README.md
In hk1785/MiVT: Microbiome Virtual Twins
MiVT
Title: Microbiome Virtual Twins
Version: 1.0
Date: 2022-12-15
Author: Hyunwook Koh
Maintainer: Hyunwook Koh hyunwook.koh@stonybrook.edu
Description: This R package provides facilities for MiVT that predicts treatment effects using dML, and then identifies subgroups by treatment effects based on microbiome composition using BoRT to evaluate the interplay between microbiome and treatment.
NeedsCompilation: no
Depends: R(>= 4.1.1), cluster, compositions, dirmult, glmnet, GUniFrac, ecodist, neuralnet, phangorn, phyloseq, proxy, randomForest, rpart, rpart.plot, splitTools, zCompositions
License: GPL-2
NeedsCompilation: no
URL: https://github.com/hk1785/MiVT
Reference
- Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
Troubleshooting Tips
If you have any problems for using this R package, please report in Issues (https://github.com/hk1785/MiVT/issues) or email Hyunwook Koh (hyunwook.koh@stonybrook.edu).
Prerequites
Rtools
https://cran.r-project.org/bin/windows/Rtools/rtools40.html
phyloseq
https://joey711.github.io/phyloseq/
cluster, compositions, devtools, dirmult, glmnet, GUniFrac, ecodist, neuralnet, phangorn, proxy, randomForest, rpart, rpart.plot, splitTools, zCompositions
install.packages(c("cluster", "compositions", "devtools", "dirmult", "glmnet", "GUniFrac", "ecodist", "nuralnet", "phangorn", "proxy", "randomForest", "rpart", "rpart.plot", "splitTools", "zCompositions"))
Installation
library(devtools)
install_github("hk1785/MiVT", force=T)
Manual
This R package contains four core functions, gen.syn.dat, biom.qc, dML and BoRT. Details are below.
:mag: gen.syn.dat
Description
This function generates example microbiome data.
Usage
gen.syn.dat(tree, tax.tab, prop, disp, num.sams = 50, seq.depth = sample(10000:1e+05, 50), keep.cut.off = 200)
Arguments
- tree - A rooted phylogenetic tree.
- tax.tab - A taxonomic table where rows are features (OTUs or ASVs), and columns are seven taxonomic ranks (Kingdom, Phylum, Class, Order, Family, Genus, Species)
- prop - A vector of proportion parameters for the Dirichlet-Multinomial distribution.\
- disp - A dispersion parameter for the Dirichlet-Multinomial distribution.
- num.sams - A sample size (Default: 50).
- seq.depth - A vector of total read counts across subjects (Default: sample(10000:1e+05, 50)).
- keep.cut.off - A number of features to keep in the microbiome data (=< 755) (Default: 200).
Values
A synthetic microbiome data in the 'phyloseq' format.
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
:mag: biom.qc
Description
This function performs quality controls and data transformations that are needed for MiVT.
Usage
biom.qc(biom = biom, kingdom = "Bacteria", lib.size.cut.off = 1000, mean.prop.cut.off = 0, rem.tax.com = c("", "gut metagenome", "mouse gut metagenome", "metagenome", "NANANA"), rem.tax.par = c("uncultured", "incertae", "Incertae", "unclassified", "unidentified", "unknown"))
Arguments
- biom - A microbiome data in the phyloseq format. sample_data(biom) should contain two binary variables: y (response) and Tr (treatment). See the example data using gen.syn.dat().
- kingdom - A microbial kingdom to be analyzed, such as 'Bacteria', 'Archaea', 'Eukaryota' or 'all'. 'all' is for all kingdoms in the taxonomic table (Default: 'Bacteria').
- lib.size.cut.off - A minimum total read count for subjects to keep in the microbiome data (Default: 1000).
- mean.prop.cut.off - A minimum mean proportion for microbial features (OTUs or ASVs) to keep in the microbiome data (Default: 0).
- rem.tax.com - Remove taxonomic names in the taxonomic table that are completely matched with the specified character strings (Default: c("", "gut metagenome", "mouse gut metagenome", "metagenome", "NANANA")).
- rem.tax.par - Remove taxonomic names in the taxonomic table that are partially matched with the specified character strings (Default: c("uncultured", "incertae", "Incertae", "unclassified", "unidentified", "unknown")).
Values
$tax.prop: A list of tables for the proportions of microbial taxa on each taxonomic rank (Phylum, Class, Order, Family, Genus, Species).
$otu.tab: A feature (OTU or ASV) table where rows are features and columns are subjects.
