Contents

This repository describes SampleTrackeR, an R script for sample assurance in multiplexed sequencing experiments, based on tagging of samples with synthetic spike-in control mixtures (STMs).

The following files are available in this repository.

1. "SampleTrackeR.R", source code.

2. Data files for results presented in Tourlousse et al. (see citation), namely "sample_plate_layout1.txt", "sample_plate_layout2.txt", "sample_plate_layout3.txt", "read_count_table1.txt", "read_count_table2.txt", "read_count_table3.txt" and "stm_compositions.txt".

3. "SampleTrackeR.nb.html", an HTML file demonstrating the usage of SampleTrackeR using the above data sets.

Using SampleTrackeR

Prerequisites

SampleTrackeR depends on a small number of external R packages that need to be installed and loaded.

library(magrittr)
library(dplyr)
library(tidyr)
library(ggplot2)

SampleTrackeR consists of a single script that can be loaded by sourcing the code.

source("/absolute/path/to/SampleTrackeR.R")

Note that the script automatically sets the current directory as search path when looking for input files (internally, input.search.path <- getwd()). All input files thus need to be present in the current directory.

Description of input files

sample_plate_layout

This tab-delimited file describes the experimental setup, namely plate layout and STM added to each of the samples. Samples lacking STMs can be added using a mock STM (designated e.g. stm00); the mock STM composition should also be present in the stm_compositions file (see below for details).

The following columns and matching names are required.

• libID: name of the sample or sequencing library.

• stmID: identifier of the STM to added to the sample.

• row: row identifier (should be integer).

• column: column identifier (should be integer).

Other columns can be present (e.g., description in the example table below); these are however ignored and not included in any of the generated output files.

libID	stmID	row	column	description
lib1	stm01	1	1	soil_stm01
lib2	stm02	2	1	sludge_stm20
lib3	stm03	3	1	feces_stm03
...	...	...	...
lib4	stm00	6	12	soil
lib5	stm00	7	12	sludge
lib6	stm00	8	12	feces

read_count_table

This tab-delimited file represent a typical OTU read count table. Read counts for both the individual spike-in controls, as in the stm_compositions file (see below), and sample OTUs can be present. A column with name otuID is mandatory. The other column names represent sample identifiers as in the sample_plate_layout file (see above).

otuID	lib1	lib2	...
control1	101	231	...
control2	3	10	...
control3	0	1	...
...	...	...	...
OTU1	0	5	...
OTU2	6	567	...
...	...	...	...

stm_compositions

This tab-delimited file represents a long-format table with the composition of the STMs.

The following columns and matching names are required.

• stmID: identifier of the STM

• controlID: identifier of the spike-in control

• value: value indicating wether the spike-in control is present in the STM (1: present and 0: absent)

stmID	controlID	value
stm01	control1	1
stm01	control2	1
stm01	control3	0
stm02	control1	1
stm02	control2	0
stm02	control3	1
stm03	control1	0
stm03	control2	1
stm03	control3	1
...	...	...
stm00	control1	0
stm00	control2	0
stm00	control3	0

For the mock STM (here stm00) for samples without added STMs, all values should be set to 0, as shown in the above table.

General usage, output and terminology

out <- SampleTrackeR(sample_plate_layout = "sample_plate_layout.txt",
                     read_count_table = "read_count_table.txt", 
                     stm_compositions = "stm_compositions.txt",
                     read.threshold = 1,
                     fraction.threshold = 1)

Description input arguments

• sample_plate_layout (required) Name of tab-delimited file of sample layout.

• read_count_table (required) Name of tab-delimited file with read count data, including both synthetic spike-in controls and sample OTUs.

• stm_compositions (required) Name of tab-delimited file with STM compositions.

• read.threshold (optional) Minimum number of reads for a given spike-in control to be scored as present (default 1).

• fraction.threshold (optional) Minimum fraction of spike-in controls for a given STM to be scored as present in order for the STM to be scored as present (default 1).

Description outputs

The output of SampleTrackeR is a list with four different objects. Assuming that the output list is called out, the resultant list contains the following:

• out$tab1, a data frame containing a summary of sample identification based on majority STMs.

• out$plot1, a ggplot2 object visualizing the output of the sample identification portion of the script.

• out$tab2, a data frame containing a summary of between-sample carry-over based on minority STMs.

• out$plot2, a ggplot2 object visualizing the output of the between-sample carry-over portion of the script.

Note that out$tab2 and out$plot2 are not generated when no minority STMs are identified in any of the samples. In this case, a message will be output stating that between-sample carry-over was not evaluated, and hence assumed to be minimal based on the lack of minority STMs.

Terminology

• majority_STM, refers to the STM with the highest cumulative read count in a given sample. The majority STM is used to assign sample identity and detect/resolve potential sample swaps.

• minority_STM, refers to STMs present in a sample, excluding the majority STM. A sample can contain multiple minority STMs and these are used for assessment of between-sample carry-over (that is, cross-contamination).

• distinguishing_controls, refers to the number of individual spike-in controls that are not shared between two different STMs. For evaluation of between-sample carry-over, the number of distinguishing controls is the number of spike-in controls present in the minority STM but absent in the majority STM.

• percent_carryover, refers to the estimated amount of sample carry-over between two samples, as quantified based on minority STMs.

Citation

Dieter M. Tourlousse, Akiko Ohashi, Yuji Sekiguchi (2018) Sample tracking in microbiome community profiling assays using synthetic 16S rRNA gene spike-in controls. Scientific Reports 8:9095. DOI: https://doi.org/10.1038/s41598-018-27314-3

Contact

Dieter Tourlousse: dieter.tourlousse@aist.go.jp