Introduction
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.
Dependencies
The MBECS package relies on the following packages to work:
Table: MBECS package dependencies
| Package | Version | Date | Repository |
|---|---|---|---|
| phyloseq | 1.44.0 | 2021-11-29 | NULL |
| magrittr | 2.0.3 | NULL | CRAN |
| cluster | 2.1.4 | 2022-08-19 | CRAN |
| dplyr | 1.1.2 | NULL | CRAN |
| ggplot2 | 3.4.2 | NULL | CRAN |
| gridExtra | 2.3 | NULL | CRAN |
| limma | 3.56.0 | 2023-04-22 | NULL |
| lme4 | 1.1.33 | NULL | CRAN |
| lmerTest | 3.1.3 | NULL | CRAN |
| pheatmap | 1.0.12 | 2018-12-26 | CRAN |
| rmarkdown | 2.21 | NULL | CRAN |
| ruv | 0.9.7.1 | 2019-08-30 | CRAN |
| sva | 3.48.0 | NULL | NULL |
| tibble | 3.2.1 | NULL | CRAN |
| tidyr | 1.3.0 | NULL | CRAN |
| vegan | 2.6.4 | NULL | CRAN |
| methods | 4.3.0 | NULL | NULL |
| stats | 4.3.0 | NULL | NULL |
| utils | 4.3.0 | NULL | NULL |
Installation
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")
Workflow
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)
Start from abundance table
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"))
Start from phyloseq object
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"))
Apply transformations
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)
Preliminary report
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")
Run corrections
The package acts as a wrapper for six different batch effect correcting algorithms (BECA).
-
Remove Unwanted Variation 3 (
ruv3)- This algorithm is implemented in ruv-package by @R-ruv.
- The algorithm requires negative control-features, i.e., features that are known to be unaffected by the batch effect, as well as technical replicates. The algorithm will check for the existence of a replicate column in the covariate data. If the column is not present, the execution stops and a warning message will be displayed. The denominators for negative controls can be supplied via the parameter ‘nc.features’. If they are not supplied, the function will employ a linear model to determine pseudo negative controls that are not significantly affected by the batch effect.
-
Batch Mean Centering (
bmc)- This algorithm is part of this package.
- For known batch effects, this method takes the batches, i.e., subgroup of samples within a particular batch, and centers them to their mean value.
-
ComBat (
bat)- Described by Johnson et al. 2007 this method was initially conceived to work with gene expression data and is part of the sva-package by @R-sva.
- This method uses an non-/parametric empirical Bayes framework to correct for known batch effects.
-
Remove Batch Effect (
rbe)- As part of the limma-package by @R-limma this method was designed to remove BEs from Micro array data.
- The algorithm fits the full-model to the data, i.e., all relevant covariates whose effect should not be removed, and a model that only contains the known batch effects. The difference between these models produces a residual matrix that (presumably) contains only the full-model-effect, e.g., treatment.
-
Percentile Normalization (
pn)- This method was actually developed specifically to facilitate the integration of microbiome data from different studies/experimental set-ups by @gibbons2018. This problem is similar to the mitigation of BEs, i.e., when collectively analyzing two or more data-sets, every study is effectively a batch on its own (not withstanding the probable BEs within studies).
- The algorithm iterates over the unique batches and converts the relative abundance of control samples into their percentiles. The relative abundance of case-samples within the respective batches is then transformed into percentiles of the associated control-distribution. Basically, the procedure assumes that the control-group is unaffected by any effect of interest, e.g., treatment or sickness, but both groups within a batch are affected by that BE. The switch to percentiles (kinda) flattens the effective difference in count values due to batch - as compared to the other batches. This also introduces the two limiting aspects in percentile normalization. It can only be applied to case/control designs because it requires a reference group. In addition, the transformation into percentiles removes information from the data.
- The ‘mbecRunCorrections’ wrapper will
automatically select Total Sum Scaled values for percentile normalization
because that is what it is supposed to be used on. The function
mbecCorrectionscan be used to manually select untransformed or centered log ratio transformed abundances.
-
Support Vector Decomposition (
svd)- Successfully applied to micro-array data by @nielsen2002.