$tax.tab: A taxonomic table where rows are features (OTUs or ASVs), and columns are seven taxonomic ranks (Kingdom, Phylum, Class, Order, Family, Genus, Species).
$sam.dat: A metadata/sample information where rows are subjects and columns are variables. It should contain two binary variables: y (response) and Tr (treatment).
$tree: A rooted phylogenetic tree.
References
- Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
:mag: dML
Description
This function implements dML to predicts treatment effects.
Usage
dML(y, Tr, X, tree, n.folds = 10, n.rep = 2, alpha = seq(0.05, 0.95, 0.05), n.trees = 1000, n.neus = c(1/2, 1/3, 1/4))
Arguments
- y - A vector of binary responses.
- Tr - A vector of binary treatment status.
- X - A feature (OTU or ASV) table where rows are features and columns are subjects.
- tree - A rooted phylogenetic tree.
- n.folds - The number of folds in the k-fold cross-validation (Default: 10).
- n.rep - The number of repeats of the k-fold cross-validation (Default: 2).
- alpha - A rooted phylogenetic tree.
- n.trees - The number of folds in the k-fold cross-validation (Default: 10).
- n.neus - Candidate numbers of neurons on the first hidden layer for the three layer deep feedforward network: {[M/2], [M/4], [M/8]}, {[M/3], [M/6], [M/12]}, and {[M/4], [M/8], [M/16]}, where M is the number of predictors (coordinates).
Values
$out.en$cv.cro: CV cross-entropy values for the elastic net and each distance measure.
$out.en$Z: Predicted treatment effects using the elastic net.
$out.rf$cv.cro: CV cross-entropy values for the random forest and each distance measure.
$out.rf$Z: Predicted treatment effects using the random forest.
$out.dfn$cv.cro: CV cross-entropy values for the deep feedforward network and each distance measure.
$out.dfn$Z: Predicted treatment effects using the deep feedforward network.
$Z: Predicted treatment effects using dML.
References
-
Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
-
Foster, J. C., Taylor, J. M. & Ruberg, S. J. Subgroup identification from randomized clinical trial data. Stat. Med. 30(24), 2867-2880 (2011).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
Perform dML
dml.out <- dML(y = qc.out$sam.dat$y, Tr = qc.out$sam.dat$Tr, X = qc.out$otu.tab, tree = qc.out$tree)
:mag: BoRT
Description
This function implements BoRT for subgroup identification and significance testing.
Usage
BoRT(Z, tax.prop, tax.rank = c("Phylum", "Class", "Order", "Family", "Genus", "Species"), minsplit = 10, minbucket = 5, cp = 0.01, n.boot = 20000)
Arguments
- Z - Predicted treatment effects using dML (see dML()).
- tax.prop - A list of tables for the proportions of microbial taxa on each taxonomic rank (Phylum, Class, Order, Family, Genus, Species) (see biom.clean()).
- tax.rank -A taxonomic rank to be used for the subgroup identification (tax.rank = c("Phylum", "Class", "Order", "Family", "Genus", "Species")).
- minsplit - The minimum number of observations that must exist in a node in order for a split to be attempted (Default = 10).
- minbucket - The minimum number of observations in any terminal node (Default = 5).
- cp - The complexity parameter of the decision tree (Default = 0.01).
- n.boot - The number of bootstrap samples (Default = 20000).
Values
$Sel.Taxa: Short taxonomic IDs and full taxonomic names.
$BoRT.out: The output table of BoRT. Columns are the identified subgroups that correspond with the terminal nodes from left to right. N is the sample size for each subgroup. Overall TE represents the overall treatment effect, and Subgroup TE represents the subgroup treatment effect.
References
-
Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
-
Foster, J. C., Taylor, J. M. & Ruberg, S. J. Subgroup identification from randomized clinical trial data. Stat. Med. 30(24), 2867-2880 (2011).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
Perform dML
dml.out <- dML(y = qc.out$sam.dat$y, Tr = qc.out$sam.dat$Tr, X = qc.out$otu.tab, tree = qc.out$tree)
Perform BoRT
bort.out <- BoRT(Z = dml.out$Z, tax.prop = qc.out$tax.prop, tax.rank = "Genus")
bort.out
hk1785/MiVT documentation built on Dec. 21, 2024, 1:08 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
MiVT
Title: Microbiome Virtual Twins
Version: 1.0
Date: 2022-12-15
Author: Hyunwook Koh
Maintainer: Hyunwook Koh hyunwook.koh@stonybrook.edu
Description: This R package provides facilities for MiVT that predicts treatment effects using dML, and then identifies subgroups by treatment effects based on microbiome composition using BoRT to evaluate the interplay between microbiome and treatment.
