Description Usage Arguments Details Value See Also Examples
View source: R/regsplice_wrapper.R
Wrapper function to run a regsplice
analysis with a single command.
1 2 3 4 5 
rs_data 

filter_zeros 
Whether to filter zerocount exon bins, using

filter_low_counts 
Whether to filter lowcount exon bins, using

filter_min_per_exon 
Filtering parameter for lowcount exon bins: minimum number
of reads per exon bin, summed across all biological samples. Default is 6. See

filter_min_per_sample 
Filtering parameter for lowcount exon bins: minimum
number of reads per biological sample; i.e. for each exon bin, at least one sample
must have this number of reads. Default is 3. See 
normalize 
Whether to calculate normalization factors, using

norm_method 
Normalization method to use. Options are 
voom 
Whether to calculate 
alpha 
Elastic net parameter 
lambda_choice 
Parameter to select which optimal 
when_null_selected 
Which option to use for genes where the lasso model selects
zero interaction terms, i.e. identical to the null model. Options are 
seed 
Random seed (integer). Default is NULL. Provide an integer value to set the random seed for reproducible results. 
... 
Other arguments to pass to 
This wrapper function runs the regsplice
analysis pipeline with a single
command.
The required input format is a RegspliceData
object, which is created with the
RegspliceData
constructor function.
The wrapper function calls each of the individual functions in the analysis pipeline in sequence. You can also run the individual functions directly, which provides additional flexibility and insight into the statistical methodology. See the vignette for a description of the individual functions and an example workflow.
After running the analysis pipeline, a summary table of the results can be displayed
with summaryTable
.
Note that when using exon microarray data, the filtering, normalization, and
voom
steps should be disabled with the respective arguments.
See RegspliceData
for details on constructing the input data
object; filterZeros
and filterLowCounts
for details about
filtering; runNormalization
and runVoom
for details about
calculation of normalization factors and voom
transformation and weights;
createDesignMatrix
for details about the model design matrices;
fitRegMultiple
, fitNullMultiple
, or
fitFullMultiple
for details about the model fitting functions; and
LRTests
for details about the likelihood ratio tests.
Returns a RegspliceResults
object containing fitted model
results and likelihood ratio (LR) test results. The LR test results consist of the
following entries for each gene:
p_vals: raw pvalues
p_adj: multiple testing adjusted pvalues (BenjaminiHochberg false discovery rates, FDR)
LR_stats: likelihood ratio test statistics
df_tests: degrees of freedom of likelihood ratio tests
RegspliceData
RegspliceResults
initializeResults
filterZeros
filterLowCounts
runNormalization
runVoom
createDesignMatrix
fitRegMultiple
fitNullMultiple
fitFullMultiple
LRTests
summaryTable
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  file_counts < system.file("extdata/vignette_counts.txt", package = "regsplice")
data < read.table(file_counts, header = TRUE, sep = "\t", stringsAsFactors = FALSE)
head(data)
counts < data[, 2:7]
tbl_exons < table(sapply(strsplit(data$exon, ":"), function(s) s[[1]]))
gene_IDs < names(tbl_exons)
n_exons < unname(tbl_exons)
condition < rep(c("untreated", "treated"), each = 3)
rs_data < RegspliceData(counts, gene_IDs, n_exons, condition)
rs_results < regsplice(rs_data)
summaryTable(rs_results)

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