microtable: Create 'microtable' object to store and manage all the basic...
In microeco: Microbial Community Ecology Data Analysis
microtable R Documentation
Create microtable object to store and manage all the basic files.
Description
This class is a wrapper for a series of operations on the input files and basic manipulations,
including microtable object creation, data trimming, data filtering, rarefaction based on Paul et al. (2013) <doi:10.1371/journal.pone.0061217>, taxonomic abundance calculation,
alpha and beta diversity calculation based on the An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035> and
Lozupone et al. (2005) <doi:10.1128/AEM.71.12.8228-8235.2005> and other basic operations.
Online tutorial: https://chiliubio.github.io/microeco_tutorial/
Download tutorial: https://github.com/ChiLiubio/microeco_tutorial/releases
Format
microtable.
Methods
Public methods
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Method new()
Usage
microtable$new(
otu_table,
sample_table = NULL,
tax_table = NULL,
phylo_tree = NULL,
rep_fasta = NULL,
auto_tidy = FALSE
)
Arguments
otu_tabledata.frame; The feature abundance table; rownames are features (e.g. OTUs/ASVs/species/genes); column names are samples.
sample_tabledata.frame; default NULL; The sample information table; rownames are samples; columns are sample metadata;
If not provided, the function can generate a table automatically according to the sample names in otu_table.
tax_tabledata.frame; default NULL; The taxonomic information table; rownames are features; column names are taxonomic classes.
phylo_treephylo; default NULL; The phylogenetic tree; use read.tree function in ape package for input.
rep_fastaDNAStringSet or list format; default NULL; The representative sequences;
use read.fasta function in seqinr package or readDNAStringSet function in Biostrings package for input.
auto_tidydefault FALSE; Whether trim the files in the microtable object automatically;
If TRUE, running the functions in microtable class can invoke the tidy_dataset function automatically.
Returns
an object of class microtable with the following components:
sample_tableThe sample information table.
otu_tableThe feature table.
tax_tableThe taxonomic table.
phylo_treeThe phylogenetic tree.
rep_fastaThe representative sequence.
taxa_abunddefault NULL; use cal_abund function to calculate.
alpha_diversitydefault NULL; use cal_alphadiv function to calculate.
beta_diversitydefault NULL; use cal_betadiv function to calculate.
Examples
data(otu_table_16S)
data(taxonomy_table_16S)
data(sample_info_16S)
data(phylo_tree_16S)
m1 <- microtable$new(otu_table = otu_table_16S)
m1 <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S,
tax_table = taxonomy_table_16S, phylo_tree = phylo_tree_16S)
# trim the files in the dataset
m1$tidy_dataset()
Method filter_pollution()
Filter the features considered pollution in microtable$tax_table.
This operation will remove any line of the microtable$tax_table containing any the word in taxa parameter regardless of word case.
Usage
microtable$filter_pollution(taxa = c("mitochondria", "chloroplast"))
Arguments
taxadefault c("mitochondria", "chloroplast"); filter mitochondria and chloroplast, or others as needed.
Returns
None
Examples
m1$filter_pollution(taxa = c("mitochondria", "chloroplast"))
Method filter_taxa()
Filter the feature with low abundance and/or low occurrence frequency.
Usage
microtable$filter_taxa(rel_abund = 0, freq = 1, include_lowest = TRUE)
Arguments
rel_abunddefault 0; the relative abundance threshold, such as 0.0001.
freqdefault 1; the occurrence frequency threshold.
For example, the number 2 represents filtering the feature that occurs less than 2 times.
A number smaller than 1 is also allowable.
For instance, the number 0.1 represents filtering the feature that occurs in less than 10% samples.
include_lowestdefault TRUE; whether include the feature with the threshold.
Returns
None
Examples
\donttest{
d1 <- clone(m1)
d1$filter_taxa(rel_abund = 0.0001, freq = 0.2)
}
Method rarefy_samples()
Rarefy communities to make all samples have same feature number.
Usage
microtable$rarefy_samples(
method = c("rarefying", "SRS")[1],
sample.size = NULL,
rngseed = 123,
replace = TRUE
)
Arguments
methoddefault c("rarefying", "SRS")[1]; "rarefying" represents the classical resampling like rrarefy function of vegan package.
"SRS" is scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) <DOI:10.7717/peerj.9593>.
sample.sizedefault NULL; feature number, If not provided, use minimum number of all samples.
rngseeddefault 123; random seed.
replacedefault TRUE; see sample for the random sampling; Available when method = "rarefying".