NeedsCompilation: no
Depends: R(>= 4.1.1), cluster, compositions, dirmult, glmnet, GUniFrac, ecodist, neuralnet, phangorn, phyloseq, proxy, randomForest, rpart, rpart.plot, splitTools, zCompositions
License: GPL-2
NeedsCompilation: no
URL: https://github.com/hk1785/MiVT
Reference
- Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
Troubleshooting Tips
If you have any problems for using this R package, please report in Issues (https://github.com/hk1785/MiVT/issues) or email Hyunwook Koh (hyunwook.koh@stonybrook.edu).
Prerequites
Rtools
https://cran.r-project.org/bin/windows/Rtools/rtools40.html
phyloseq
https://joey711.github.io/phyloseq/
cluster, compositions, devtools, dirmult, glmnet, GUniFrac, ecodist, neuralnet, phangorn, proxy, randomForest, rpart, rpart.plot, splitTools, zCompositions
install.packages(c("cluster", "compositions", "devtools", "dirmult", "glmnet", "GUniFrac", "ecodist", "nuralnet", "phangorn", "proxy", "randomForest", "rpart", "rpart.plot", "splitTools", "zCompositions"))
Installation
library(devtools)
install_github("hk1785/MiVT", force=T)
Manual
This R package contains four core functions, gen.syn.dat, biom.qc, dML and BoRT. Details are below.
:mag: gen.syn.dat
Description
This function generates example microbiome data.
Usage
gen.syn.dat(tree, tax.tab, prop, disp, num.sams = 50, seq.depth = sample(10000:1e+05, 50), keep.cut.off = 200)
Arguments
- tree - A rooted phylogenetic tree.
- tax.tab - A taxonomic table where rows are features (OTUs or ASVs), and columns are seven taxonomic ranks (Kingdom, Phylum, Class, Order, Family, Genus, Species)
- prop - A vector of proportion parameters for the Dirichlet-Multinomial distribution.\
- disp - A dispersion parameter for the Dirichlet-Multinomial distribution.
- num.sams - A sample size (Default: 50).
- seq.depth - A vector of total read counts across subjects (Default: sample(10000:1e+05, 50)).
- keep.cut.off - A number of features to keep in the microbiome data (=< 755) (Default: 200).
Values
A synthetic microbiome data in the 'phyloseq' format.
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
:mag: biom.qc
Description
This function performs quality controls and data transformations that are needed for MiVT.
Usage
biom.qc(biom = biom, kingdom = "Bacteria", lib.size.cut.off = 1000, mean.prop.cut.off = 0, rem.tax.com = c("", "gut metagenome", "mouse gut metagenome", "metagenome", "NANANA"), rem.tax.par = c("uncultured", "incertae", "Incertae", "unclassified", "unidentified", "unknown"))
Arguments
- biom - A microbiome data in the phyloseq format. sample_data(biom) should contain two binary variables: y (response) and Tr (treatment). See the example data using gen.syn.dat().
- kingdom - A microbial kingdom to be analyzed, such as 'Bacteria', 'Archaea', 'Eukaryota' or 'all'. 'all' is for all kingdoms in the taxonomic table (Default: 'Bacteria').
- lib.size.cut.off - A minimum total read count for subjects to keep in the microbiome data (Default: 1000).
- mean.prop.cut.off - A minimum mean proportion for microbial features (OTUs or ASVs) to keep in the microbiome data (Default: 0).
- rem.tax.com - Remove taxonomic names in the taxonomic table that are completely matched with the specified character strings (Default: c("", "gut metagenome", "mouse gut metagenome", "metagenome", "NANANA")).
- rem.tax.par - Remove taxonomic names in the taxonomic table that are partially matched with the specified character strings (Default: c("uncultured", "incertae", "Incertae", "unclassified", "unidentified", "unknown")).
Values
$tax.prop: A list of tables for the proportions of microbial taxa on each taxonomic rank (Phylum, Class, Order, Family, Genus, Species).
$otu.tab: A feature (OTU or ASV) table where rows are features and columns are subjects.
$tax.tab: A taxonomic table where rows are features (OTUs or ASVs), and columns are seven taxonomic ranks (Kingdom, Phylum, Class, Order, Family, Genus, Species).
$sam.dat: A metadata/sample information where rows are subjects and columns are variables. It should contain two binary variables: y (response) and Tr (treatment).
$tree: A rooted phylogenetic tree.
References
- Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
:mag: dML
Description
This function implements dML to predicts treatment effects.
