swinsor {RNAprobR} | R Documentation |
Performs sliding window Winsorization given treated GRanges generated by comp() function. It winsorizes values in windows (of a size specified by window_size) sliding by 1 nt over whole transcript length and reports mean winsorized value for each nucleotide (as well as standard deviation).
swinsor(Comp_GR, winsor_level = 0.9, window_size = 71, only_top = FALSE, nt_offset = 1, add_to)
Comp_GR |
GRanges object made by comp() function. |
winsor_level |
Winsorization level. Bottom outliers will be set to (1-winsor_level)/2 quantile and top outliers to (1+winsor_level)/2 quantile. |
window_size |
Size of a sliding window. |
only_top |
If TRUE then bottom values are not Winsorized and are set to 0. |
nt_offset |
How many position in the 5' direction should the signal be offset to account for the fact that reverse transcription termination occurs before site of modification. |
add_to |
GRanges object made by other normalization function (dtcr(), slograt(), swinsor(), compdata()) to which normalized values should be added. |
GRanges object with "swinsor" (mean smooth-Winsor values) and "swinsor.sd" (standard deviation of smooth-Winsor values) metadata.
Lukasz Jan Kielpinski, Jeppe Vinther, Nikos Sidiropoulos
"Analysis of sequencing based RNA structure probing data" Kielpinski, Sidiropoulos, Vinther. Chapter in "Methods in Enzymology" (in preparation)
comp
, dtcr
, slograt
,
compdata
, GR2norm_df
,
plotRNA
, norm2bedgraph
, winsor
,
swinsor_vector
dummy_euc_GR <- GRanges(seqnames="DummyRNA", IRanges(start=round(runif(100)*100), width=round(runif(100)*100+1)), strand="+", EUC=round(runif(100)*100)) dummy_comp_GR <- comp(dummy_euc_GR) swinsor(dummy_comp_GR)