## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup, message=FALSE----------------------------------------------------- library(MicrobiomeBenchmarkData) library(dplyr) library(ggplot2) library(tidyr) ## ----------------------------------------------------------------------------- tse <- getBenchmarkData('Stammler_2016_16S_spikein', dryrun = FALSE)[[1]] counts <- assay(tse) ## ----------------------------------------------------------------------------- ## AF323500XXXX is the unique OTU corresponding to S. ruber s_ruber <- counts['AF323500XXXX', ] size_factor <- s_ruber/mean(s_ruber) SCML_data <- counts for(i in seq(ncol(SCML_data))){ SCML_data[,i] <- round(SCML_data[,i] / size_factor[i]) } ## ----fig.width=7-------------------------------------------------------------- no_cal <- counts |> colSums() |> as.data.frame() |> tibble::rownames_to_column(var = 'sample_id') |> magrittr::set_colnames(c('sample_id', 'colSum')) |> mutate(calibrated = 'no') |> as_tibble() cal <- SCML_data |> colSums() |> as.data.frame() |> tibble::rownames_to_column(var = 'sample_id') |> magrittr::set_colnames(c('sample_id', 'colSum')) |> mutate(calibrated = 'yes') |> as_tibble() data <- bind_rows(no_cal, cal) data |> ggplot(aes(sample_id, colSum)) + geom_col(aes(fill = calibrated), position = 'dodge') + theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) ## ----------------------------------------------------------------------------- assay(tse) <- SCML_data tse ## ----------------------------------------------------------------------------- sessionInfo()