Returns
None; rarefied dataset.
Examples
\donttest{
m1$rarefy_samples(sample.size = min(m1$sample_sums()), replace = TRUE)
}
Method tidy_dataset()
Trim all the data in the microtable object to make taxa and samples consistent. So the results are intersections.
Usage
microtable$tidy_dataset(main_data = FALSE)
Arguments
main_datadefault FALSE; if TRUE, only basic data in microtable object is trimmed. Otherwise, all data,
including taxa_abund, alpha_diversity and beta_diversity, are all trimed.
Returns
None, object of microtable itself cleaned up.
Examples
m1$tidy_dataset(main_data = TRUE)
Method add_rownames2taxonomy()
Add the rownames of microtable$tax_table as its last column.
This is especially useful when the rownames of microtable$tax_table are required as a taxonomic level
for the taxonomic abundance calculation and biomarker idenfification.
Usage
microtable$add_rownames2taxonomy(use_name = "OTU")
Arguments
use_namedefault "OTU"; The column name used in the tax_table.
Returns
NULL, a new tax_table stored in the object.
Examples
\donttest{
m1$add_rownames2taxonomy()
}
Method sample_sums()
Sum the species number for each sample.
Usage
microtable$sample_sums()
Returns
species number of samples.
Examples
\donttest{
m1$sample_sums()
}
Method taxa_sums()
Sum the species number for each taxa.
Usage
microtable$taxa_sums()
Returns
species number of taxa.
Examples
\donttest{
m1$taxa_sums()
}
Method sample_names()
Show sample names.
Usage
microtable$sample_names()
Returns
sample names.
Examples
\donttest{
m1$sample_names()
}
Method taxa_names()
Show taxa names of tax_table.
Usage
microtable$taxa_names()
Returns
taxa names.
Examples
\donttest{
m1$taxa_names()
}
Method rename_taxa()
Rename the features, including the rownames of otu_table, rownames of tax_table, tip labels of phylo_tree and rep_fasta.
Usage
microtable$rename_taxa(newname_prefix = "ASV_")
Arguments
newname_prefixdefault "ASV_"; the prefix of new names; new names will be newname_prefix + numbers according to the rownames order of otu_table.
Returns
None; renamed dataset.
Examples
\donttest{
m1$rename_taxa()
}
Method merge_samples()
Merge samples according to specific group to generate a new microtable.
Usage
microtable$merge_samples(use_group)
Arguments
use_groupthe group column in sample_table.
Returns
a new merged microtable object.
Examples
\donttest{
m1$merge_samples(use_group = "Group")
}
Method merge_taxa()
Merge taxa according to specific taxonomic rank to generate a new microtable.
Usage
microtable$merge_taxa(taxa = "Genus")
Arguments
taxadefault "Genus"; the specific rank in tax_table.
Returns
a new merged microtable object.
Examples
\donttest{
m1$merge_taxa(taxa = "Genus")
}
Method save_table()
Save each basic data in microtable object as local file.
Usage
microtable$save_table(dirpath = "basic_files", sep = ",", ...)
Arguments
dirpathdefault "basic_files"; directory to save the tables, phylogenetic tree and sequences in microtable object. It will be created if not found.
sepdefault ","; the field separator string, used to save tables. Same with sep parameter in write.table function.
default ',' correspond to the file name suffix 'csv'. The option '\t' correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'.
...parameters passed to write.table.
Examples
\dontrun{
m1$save_table()
}
Method cal_abund()
Calculate the taxonomic abundance at each taxonomic level or selected levels.
Usage
microtable$cal_abund(
select_cols = NULL,
rel = TRUE,
merge_by = "|",
split_group = FALSE,
split_by = "&&",
split_column = NULL
)
Arguments
select_colsdefault NULL; numeric vector or character vector of colnames of microtable$tax_table;
applied to select columns to merge and calculate abundances according to ordered hierarchical levels.
This is very useful if there are commented columns or some columns with multiple structure that cannot be used directly.
reldefault TRUE; if TRUE, relative abundance is used; if FALSE, absolute abundance (i.e. raw values) will be summed.
merge_bydefault "|"; the symbol to merge and concatenate taxonomic names of different levels.
split_groupdefault FALSE; if TRUE, split the rows to multiple rows according to one or more columns in tax_table.
