SCRuB is a tool designed to help researchers address the common issue of contamination in microbial studies. This package provides an easy to use framework to apply SCRuB to your projects. All you need to get started are n samples x m taxa count matrices for both your samples and controls. To begin, we recommend working through SCRUB's documentation pages .
For support using SCRuB, please use our issues page or email: [email protected].
SCRuB is implemented in R (>= 3.6.3) and requires the following dependencies: glmnet, torch, tidyverse. Please install and load them prior to trying to install SCRuB.
install.packages( c('glmnet', 'torch', 'tidyverse') )
A QIIME 2 plugin for SCRuB is available here. You can also install SCRuB using devtools:
devtools::install_github("shenhav-and-korem-labs/SCRuB")
As an expectation-maximization model, SCRuB is highly efficient, with very low computational costs. On a standard machine, it only requires 5 seconds to install SCRuB using devtools. For the example dataset consisting of 384 samples, we expect the SCRuB function to require at most 45 seconds of runtime. As a result, the full tutorial notebook we provide, which loads the data and runs through the SCRuB pipeline, will run successfully in under one minute!
data - An ( n_samples + n_controls ) x n_taxa count matrix. We recommend supplying SCRuB with the most granular phylogenetic level possible - e.g., ASV, OTU, mOTU2, etc.
metadata - a metadata matrix of 2 columns (and a third optional column), where each row name is aligned to the data parameter row name.
The first metadata column is boolean, denoting TRUE if the corresponding data row belongs to a sample representing a contamination source, and FALSE otherwise.
The second column is a string, identifying the type of each sample, such that SCRuB can identifying which control samples should be grouped together.
The third (and optional, but highly recommended) column is a string entry identifying the well location of the corresponding sample, which allows SCRuB to track well leakage.
This must be in a standard [LETTER][NUMBER] format, i.e. A3, B12, D4...
(optional) control_order - vector, default NA. If specified, outlines the order of controls to run decontamination in. Input as a vector, of which each element must also be found in the metadata's second column. If not specified, all control types found in metadata will be run sequentially based on their order from that table.
(optional) dist_threshold float - Determines the maximum distance between samples and controls which SCRuB determines as potential sources of well leakage. Default of 1.5. If the provided metadata contains samples' well locations, this parameter determines how SCRuB de-leaks the controls and refines the estimated contamination sources.
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decontaminated_samples - decontaminated_samples - a n_samples x n_taxa count matrix, representing the decontaminated taxonomic compositions.
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p - The fitted p parameter, as described in SCRuB's methods. An n_sample vector representing the estimate proportion of each observe sample that was not contamination. A dataset that had no contamination would have a p of 1s, while a dataset of entirely contamination would have a p of 0.
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inner_iterations -- results from SCRuB's intermediary steps, which includes one list entry per type of control. Each iteration contains:
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3.1).1) decontaminated_samples - a n_samples x n_taxa count matrix, representing the taxonomic composition of the samples after that round of decontamination.
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3.2) p - The fitted
$p$ parameter, as described in SCRuB's methods. An n_sample vector representing the estimate proportion of each observe sample that was not contamination. A dataset that had no contamination would have a p of 1s, while a dataset of entirely contamination would have a p of 0. -
3.3) alpha - The fitted
$\alpha$ parameter, as described in SCRuB's methods. An n_control x ( n_sample + 1 ) matrix, representing the estimated contribution of the contaminant and each sample to each control, where the (n_sample + 1)th column represents the contribution from the contamination to the control. Each row of$\alpha$ sums to 1, with each entry of the (n_sample + 1)th column being 1 means there is zero estimated well leakage, while entries close to zero would indicate there is a high level of well leakage. -
3.4) gamma - the
$\gamma$ parameter described in SCRuB's methods. An n_taxa vector representing the estimated relative abundance of the contamination community -
3.5) loglikelihood - float. The log-likelihood of the inputted dataset based on SCRuB's fitted parameters.
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We provide a dataset for an example of SCRuB usage. Download the demo files here. We provide an rmarkdown notebook here to follow along with the example below.
First load the SCRuB packages into R:
library(SCRuB)
Then, load the datasets:
data <- read.csv('plasma_data.csv', row.names=1) %>% as.matrix()
Next, load the well positions metadata
metadata <- read.csv('plasma_metadata.csv', row.names=1)
Run SCRuB,:
scr_out <- SCRuB(data = data,
metadata = metadata
control_order = c("control blank DNA extraction", "control blank library prep")
)
The required input to SCRuB is composed of one count matrix, and one metadata matrix (or data frame):
(1) data - ( n_samples + n_controls ) x n_taxa count matrix, where m is the number samples and n is the number of taxa. Row names are the sample ids ('SampleID'). Column names are the taxa ids. Every consecutive column contains read counts for each sample. Note that this order must be consistent between the samples and metadata.
count matrix (first 4 rows and columns):
| taxa_1 | taxa_2 | taxa_3 | taxa_4 | |
|---|---|---|---|---|
| ERR525698 | 0 | 5 | 0 | 20 |
| ERR525693 | 15 | 5 | 0 | 0 |
| ERR525688 | 0 | 13 | 0 | 200 |
| ERR525699 | 4 | 5 | 0 | 0 |
(2) metadata - (n_samples + n_controls x 2, or (n_samples + n_controls x 3. The columns must be ordered as described below, with the third column being optional. The three columns are 1) a boolean indicator identifying which entries correspond to control samples; 2) a string column denoting the types of control samples; and (optionally) 3) the plate location of each sample.
| is_control | sample_type | sample_well | |
|---|---|---|---|
| ERR525698 | FALSE | plasma | A1 |
| ERR525693 | FALSE | plasma | A2 |
| ERR525688 | TRUE | extraction control | B1 |
| ERR525699 | FALSE | plasma | B2 |
(3) (optional) control_order - vector, default NA. If specified, outlines the order of controls to run decontamination in. Input as a vector, of which each element must also be found in the metadata's second column. If not specified, all control types found in metadata will be run sequentially based on their order from that table.
The below input indicates that decontamination from the "control blank DNA extraction" samples should be run first, followed by decontamination from the "control blank library prep" samples.
c("control blank DNA extraction", "control blank library prep")
SCRuB outputs a list containing the following:
(1) decontaminated_samples
| taxa_1 | taxa_2 | taxa_3 | taxa_4 | |
|---|---|---|---|---|
| ERR525698 | 0 | 4 | 0 | 0 |
| ERR525693 | 15 | 5 | 0 | 0 |
| ERR525699 | 4 | 5 | 0 | 0 |
(2) p - The estimated fraction of each sample that is not contamination
c(0.16, 1, 1)
(3) outputs from inner SCRuB iterations, which includes fitted (3.1) decontaminated samples and (3.2) p, as described above, and:
(3.3) alpha - the estimated composition of each control sample:
| ERR525698 | ERR525693 | ERR525699 | contaminant | |
|---|---|---|---|---|
| ERR525688 | 0.001 | 0.001 | 0.001 | 0.997 |
(3.4) gamma - The estimated relative abundance of the contamination community
c(0, 0.05, 0, 0.95)
Austin, G.I., Park, H., Meydan, Y. et al. Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01696-w