The Microbiome Batch-Effect Correction Suite aims to provide a toolkit for
stringent assessment and correction of batch-effects in microbiome data sets.
To that end, the package offers wrapper-functions to summarize study-design and
data, e.g., PCA, Heatmap and Mosaic-plots, and to estimate the proportion of
variance that can be attributed to the batch effect. The mbecsCorrection()
function acts as a wrapper for various batch effect correcting algorithms
(BECA) and in conjunction with the aforementioned tools, it can be used to
compare the effectiveness of correction methods on particular sets of data.
All functions of this package are accessible on their own or within the
preliminary and comparative report pipelines respectively.
The MBECS
package relies on the following packages to work:
Table: MBECS package dependencies
Package | Version | Date | Repository |
---|---|---|---|
phyloseq | 1.48.0 | 2021-11-29 | Bioconductor 3.19 |
magrittr | 2.0.3 | NULL | CRAN |
cluster | 2.1.6 | 2023-11-30 | CRAN |
dplyr | 1.1.4 | NULL | CRAN |
ggplot2 | 3.5.1 | NULL | CRAN |
gridExtra | 2.3 | NULL | CRAN |
limma | 3.60.0 | 2024-04-23 | Bioconductor 3.19 |
lme4 | 1.1.35.3 | NULL | CRAN |
lmerTest | 3.1.3 | NULL | CRAN |
pheatmap | 1.0.12 | 2018-12-26 | CRAN |
rmarkdown | 2.26 | NULL | CRAN |
ruv | 0.9.7.1 | 2019-08-30 | CRAN |
sva | 3.52.0 | NULL | Bioconductor 3.19 |
tibble | 3.2.1 | NULL | CRAN |
tidyr | 1.3.1 | NULL | CRAN |
vegan | 2.6.4 | NULL | CRAN |
methods | 4.4.0 | NULL | NULL |
stats | 4.4.0 | NULL | NULL |
utils | 4.4.0 | NULL | NULL |
MBECS
should be installed as follows:
if (!"BiocManager" %in% rownames(installed.packages()))
install.packages("BiocManager")
BiocManager::install("MBECS")
To install the most current (but not necessarily stable) version, use the repository on GitHub:
# Use the devtools package to install from a GitHub repository.
install.packages("devtools")
# This will install the MBECS package from GitHub.
devtools::install_github("rmolbrich/MBECS")
The main application of this package is microbiome data. It is common practice
to use the phyloseq
[@R-phyloseq] package for analyses of this type of data.
To that end, the MBECS
package extends the phyloseq
class in order to
provide its functionality. The user can utilize objects of class phyloseq
or
a list
object that contains an abundance table as well as meta data. The
package contains a dummy data-set of artificially generated data to illustrate
this process. To start an analysis, the user requires the mbecProcessInput()
function.
Load the package via the library()
function.
library(MBECS)
Use the data()
function to load the provided mockup data-sets at this point.
# List object
data(dummy.list)
# Phyloseq object
data(dummy.ps)
# MbecData object
data(dummy.mbec)
For an input that consists of an abundance table and meta-data, both tables
require sample names as either row or column names. They need to be passed in a
list
object with the abundance matrix as first element. The
mbecProcessInput()
function will handle the correct orientation and return an
object of class MbecData
.
# The dummy-list input object comprises two matrices:
names(dummy.list)
#> [1] "cnts" "meta"
The optional argument required.col
may be used to ensure that all covariate
columns that should be there are available. For the dummy-data these are
“group”,
“batch” and “replicate”.
mbec.obj <- mbecProcessInput(dummy.list,
required.col = c("group", "batch", "replicate"))
The start is the same if the data is already of class phyloseq
. The dummy.ps
object contains the same data as dummy.list
, but it is of class phyloseq
.
Create an MbecData
object from phyloseq
input.