Very useful when multiple mapping information exist.
split_bydefault "&&"; Separator delimiting collapsed values; only useful when split_group = TRUE;
see sep parameter in separate_rows function of tidyr package.
split_columndefault NULL; character vector or list; only useful when split_group = TRUE; character vector:
fixed column or columns used for the splitting in tax_table for each abundance calculation;
list: containing more character vectors to assign the column names to each calculation, such as list(c("Phylum"), c("Phylum", "Class")).
Returns
taxa_abund list in object.
Examples
\donttest{
m1$cal_abund()
}
Method save_abund()
Save taxonomic abundance as local file.
Usage
microtable$save_abund(
dirpath = "taxa_abund",
merge_all = FALSE,
rm_un = FALSE,
rm_pattern = "__$",
sep = ",",
...
)
Arguments
dirpathdefault "taxa_abund"; directory to save the taxonomic abundance files. It will be created if not found.
merge_alldefault FALSE; Whether merge all tables into one. The merged file format is generally called 'mpa' style.
rm_undefault FALSE; Whether remove unclassified taxa in which the name ends with '__' generally.
rm_patterndefault "__$"; The pattern searched through the merged taxonomic names. See also pattern parameter in grepl function.
Only available when rm_un = TRUE. The default "__$" means removing the names end with '__'.
sepdefault ","; the field separator string. Same with sep parameter in write.table function.
default ',' correspond to the file name suffix 'csv'. The option '\t' correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'.
...parameters passed to write.table.
Examples
\dontrun{
m1$save_abund(dirpath = "taxa_abund")
m1$save_abund(merge_all = TRUE, rm_un = TRUE, sep = "\t")
}
Method cal_alphadiv()
Calculate alpha diversity.
Usage
microtable$cal_alphadiv(measures = NULL, PD = FALSE)
Arguments
measuresdefault NULL; one or more indexes of c("Observed", "Coverage", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher", "PD");
If null, use all those measures. 'Shannon', 'Simpson' and 'InvSimpson' are calculated based on vegan::diversity function;
'Chao1' and 'ACE' depend on the function vegan::estimateR; 'PD' depends on the function picante::pd.
PDdefault FALSE; whether Faith's phylogenetic diversity should be calculated.
Returns
alpha_diversity stored in object.
Examples
\donttest{
m1$cal_alphadiv(measures = NULL, PD = FALSE)
class(m1$alpha_diversity)
}
Method save_alphadiv()
Save alpha diversity table to the computer.
Usage
microtable$save_alphadiv(dirpath = "alpha_diversity")
Arguments
dirpathdefault "alpha_diversity"; directory name to save the alpha_diversity.csv file.
Method cal_betadiv()
Calculate beta diversity, including Bray-Curtis, Jaccard, and UniFrac.
See An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035> and Lozupone et al. (2005) <doi:10.1128/AEM.71.12.8228–8235.2005>.
Usage
microtable$cal_betadiv(method = NULL, unifrac = FALSE, binary = FALSE, ...)
Arguments
methoddefault NULL; a character vector with one or more elements; If default, "bray" and "jaccard" will be used;
see vegdist function and method parameter in vegan package.
unifracdefault FALSE; whether UniFrac index should be calculated.
binarydefault FALSE; TRUE is used for jaccard and unweighted unifrac; optional for other indexes.
...parameters passed to vegdist function.
Returns
beta_diversity list stored in object.
Examples
\donttest{
m1$cal_betadiv(unifrac = FALSE)
class(m1$beta_diversity)
}
Method save_betadiv()
Save beta diversity matrix to the computer.
Usage
microtable$save_betadiv(dirpath = "beta_diversity")
Arguments
dirpathdefault "beta_diversity"; directory name to save the beta diversity matrix files.
Method print()
Print the microtable object.
