocDF=geneLocs_hsa) # # here we work on a WebQTL computation # # get the expr+genotype data data(gse2031GG) # get a provisional snp metadata structure data(INB34snpMeta) # run a screen for Erdr1 ss = snpScreen(gse2031GG, INB34snpMeta, genesym("Erdr1"), ~., fastAGM, 1) plot_mlp(ss, INB34snpMeta, gchr="all", plotf=plot, organism="mouse" )