Given a matrix of bin counts, bin gc and N frequency, and filtering parameters, return a boolean matrix flagging ideal bins
Usage
get_ideal_mat(
mat,
gc,
n_freq,
map,
min_reads = 1,
max_N_freq = 0.05,
reads_outlier = 0.01,
gc_outlier = 0.001,
min_map = 0.9,
ncores = 1,
verbose = FALSE
)
add_ideal_mat(
sce,
assay_name = "counts",
gc = rowData(sce)$gc,
n_freq = rowData(sce)$n_freq,
map = rowData(sce)$map,
min_reads = 1,
max_N_freq = 0.05,
reads_outlier = 0.01,
gc_outlier = 0.001,
min_map = 0.9,
ncores = 1,
verbose = FALSE
)Arguments
- mat, sce
A count matrix or SCE object depending on the function
- gc
Vector of gc content
- n_freq
Vector of bin N frequency (proportion of N bases in a bin)
- map
Vector of bin mappability
- min_reads
Minimum number of reads to consider a bin
- max_N_freq
Maximum allowable frequency of N bases to consider a bin. Range (0, 1)
- reads_outlier
Flag bins with reads in the top quantile given by this value. Range (0, 1)
- gc_outlier
Flag bins with GC content in the top and bottom quantule given by this value. Range (0, 1)
- min_map
Minimum allowable mappability score for a bin. Range (0, 1)
- ncores
number of cores for parallel evaluation (requires
pbmcapplypackage)- verbose
message verbosity
- assay_name
Name of assay