The optional argument required.col
may be used to ensure that all covariate
columns that should be there are available. For the dummy-data these are
“group”,
“batch” and “replicate”.
mbec.obj <- mbecProcessInput(dummy.ps,
required.col = c("group", "batch", "replicate"))
The most common normalizing transformations in microbiome analysis are total
sum scaling (TSS) and centered log-ratio transformation (CLR). Hence, the
MBECS
package offers these two methods. The resulting matrices will be stored
in their respective slots (tss, clr)
in the MbecData
object, while the
original abundance table will remain unchanged.
Use mbecTransform()
to apply total sum scaling to the data.
mbec.obj <- mbecTransform(mbec.obj, method = "tss")
#> Set tss-transformed counts.
Apply centered log-ratio transformation to the data. Due to the sparse nature
of compositional microbiome data, the parameter offset
may be used to add a
small offset to the abundance matrix in order to facilitate the CLR
transformation.
mbec.obj <- mbecTransform(mbec.obj, method = "clr", offset = 0.0001)
The function mbecReportPrelim()
will provide the user with an overview of
experimental setup and the significance of the batch effect. To that end it is
required to declare the covariates that are related to batch effect and group
effect respectively. In addition it provides the option to select the abundance
table to use here. The CLR transformed abundances are the default and the
function will calculate them if they are not present in the input. Technically,
the user can start the analysis at this point because the function incorporates
the functionality of the aforementioned processing functions.
The parameter model.vars
is a character vector with two elements. The first
denotes the covariate column that describes the batch effect and the second one
should be used for the presumed biological effect of interest, e.g., the group
effect in case/control studies. The type
parameter selects which abundance
table is to be used “otu”,
“clr”, “tss”
.
mbecReportPrelim(input.obj=mbec.obj, model.vars=c("batch","group"),
type="clr")
The package acts as a wrapper for six different batch effect correcting algorithms (BECA).
Remove Unwanted Variation 3 (ruv3
)
Batch Mean Centering (bmc
)
ComBat (bat
)
Remove Batch Effect (rbe
)
Percentile Normalization (pn
)
mbecCorrections
can be used to manually select untransformed or centered
log ratio transformed abundances.Support Vector Decomposition (svd
)
The user has the choice between two functions mbecCorrection()
and
mbecRunCorrections()
, the latter one acts as a wrapper that can apply multiple
correction methods in a single run. If the input
to mbecRunCorrections()
is missing CLR and TSS transformed abundance matrices,
they will be created with default settings, i.e., offsets for zero values will
be set to 1/#features.
The function mbecCorrection()
will apply a single correction algorithm
selected by the parameter method
and return an object that contains the
resulting corrected abundance matrix in its cor slot
with the respective name.
mbec.obj <- mbecCorrection(mbec.obj, model.vars=c("batch","group"),
method = "bat", type = "clr")
#> Applying ComBat (sva) for batch-correction.
#> Found2batches
#> Adjusting for1covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding nonparametric adjustments
#> Adjusting the Data
The function mbecRunCorrections()
will apply all correction algorithms
selected by the parameter method
and return an object that contains all
respective corrected abundance matrices in the cor
slot. In this example
there will be three in total, named like the methods that created them.
mbec.obj <- mbecRunCorrections(mbec.obj, model.vars=c("batch","group"),
method=c("ruv3","rbe","bmc","pn","svd"),
type = "clr")
#> No negative control features provided.
#> Using pseudo-negative controls.
#> Applying Remove Unwanted Variantion v3 (RUV-III).
#> Applying 'removeBatchEffect' (limma) for batch-correction.
#> Applying Batch Mean-Centering (BMC) for batch-correction.
#> Applying Percentile Normalization (PN).
#> Warning in mbecPN(input.obj, model.vars, type = eval(type)): Abundances contain
#> zero values. Adding small uniform offset.
#> Group A is considered control group, i.e., reference
#> for normalization procedure. To change reference please 'relevel()'
#> grouping factor accordingly.
#> Applying Singular Value Decomposition (SVD) for batch-correction.
The mbecReportPost()
function will provide the user with a comparative report
that shows how the chosen batch effect correction algorithms changed the
data-set compared to the initial values.