Usage
microtable$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
microtable$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `microtable$new`
## ------------------------------------------------
data(otu_table_16S)
data(taxonomy_table_16S)
data(sample_info_16S)
data(phylo_tree_16S)
m1 <- microtable$new(otu_table = otu_table_16S)
m1 <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S,
tax_table = taxonomy_table_16S, phylo_tree = phylo_tree_16S)
# trim the files in the dataset
m1$tidy_dataset()
## ------------------------------------------------
## Method `microtable$filter_pollution`
## ------------------------------------------------
m1$filter_pollution(taxa = c("mitochondria", "chloroplast"))
## ------------------------------------------------
## Method `microtable$filter_taxa`
## ------------------------------------------------
d1 <- clone(m1)
d1$filter_taxa(rel_abund = 0.0001, freq = 0.2)
## ------------------------------------------------
## Method `microtable$rarefy_samples`
## ------------------------------------------------
m1$rarefy_samples(sample.size = min(m1$sample_sums()), replace = TRUE)
## ------------------------------------------------
## Method `microtable$tidy_dataset`
## ------------------------------------------------
m1$tidy_dataset(main_data = TRUE)
## ------------------------------------------------
## Method `microtable$add_rownames2taxonomy`
## ------------------------------------------------
m1$add_rownames2taxonomy()
## ------------------------------------------------
## Method `microtable$sample_sums`
## ------------------------------------------------
m1$sample_sums()
## ------------------------------------------------
## Method `microtable$taxa_sums`
## ------------------------------------------------
m1$taxa_sums()
## ------------------------------------------------
## Method `microtable$sample_names`
## ------------------------------------------------
m1$sample_names()
## ------------------------------------------------
## Method `microtable$taxa_names`
## ------------------------------------------------
m1$taxa_names()
## ------------------------------------------------
## Method `microtable$rename_taxa`
## ------------------------------------------------
m1$rename_taxa()
## ------------------------------------------------
## Method `microtable$merge_samples`
## ------------------------------------------------
m1$merge_samples(use_group = "Group")
## ------------------------------------------------
## Method `microtable$merge_taxa`
## ------------------------------------------------
m1$merge_taxa(taxa = "Genus")
## ------------------------------------------------
## Method `microtable$save_table`
## ------------------------------------------------
## Not run:
m1$save_table()
## End(Not run)
## ------------------------------------------------
## Method `microtable$cal_abund`
## ------------------------------------------------
m1$cal_abund()
## ------------------------------------------------
## Method `microtable$save_abund`
## ------------------------------------------------
## Not run:
m1$save_abund(dirpath = "taxa_abund")
m1$save_abund(merge_all = TRUE, rm_un = TRUE, sep = "\t")
## End(Not run)
## ------------------------------------------------
## Method `microtable$cal_alphadiv`
## ------------------------------------------------
m1$cal_alphadiv(measures = NULL, PD = FALSE)
class(m1$alpha_diversity)
## ------------------------------------------------
## Method `microtable$cal_betadiv`
## ------------------------------------------------
m1$cal_betadiv(unifrac = FALSE)
class(m1$beta_diversity)
microeco documentation built on Nov. 18, 2023, 9:06 a.m.
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| microtable | R Documentation |
Create microtable object to store and manage all the basic files.
Description
This class is a wrapper for a series of operations on the input files and basic manipulations,
including microtable object creation, data trimming, data filtering, rarefaction based on Paul et al. (2013) <doi:10.1371/journal.pone.0061217>, taxonomic abundance calculation,
alpha and beta diversity calculation based on the An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035> and
Lozupone et al. (2005) <doi:10.1128/AEM.71.12.8228-8235.2005> and other basic operations.
Online tutorial: https://chiliubio.github.io/microeco_tutorial/
Download tutorial: https://github.com/ChiLiubio/microeco_tutorial/releases
Format
microtable.
Methods
Public methods
Method new()
Usage
microtable$new( otu_table, sample_table = NULL, tax_table = NULL, phylo_tree = NULL, rep_fasta = NULL, auto_tidy = FALSE )
Arguments
otu_tabledata.frame; The feature abundance table; rownames are features (e.g. OTUs/ASVs/species/genes); column names are samples.
sample_tabledata.frame; default NULL; The sample information table; rownames are samples; columns are sample metadata; If not provided, the function can generate a table automatically according to the sample names in otu_table.
tax_tabledata.frame; default NULL; The taxonomic information table; rownames are features; column names are taxonomic classes.
phylo_treephylo; default NULL; The phylogenetic tree; use
read.treefunction in ape package for input.rep_fastaDNAStringSetorlistformat; default NULL; The representative sequences; useread.fastafunction inseqinrpackage orreadDNAStringSetfunction inBiostringspackage for input.auto_tidydefault FALSE; Whether trim the files in the
microtableobject automatically; If TRUE, running the functions inmicrotableclass can invoke thetidy_datasetfunction automatically.
