In waldronlab/MicrobiomeBenchmarkData: Datasets for benchmarking in microbiome research

knitr::opts_chunk$set(
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One of the main characteristics of the Stammler_2016_16S_spikein dataset is the presence of spike-in bacteria with a known fixed amount of bacterial cells. These known loads of bacteria can be used to recalibrate the raw counts of the matrix and obtain recalibrated absolute counts. In this vignette, we provide an example of how to recalibrate the counts of the count matrix based on the read counts of Salinibacter ruber. This procedure is referred to as Spike-in-based calibration to total microbial load (SCML) in Sammler et al., 2016.

library(MicrobiomeBenchmarkData)
library(dplyr)
library(ggplot2)
library(tidyr)

Import data

tse <- getBenchmarkData('Stammler_2016_16S_spikein', dryrun = FALSE)[[1]]
counts <- assay(tse)

Ids of the spike-in bacteria

Identifiers of the spiked-in bacteria have the suffix 'XXXX'.

| Bacteria | ID | Load | | -------- | -- | ---- | | Salinibacter ruber | AF323500XXXX | 3.0 x 10⁸ | | Rhizobium radiobacter | AB247615XXXX | 5.0 x 10⁸ | | Alicyclobacillus acidiphilus | AB076660XXXX | 1.0 x 10⁸ |

Recalibrate based on Salinibacter ruber abundance.

This recalibration is based on the original article. The only difference is that the numbers have been rounded up to obtain counts.

## 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])
}

Brief comparison of counts

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))

The counts matrix can be replaced in the original tse in order to preserve the same metadata.

assay(tse) <- SCML_data
tse

Session information

sessionInfo()

waldronlab/MicrobiomeBenchmarkData documentation built on May 2, 2024, 1:20 p.m.