Calculate likelihood ratio tests between fitted models and null models.
Which option to use for genes where the lasso model selects
zero interaction terms, i.e. identical to the null model. Options are
The regularized (lasso) fitted models contain an optimal subset of exon:condition interaction terms for each gene, and the "full" fitted models contain all exon:condition interaction terms. The null models contain zero interaction terms, so they are nested within the fitted models.
The likelihood ratio (LR) tests compare the fitted models against the nested null models.
If the regularized (lasso) model contains at least one exon:condition interaction
term, the LR test compares the lasso model against the null model. However, if the
lasso model contains zero interaction terms, then the lasso and null models are
identical, so the LR test cannot be calculated. The
lets the user choose what to do in these cases: either set p-values equal to 1
when_null_selected = "ones"); or calculate a LR test using the "full" model
containing all exon:condition interaction terms (
when_null_selected = "full"),
which reduces power due to the larger number of terms, but allows the evidence for
differential exon usage among these genes to be distinguished. You can also return
NAs for these genes (
when_null_selected = "NA").
The default option is
when_null_selected = "ones". This simply calls all these
genes non-significant, which in most cases is sufficient since we are more interested
in genes with strong evidence for differential exon usage. However, if it is important
to rank the low-evidence genes in your data set, use the
when_null_selected = "ones" or
when_null_selected = "NA", the "full"
fitted models are not required.
Previous step: Fit models with
Next step: Generate summary table of results with
RegspliceResults object containing results of
the LR tests. The results consist of the following entries for each gene:
p_vals: raw p-values
p_adj: multiple testing adjusted p-values (Benjamini-Hochberg false discovery rates, FDR)
LR_stats: likelihood ratio test statistics
df_tests: degrees of freedom of likelihood ratio tests
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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[])) 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_data <- filterZeros(rs_data) rs_data <- filterLowCounts(rs_data) rs_data <- runNormalization(rs_data) rs_data <- runVoom(rs_data) rs_results <- initializeResults(rs_data) rs_results <- fitRegMultiple(rs_results, rs_data) rs_results <- fitNullMultiple(rs_results, rs_data) rs_results <- fitFullMultiple(rs_results, rs_data) rs_results <- LRTests(rs_results)
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