Returns
an object of class microtable with the following components:
sample_tableThe sample information table.
otu_tableThe feature table.
tax_tableThe taxonomic table.
phylo_treeThe phylogenetic tree.
rep_fastaThe representative sequence.
taxa_abunddefault NULL; use
cal_abundfunction to calculate.alpha_diversitydefault NULL; use
cal_alphadivfunction to calculate.beta_diversitydefault NULL; use
cal_betadivfunction to calculate.
Examples
data(otu_table_16S) data(taxonomy_table_16S) data(sample_info_16S) data(phylo_tree_16S) m1 <- microtable$new(otu_table = otu_table_16S) m1 <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S, tax_table = taxonomy_table_16S, phylo_tree = phylo_tree_16S) # trim the files in the dataset m1$tidy_dataset()
Method filter_pollution()
Filter the features considered pollution in microtable$tax_table.
This operation will remove any line of the microtable$tax_table containing any the word in taxa parameter regardless of word case.
Usage
microtable$filter_pollution(taxa = c("mitochondria", "chloroplast"))Arguments
taxadefault
c("mitochondria", "chloroplast"); filter mitochondria and chloroplast, or others as needed.
Returns
None
Examples
m1$filter_pollution(taxa = c("mitochondria", "chloroplast"))
Method filter_taxa()
Filter the feature with low abundance and/or low occurrence frequency.
Usage
microtable$filter_taxa(rel_abund = 0, freq = 1, include_lowest = TRUE)
Arguments
rel_abunddefault 0; the relative abundance threshold, such as 0.0001.
freqdefault 1; the occurrence frequency threshold. For example, the number 2 represents filtering the feature that occurs less than 2 times. A number smaller than 1 is also allowable. For instance, the number 0.1 represents filtering the feature that occurs in less than 10% samples.
include_lowestdefault TRUE; whether include the feature with the threshold.
Returns
None
Examples
\donttest{
d1 <- clone(m1)
d1$filter_taxa(rel_abund = 0.0001, freq = 0.2)
}
Method rarefy_samples()
Rarefy communities to make all samples have same feature number.
Usage
microtable$rarefy_samples(
method = c("rarefying", "SRS")[1],
sample.size = NULL,
rngseed = 123,
replace = TRUE
)Arguments
methoddefault c("rarefying", "SRS")[1]; "rarefying" represents the classical resampling like
rrarefyfunction ofveganpackage. "SRS" is scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) <DOI:10.7717/peerj.9593>.sample.sizedefault NULL; feature number, If not provided, use minimum number of all samples.
rngseeddefault 123; random seed.
replacedefault TRUE; see
samplefor the random sampling; Available whenmethod = "rarefying".
Returns
None; rarefied dataset.
Examples
\donttest{
m1$rarefy_samples(sample.size = min(m1$sample_sums()), replace = TRUE)
}
Method tidy_dataset()
Trim all the data in the microtable object to make taxa and samples consistent. So the results are intersections.
Usage
microtable$tidy_dataset(main_data = FALSE)
Arguments
main_datadefault FALSE; if TRUE, only basic data in
microtableobject is trimmed. Otherwise, all data, includingtaxa_abund,alpha_diversityandbeta_diversity, are all trimed.
Returns
None, object of microtable itself cleaned up.
Examples
m1$tidy_dataset(main_data = TRUE)
Method add_rownames2taxonomy()
Add the rownames of microtable$tax_table as its last column.
This is especially useful when the rownames of microtable$tax_table are required as a taxonomic level
for the taxonomic abundance calculation and biomarker idenfification.
Usage
microtable$add_rownames2taxonomy(use_name = "OTU")
Arguments
use_namedefault "OTU"; The column name used in the
tax_table.
Returns
NULL, a new tax_table stored in the object.
Examples
\donttest{
m1$add_rownames2taxonomy()
}
Method sample_sums()
Sum the species number for each sample.
Usage
microtable$sample_sums()
Returns
species number of samples.
Examples
\donttest{
m1$sample_sums()
}
Method taxa_sums()
Sum the species number for each taxa.
Usage
microtable$taxa_sums()
Returns
species number of taxa.
Examples
\donttest{
m1$taxa_sums()
}
Method sample_names()
Show sample names.
Usage
microtable$sample_names()
Returns
sample names.
Examples
\donttest{
m1$sample_names()
}
Method taxa_names()
Show taxa names of tax_table.