Usage
dML(y, Tr, X, tree, n.folds = 10, n.rep = 2, alpha = seq(0.05, 0.95, 0.05), n.trees = 1000, n.neus = c(1/2, 1/3, 1/4))
Arguments
- y - A vector of binary responses.
- Tr - A vector of binary treatment status.
- X - A feature (OTU or ASV) table where rows are features and columns are subjects.
- tree - A rooted phylogenetic tree.
- n.folds - The number of folds in the k-fold cross-validation (Default: 10).
- n.rep - The number of repeats of the k-fold cross-validation (Default: 2).
- alpha - A rooted phylogenetic tree.
- n.trees - The number of folds in the k-fold cross-validation (Default: 10).
- n.neus - Candidate numbers of neurons on the first hidden layer for the three layer deep feedforward network: {[M/2], [M/4], [M/8]}, {[M/3], [M/6], [M/12]}, and {[M/4], [M/8], [M/16]}, where M is the number of predictors (coordinates).
Values
$out.en$cv.cro: CV cross-entropy values for the elastic net and each distance measure. $out.en$Z: Predicted treatment effects using the elastic net.
$out.rf$cv.cro: CV cross-entropy values for the random forest and each distance measure. $out.rf$Z: Predicted treatment effects using the random forest.
$out.dfn$cv.cro: CV cross-entropy values for the deep feedforward network and each distance measure. $out.dfn$Z: Predicted treatment effects using the deep feedforward network.
$Z: Predicted treatment effects using dML.
References
-
Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
-
Foster, J. C., Taylor, J. M. & Ruberg, S. J. Subgroup identification from randomized clinical trial data. Stat. Med. 30(24), 2867-2880 (2011).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
Perform dML
dml.out <- dML(y = qc.out$sam.dat$y, Tr = qc.out$sam.dat$Tr, X = qc.out$otu.tab, tree = qc.out$tree)
:mag: BoRT
Description
This function implements BoRT for subgroup identification and significance testing.
Usage
BoRT(Z, tax.prop, tax.rank = c("Phylum", "Class", "Order", "Family", "Genus", "Species"), minsplit = 10, minbucket = 5, cp = 0.01, n.boot = 20000)
Arguments
- Z - Predicted treatment effects using dML (see dML()).
- tax.prop - A list of tables for the proportions of microbial taxa on each taxonomic rank (Phylum, Class, Order, Family, Genus, Species) (see biom.clean()).
- tax.rank -A taxonomic rank to be used for the subgroup identification (tax.rank = c("Phylum", "Class", "Order", "Family", "Genus", "Species")).
- minsplit - The minimum number of observations that must exist in a node in order for a split to be attempted (Default = 10).
- minbucket - The minimum number of observations in any terminal node (Default = 5).
- cp - The complexity parameter of the decision tree (Default = 0.01).
- n.boot - The number of bootstrap samples (Default = 20000).
Values
$Sel.Taxa: Short taxonomic IDs and full taxonomic names.
$BoRT.out: The output table of BoRT. Columns are the identified subgroups that correspond with the terminal nodes from left to right. N is the sample size for each subgroup. Overall TE represents the overall treatment effect, and Subgroup TE represents the subgroup treatment effect.
References
-
Koh, H. (2023) Subgroup identification using virtual twins for human microbiome studies. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-10 (DOI: 10.1109/TCBB.2023.3324139).
-
Foster, J. C., Taylor, J. M. & Ruberg, S. J. Subgroup identification from randomized clinical trial data. Stat. Med. 30(24), 2867-2880 (2011).
Example
Import requisite R packages
library('phyloseq')
library('cluster')
library('dirmult')
library('phangorn')
library('compositions')
library('zCompositions')
library('GUniFrac')
library('ecodist')
library('proxy')
library('glmnet')
library('randomForest')
library('neuralnet')
library('splitTools')
library('rpart')
library('rpart.plot')
library('MiVT')
Generate example microbiome data
data(fit)
data(tree)
data(tax.tab)
prop <- fit$pi
disp <- fit$theta
sim.biom <- gen.syn.dat(tree = tree, tax.tab = tax.tab, prop = prop, disp = disp)
sim.biom
Perform quality controls and data transformations
qc.out <- biom.qc(biom = sim.biom)
Perform dML
dml.out <- dML(y = qc.out$sam.dat$y, Tr = qc.out$sam.dat$Tr, X = qc.out$otu.tab, tree = qc.out$tree)
Perform BoRT
bort.out <- BoRT(Z = dml.out$Z, tax.prop = qc.out$tax.prop, tax.rank = "Genus")
bort.out
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