filter_coverage: Filter data
In mrc-ide/mipmapper: Filtering and analysis of MIP panel genetic datasets
Description
Usage
Arguments
Details
Examples
Description
Filter data based on coverage
Usage
1
filter_coverage(dat, min_coverage = 2)
Arguments
dat
MIP data. The data must have the following variables:
Coverage : The total read coverage as numerics
min_coverage
Numeric for the minimum required coverage. Default = 2
Details
The MIP dataset is filtered to remove all loci that have less than
the minimum required coverage.
Examples
1
2
3
4
dat <- dummy_data()
dat <- filter_misc(dat)
dat2 <- filter_coverage(dat, min_coverage = 5)
dim(dat2)[1] < dim(dat)[1]
mrc-ide/mipmapper documentation built on May 20, 2019, 3:27 p.m.
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Description Usage Arguments Details Examples
Description
Filter data based on coverage
Usage
1 | filter_coverage(dat, min_coverage = 2)
|
Arguments
dat |
MIP data. The data must have the following variables:
|
min_coverage |
Numeric for the minimum required coverage. Default = 2 |
Details
The MIP dataset is filtered to remove all loci that have less than the minimum required coverage.
Examples
1 2 3 4 | dat <- dummy_data()
dat <- filter_misc(dat)
dat2 <- filter_coverage(dat, min_coverage = 5)
dim(dat2)[1] < dim(dat)[1]
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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