The parameter model.vars
is a character vector with two elements. The first
denotes the covariate column that describes the batch effect and the second one
should be used for the presumed biological effect of interest, e.g., the group
effect in case/control studies. The type
parameter selects which abundance
table is to be used “otu”,
“clr”, “tss”
.
mbecReportPost(input.obj=mbec.obj, model.vars=c("batch","group"),
type="clr")
Because the MbecData
class extends the phyloseq
class, all functions from
phyloseq
can be used as well. They do however only apply to the otu_table
slot and will return an object of class phyloseq
, i.e., any transformations
or corrections will be lost. To retrieve an object of class phyloseq
that
contains the otu_table
of corrected counts, for downstream analyses, the user
can employ the mbecGetPhyloseq()
function. As before, the arguments type
and
label
are used to specify which abundance table should be used in the
returned object.
To retrieve the CLR transformed counts, set type
accordingly.
ps.clr <- mbecGetPhyloseq(mbec.obj, type="clr")
If the batch-mean-centering corrected counts show the best results, select
“cor” as type
and set the label
to
“bmc”.
ps.bmc <- mbecGetPhyloseq(mbec.obj, type="cor", label="bmc")
Relative Log-Expression plots can be created with the mbecRLE()
function.
Produce RLE-plot on CLR transformed values. The return.data
parameter can be
set to TRUE to retrieve the data for
inspection or use in your own plotting function.
rle.plot <- mbecRLE(input.obj=mbec.obj, model.vars = c("batch","group"),
type="clr",return.data = FALSE)
#> Calculating RLE for group: A
#> Calculating RLE for group: B
# Take a look.
plot(rle.plot)
PCA plots can be created with the mbecPCA()
function.
Produce PCA-plot on CLR transformed values. The principal components can be
selected with the pca.axes
parameter. The vector of length two works like
this c(x-axis,
y-axis). The return data parameter can
be set to TRUE to retrieve the data for
inspection or use in your own plotting function.
pca.plot <- mbecPCA(input.obj=mbec.obj, model.vars = "group", type="clr",
pca.axes=c(1,2), return.data = FALSE)
Plot with two grouping variables, determining color and shape of the output.
pca.plot <- mbecPCA(input.obj=mbec.obj, model.vars = c("batch","group"),
type="clr", pca.axes=c(1,2), return.data = FALSE)
Box plots of the most variable features can be create with the mbecBox()
function.
Produce Box-plots of the most variable features on CLR transformed values. The
method
parameter determines if “ALL” or
only the “TOP” n features are plotted. The
n
parameter selects the number of features to plot. The function will return a
list
of plot objects to be used.
box.plot <- mbecBox(input.obj=mbec.obj, method = "TOP", n = 2,
model.var = "batch", type="clr", return.data = FALSE)
# Take a look.
plot(box.plot$OTU109)
Setting method
to a vector of feature names will select exactly these
features for plotting.
box.plot <- mbecBox(input.obj=mbec.obj, method = c("OTU1","OTU2"),
model.var = "batch", type="clr", return.data = FALSE)
#> 'Method' parameter contains multiple elements -
#> using to select features.
# Take a look.
plot(box.plot$OTU2)
Pheatmap is used to produce heat-maps of the most variable features with the
mbecHeat()
function.
Produce a heatmap of the most variable features on CLR transformed values. The
method
parameter determines if “ALL” or
only the “TOP” n features are plotted. The
n
parameter selects the number of features to plot. The parameters center
and scale
can be used to de-/activate centering and scaling prior to plotting.
The model.vars
parameter denotes all covariates of interest.
heat.plot <- mbecHeat(input.obj=mbec.obj, method = "TOP", n = 10,
model.vars = c("batch","group"), center = TRUE,
scale = TRUE, type="clr", return.data = FALSE)
A mosaic plot of the distribution of samples over two covariates of interest
can be produced with the mbecMosaic()
function.
mosaic.plot <- mbecMosaic(input.obj = mbec.obj,
model.vars = c("batch","group"),
return.data = FALSE)
The functions aim to determine the amount of variance that can be attributed to selected covariates of interest and visualize the results.