Usage
microtable$taxa_names()
Returns
taxa names.
Examples
\donttest{
m1$taxa_names()
}
Method rename_taxa()
Rename the features, including the rownames of otu_table, rownames of tax_table, tip labels of phylo_tree and rep_fasta.
Usage
microtable$rename_taxa(newname_prefix = "ASV_")
Arguments
newname_prefixdefault "ASV_"; the prefix of new names; new names will be newname_prefix + numbers according to the rownames order of
otu_table.
Returns
None; renamed dataset.
Examples
\donttest{
m1$rename_taxa()
}
Method merge_samples()
Merge samples according to specific group to generate a new microtable.
Usage
microtable$merge_samples(use_group)
Arguments
use_groupthe group column in
sample_table.
Returns
a new merged microtable object.
Examples
\donttest{
m1$merge_samples(use_group = "Group")
}
Method merge_taxa()
Merge taxa according to specific taxonomic rank to generate a new microtable.
Usage
microtable$merge_taxa(taxa = "Genus")
Arguments
taxadefault "Genus"; the specific rank in
tax_table.
Returns
a new merged microtable object.
Examples
\donttest{
m1$merge_taxa(taxa = "Genus")
}
Method save_table()
Save each basic data in microtable object as local file.
Usage
microtable$save_table(dirpath = "basic_files", sep = ",", ...)
Arguments
dirpathdefault "basic_files"; directory to save the tables, phylogenetic tree and sequences in microtable object. It will be created if not found.
sepdefault ","; the field separator string, used to save tables. Same with
sepparameter inwrite.tablefunction. default','correspond to the file name suffix 'csv'. The option'\t'correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'....parameters passed to
write.table.
Examples
\dontrun{
m1$save_table()
}
Method cal_abund()
Calculate the taxonomic abundance at each taxonomic level or selected levels.
Usage
microtable$cal_abund( select_cols = NULL, rel = TRUE, merge_by = "|", split_group = FALSE, split_by = "&&", split_column = NULL )
Arguments
select_colsdefault NULL; numeric vector or character vector of colnames of
microtable$tax_table; applied to select columns to merge and calculate abundances according to ordered hierarchical levels. This is very useful if there are commented columns or some columns with multiple structure that cannot be used directly.reldefault TRUE; if TRUE, relative abundance is used; if FALSE, absolute abundance (i.e. raw values) will be summed.
merge_bydefault "|"; the symbol to merge and concatenate taxonomic names of different levels.
split_groupdefault FALSE; if TRUE, split the rows to multiple rows according to one or more columns in
tax_table. Very useful when multiple mapping information exist.split_bydefault "&&"; Separator delimiting collapsed values; only useful when
split_group = TRUE; seesepparameter inseparate_rowsfunction of tidyr package.split_columndefault NULL; character vector or list; only useful when
split_group = TRUE; character vector: fixed column or columns used for the splitting in tax_table for each abundance calculation; list: containing more character vectors to assign the column names to each calculation, such as list(c("Phylum"), c("Phylum", "Class")).
Returns
taxa_abund list in object.
Examples
\donttest{
m1$cal_abund()
}
Method save_abund()
Save taxonomic abundance as local file.
Usage
microtable$save_abund( dirpath = "taxa_abund", merge_all = FALSE, rm_un = FALSE, rm_pattern = "__$", sep = ",", ... )
Arguments
dirpathdefault "taxa_abund"; directory to save the taxonomic abundance files. It will be created if not found.
merge_alldefault FALSE; Whether merge all tables into one. The merged file format is generally called 'mpa' style.
rm_undefault FALSE; Whether remove unclassified taxa in which the name ends with '__' generally.
rm_patterndefault "__$"; The pattern searched through the merged taxonomic names. See also
patternparameter ingreplfunction. Only available whenrm_un = TRUE. The default "__$" means removing the names end with '__'.sepdefault ","; the field separator string. Same with
sepparameter inwrite.tablefunction. default','correspond to the file name suffix 'csv'. The option'\t'correspond to the file name suffix 'tsv'. For other options, suffix are all 'txt'....parameters passed to
write.table.
Examples
\dontrun{
m1$save_abund(dirpath = "taxa_abund")
m1$save_abund(merge_all = TRUE, rm_un = TRUE, sep = "\t")
}
Method cal_alphadiv()
Calculate alpha diversity.
