Description Usage Arguments Details Value See Also Examples
Mark RNA editing sites in contiguous and co-edited region by
selecting sites for which r_drop
values calculated from inner
function CreateRdrop
is greater than rDropThresh_num
.
1 2 3 4 5 6 7 | MarkCoeditedSites(
rnaEditCluster_mat,
rDropThresh_num = 0.4,
method = c("spearman", "pearson"),
minEditFreq = 0.05,
verbose = TRUE
)
|
rnaEditCluster_mat |
A matrix of RNA editing level values on individual sites, with row names as sample IDs and column names as site IDs in the form of "chrAA:XXXXXXXX". |
rDropThresh_num |
Threshold for minimum correlation between RNA editing levels of one site and the mean RNA editing levels of the rest of the sites. Please set a number between 0 and 1. Defaults to 0.4. |
method |
Method for computing correlation. Defaults to
|
minEditFreq |
Threshold for minimum percentage of edited samples for a
given site. The |
verbose |
Should messages and warnings be displayed? Defaults to TRUE. |
r_drop
statistic is used to identify co-edited sites. An
outlier site (keep = 0
) in a genomic region typically has low
correlation with the rest of the sites in a genomic region. The sites with
r_drop
value greater than rDropThresh_num
are marked to have
keep = 1
. Please see CreateRdrop
for more details.
A data frame with the following columns:
site
: site ID.
r_drop
: The correlation between RNA editing levels of
one site and the mean RNA editing levels of the rest of the sites.
keep
: indicator for co-edited sites, The sites with
keep = 1
belong to the contiguous and co-edited region.
keep_contiguous
: contiguous co-edited region number
site
: site ID.
keep
: indicator for co-edited sites, The sites with
keep = 1
belong to the contiguous and co-edited region.
ind
: index for the sites.
r_drop
: the correlation between RNA editing levels of
one site and the mean RNA editing levels of the rest of the sites.
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(t_rnaedit_df)
ordered_cols <- OrderSitesByLocation(
sites_char = colnames(t_rnaedit_df),
output = "vector"
)
exm_data <- t_rnaedit_df[, ordered_cols]
MarkCoeditedSites(
rnaEditCluster_mat = exm_data,
method = "spearman"
)
|
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