## ------------------------------------------------------------------------ library(transite) ## ---- message=FALSE------------------------------------------------------ background.df <- transite:::ge$background ## ---- message=FALSE------------------------------------------------------ background.df <- dplyr::arrange(background.df, value) ## ---- message=FALSE------------------------------------------------------ background.set <- gsub("T", "U", background.df$seq) ## ---- message=FALSE------------------------------------------------------ names(background.set) <- paste0(background.df$refseq, "|", background.df$seq.type) ## ---- message=FALSE------------------------------------------------------ motif.db <- getMotifById("M178_0.6") ## ---- message=FALSE------------------------------------------------------ results <- runMatrixSPMA(background.set, motifs = motif.db, cache = FALSE) # Usually, all motifs are included in the analysis and results are cached to make subsequent analyses more efficient. # results <- runMatrixSPMA(background.set) ## ---- results='asis', echo=FALSE, fig.width=10, fig.height=7------------- cat("\n\n####", results$spectrum.info.df$motif.rbps, " (", results$spectrum.info.df$motif.id, ")\n\n", sep = "") cat("\n\n**Spectrum plot with polynomial regression:**\n\n") grid::grid.draw(results$spectrum.plots[[1]]) cat("\n\n**Classification:**\n\n") if (results$spectrum.info.df$aggregate.classifier.score == 3) { cat('\n\n
spectrum classification: non-random (3 out of 3 criteria met)
\n\n') } else if (results$spectrum.info.df$aggregate.classifier.score == 2) { cat('\n\nspectrum classification: random (2 out of 3 criteria met)
\n\n') } else if (results$spectrum.info.df$aggregate.classifier.score == 1) { cat('\n\nspectrum classification: random (1 out of 3 criteria met)
\n\n') } else { cat('\n\nspectrum classification: random (0 out of 3 criteria met)
\n\n') } cat("\n\nProperty | Value | Threshold\n") cat("------------- | ------------- | -------------\n") cat("adjusted $R^2$ | ", round(results$spectrum.info.df$adj.r.squared, 3), " | $\\geq 0.4$ \n") cat("polynomial degree | ", results$spectrum.info.df$degree, " | $\\geq 1$ \n") cat("slope | ", round(results$spectrum.info.df$slope, 3), " | $\\neq 0$ \n") cat("unadjusted p-value estimate of consistency score | ", round(results$spectrum.info.df$consistency.score.p.value, 7), " | $< 0.000005$ \n") cat("number of significant bins | ", results$spectrum.info.df$n.significant, " | ", paste0("$\\geq ", floor(40 / 10), "$"), " \n\n")