- Basically perform matrix factorization and compute singular eigen-vectors (SEV). Assume that the first SEV captures the batch-effect and remove this effect from the data.
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.
#> Warning: replacing previous import 'utils::findMatches' by
#> 'S4Vectors::findMatches' when loading 'AnnotationDbi'
#> 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.
Post report
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")
Retrieve corrrected data
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")
Use single functions
Exploratory Functions
Relative Log-Expression
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)
Principal Components Analysis
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 Plot
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)
Heatmap
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)
Mosaic
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)
Analysis of Variance
The functions aim to determine the amount of variance that can be attributed to selected covariates of interest and visualize the results.
Linear Model
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)
Linear Mixed Model
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)
Redundancy Analysis
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)
Principal Variance Components Analysis
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)
Silhouette Coefficient
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)
Session
#> R version 4.3.0 RC (2023-04-13 r84269)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] MBECS_1.4.0
#>
#> loaded via a namespace (and not attached):
#> [1] DBI_1.1.3 bitops_1.0-7 gridExtra_2.3
#> [4] permute_0.9-7 rlang_1.1.0 magrittr_2.0.3
#> [7] ade4_1.7-22 matrixStats_0.63.0 compiler_4.3.0
#> [10] RSQLite_2.3.1 mgcv_1.8-42 png_0.1-8
#> [13] vctrs_0.6.2 reshape2_1.4.4 sva_3.48.0
#> [16] stringr_1.5.0 pkgconfig_2.0.3 crayon_1.5.2
#> [19] fastmap_1.1.1 XVector_0.40.0 labeling_0.4.2
#> [22] utf8_1.2.3 nloptr_2.0.3 purrr_1.0.1
#> [25] bit_4.0.5 xfun_0.39 zlibbioc_1.46.0
#> [28] cachem_1.0.7 GenomeInfoDb_1.36.0 jsonlite_1.8.4
#> [31] biomformat_1.28.0 blob_1.2.4 highr_0.10
#> [34] rhdf5filters_1.12.0 Rhdf5lib_1.22.0 BiocParallel_1.34.0
#> [37] parallel_4.3.0 cluster_2.1.4 R6_2.5.1
#> [40] stringi_1.7.12 RColorBrewer_1.1-3 limma_3.56.0
#> [43] genefilter_1.82.0 boot_1.3-28.1 Rcpp_1.0.10
#> [46] iterators_1.0.14 knitr_1.42 IRanges_2.34.0
#> [49] Matrix_1.5-4 splines_4.3.0 igraph_1.4.2
#> [52] tidyselect_1.2.0 vegan_2.6-4 codetools_0.2-19
#> [55] lattice_0.21-8 tibble_3.2.1 plyr_1.8.8
#> [58] Biobase_2.60.0 withr_2.5.0 KEGGREST_1.40.0
#> [61] evaluate_0.20 survival_3.5-5 Biostrings_2.68.0
#> [64] pillar_1.9.0 phyloseq_1.44.0 MatrixGenerics_1.12.0
#> [67] foreach_1.5.2 stats4_4.3.0 generics_0.1.3
#> [70] RCurl_1.98-1.12 S4Vectors_0.38.0 ggplot2_3.4.2
#> [73] munsell_0.5.0 scales_1.2.1 minqa_1.2.5
#> [76] xtable_1.8-4 glue_1.6.2 pheatmap_1.0.12
#> [79] tools_4.3.0 data.table_1.14.8 lme4_1.1-33
#> [82] annotate_1.78.0 locfit_1.5-9.7 XML_3.99-0.14
#> [85] rhdf5_2.44.0 grid_4.3.0 tidyr_1.3.0
#> [88] ape_5.7-1 AnnotationDbi_1.62.0 edgeR_3.42.0
#> [91] colorspace_2.1-0 nlme_3.1-162 GenomeInfoDbData_1.2.10
#> [94] cli_3.6.1 ruv_0.9.7.1 fansi_1.0.4
#> [97] dplyr_1.1.2 gtable_0.3.3 digest_0.6.31
#> [100] BiocGenerics_0.46.0 farver_2.1.1 memoise_2.0.1
#> [103] multtest_2.56.0 lifecycle_1.0.3 httr_1.4.5
#> [106] bit64_4.0.5 MASS_7.3-59