Use the function mbecModelVariance()
with parameter method
set to
“lm” to fit a linear model of the form
y ~ group + batch
to every feature. Covariates of interest can be selected
with the model.vars
parameter and the function will construct a
model-formula. The parameters type
and label
can be used to select the
desired abundance matrix to work on This defaults to CLR transformed values.
Plot the resulting data with the mbecVarianceStatsPlot()
function and show the
proportion of variance with regards to the covariates in a box-plot.
lm.variance <- mbecModelVariance(input.obj=mbec.obj,
model.vars = c("batch", "group"),
method="lm",type="cor", label="svd")
# Produce plot from data.
lm.plot <- mbecVarianceStatsPlot(lm.variance)
# Take a look.
plot(lm.plot)
Use the function mbecModelVariance()
with parameter method
set to
“lmm” to fit a linear mixed model of the
form y ~ group + (1|batch)
to every feature. Covariates of interest can be
selected with the model.vars
parameter and the function will construct a
model-formula. The parameters type
and label
can be used to select the
desired abundance matrix to work on This defaults to CLR transformed values.
Plot the resulting data with the mbecVarianceStatsPlot()
function and show the
proportion of variance with regards to the covariates in a box-plot.
lmm.variance <- mbecModelVariance(input.obj=mbec.obj,
model.vars = c("batch","group"),
method="lmm",
type="cor", label="ruv3")
# Produce plot from data.
lmm.plot <- mbecVarianceStatsPlot(lmm.variance)
# Take a look.
plot(lmm.plot)
Use the function mbecModelVariance()
with parameter method
set to
“rda” to calculate the percentage of
variance that can be attributed to the covariates of interest with partial
Redundancy Analysis. Covariates of interest can be selected with the
model.vars
parameter. The parameters type
and label
can be used to select
the desired abundance matrix to work on This defaults to CLR transformed values.
Plot the resulting data with the mbecRDAStatsPlot()
function and show the
percentage of variance with regards to the covariates in a bar-plot.
rda.variance <- mbecModelVariance(input.obj=mbec.obj,
model.vars = c("batch", "group"),
method="rda",type="cor", label="bat")
# Produce plot from data.
rda.plot <- mbecRDAStatsPlot(rda.variance)
# Take a look.
plot(rda.plot)
Use the function mbecModelVariance()
with parameter method
set to
“pvca” to calculate the percentage of
variance that can be attributed to the covariates of interest using Principal
Variance Components Analysis. Covariates of interest can be selected with the
model.vars
parameter. The parameters type
and label
can be used to select
the desired abundance matrix to work on This defaults to CLR transformed values.
Plot the resulting data with the mbecPVCAStatsPlot()
function and show the
percentage of variance with regards to the covariates in a bar-plot.
pvca.variance <- mbecModelVariance(input.obj=mbec.obj,
model.vars = c("batch", "group"),
method="pvca",type="cor", label="rbe")
# Produce plot from data.
pvca.plot <- mbecPVCAStatsPlot(pvca.variance)
# Take a look.
plot(pvca.plot)
Evaluate how good the samples fit to the clustering that is implied by the covariates of interest.
Use the function mbecModelVariance()
with parameter method
set to
“s.coef” to evaluate the clustering that is
implied by the covariates of interest with the Silhouette Coefficient.
Covariates of interest can be selected with the model.vars
parameter. The
parameters type
and label
can be used to select the desired abundance
matrix to work on This defaults to CLR transformed values.
Plot the resulting data with the mbecSCOEFStatsPlot()
function and show the
silhouette coefficients in a dot-plot.
sil.coefficient <- mbecModelVariance(input.obj=mbec.obj,
model.vars = c("batch", "group"),
method="s.coef",type="cor", label="bmc")
# Produce plot from data.
scoef.plot <- mbecSCOEFStatsPlot(sil.coefficient)
# Take a look.
plot(scoef.plot)
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
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#>
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#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
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#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> [1] MBECS_1.8.0
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