predict_pmol {spiky}R Documentation

predict picomoles of DNA from a fit and read counts (coverage)

Description

FIXME: this could be made MUCH faster by precomputing CpG/GC stats per bin

Usage

predict_pmol(fit, ssb_res, bsgenome = NULL, ret = c("gr", "df"), slide = FALSE)

Arguments

fit

result of model_glm_pmol

ssb_res

the data / new data

bsgenome

BSgenome name (if null, will guess from ssb_res)

ret

return a data.frame ("df") or GRanges ("gr")? ("gr")

slide

compute a sliding window estimate for GCfrac (1/3 width)?

Details

Using GRanges as the return value is (perhaps counterintuitively) much faster than the data.frame, since the sequence of the bins gets converted from a BSgenome representation to characters in the latter (it is implied by the bin start, stop, and genome when left as a GRanges).

Value

object with read count, fraglen, GC%, CpG**(1/3), and concentration

Examples


data(ssb_res)
data(spike, package="spiky")
fit <- model_glm_pmol(covg_to_df(ssb_res, spike=spike),spike=spike)
preddf <- predict_pmol(fit, ssb_res, ret="df")
pred <- predict_pmol(fit, ssb_res, ret="gr")
bin_pmol(pred)


[Package spiky version 0.99.999 Index]