Usage
microtable$cal_alphadiv(measures = NULL, PD = FALSE)
Arguments
measuresdefault NULL; one or more indexes of
c("Observed", "Coverage", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher", "PD"); If null, use all those measures. 'Shannon', 'Simpson' and 'InvSimpson' are calculated based onvegan::diversityfunction; 'Chao1' and 'ACE' depend on the functionvegan::estimateR; 'PD' depends on the functionpicante::pd.PDdefault FALSE; whether Faith's phylogenetic diversity should be calculated.
Returns
alpha_diversity stored in object.
Examples
\donttest{
m1$cal_alphadiv(measures = NULL, PD = FALSE)
class(m1$alpha_diversity)
}
Method save_alphadiv()
Save alpha diversity table to the computer.
Usage
microtable$save_alphadiv(dirpath = "alpha_diversity")
Arguments
dirpathdefault "alpha_diversity"; directory name to save the alpha_diversity.csv file.
Method cal_betadiv()
Calculate beta diversity, including Bray-Curtis, Jaccard, and UniFrac. See An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035> and Lozupone et al. (2005) <doi:10.1128/AEM.71.12.8228–8235.2005>.
Usage
microtable$cal_betadiv(method = NULL, unifrac = FALSE, binary = FALSE, ...)
Arguments
methoddefault NULL; a character vector with one or more elements; If default, "bray" and "jaccard" will be used; see
vegdistfunction andmethodparameter inveganpackage.unifracdefault FALSE; whether UniFrac index should be calculated.
binarydefault FALSE; TRUE is used for jaccard and unweighted unifrac; optional for other indexes.
...parameters passed to
vegdistfunction.
Returns
beta_diversity list stored in object.
Examples
\donttest{
m1$cal_betadiv(unifrac = FALSE)
class(m1$beta_diversity)
}
Method save_betadiv()
Save beta diversity matrix to the computer.
Usage
microtable$save_betadiv(dirpath = "beta_diversity")
Arguments
dirpathdefault "beta_diversity"; directory name to save the beta diversity matrix files.
Method print()
Print the microtable object.
Usage
microtable$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
microtable$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `microtable$new`
## ------------------------------------------------
data(otu_table_16S)
data(taxonomy_table_16S)
data(sample_info_16S)
data(phylo_tree_16S)
m1 <- microtable$new(otu_table = otu_table_16S)
m1 <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S,
tax_table = taxonomy_table_16S, phylo_tree = phylo_tree_16S)
# trim the files in the dataset
m1$tidy_dataset()
## ------------------------------------------------
## Method `microtable$filter_pollution`
## ------------------------------------------------
m1$filter_pollution(taxa = c("mitochondria", "chloroplast"))
## ------------------------------------------------
## Method `microtable$filter_taxa`
## ------------------------------------------------
d1 <- clone(m1)
d1$filter_taxa(rel_abund = 0.0001, freq = 0.2)
## ------------------------------------------------
## Method `microtable$rarefy_samples`
## ------------------------------------------------
m1$rarefy_samples(sample.size = min(m1$sample_sums()), replace = TRUE)
## ------------------------------------------------
## Method `microtable$tidy_dataset`
## ------------------------------------------------
m1$tidy_dataset(main_data = TRUE)
## ------------------------------------------------
## Method `microtable$add_rownames2taxonomy`
## ------------------------------------------------
m1$add_rownames2taxonomy()
## ------------------------------------------------
## Method `microtable$sample_sums`
## ------------------------------------------------
m1$sample_sums()
## ------------------------------------------------
## Method `microtable$taxa_sums`
## ------------------------------------------------
m1$taxa_sums()
## ------------------------------------------------
## Method `microtable$sample_names`
## ------------------------------------------------
m1$sample_names()
## ------------------------------------------------
## Method `microtable$taxa_names`
## ------------------------------------------------
m1$taxa_names()
## ------------------------------------------------
## Method `microtable$rename_taxa`
## ------------------------------------------------
m1$rename_taxa()
## ------------------------------------------------
## Method `microtable$merge_samples`
## ------------------------------------------------
m1$merge_samples(use_group = "Group")
## ------------------------------------------------
## Method `microtable$merge_taxa`
## ------------------------------------------------
m1$merge_taxa(taxa = "Genus")
## ------------------------------------------------
## Method `microtable$save_table`
## ------------------------------------------------
## Not run:
m1$save_table()
## End(Not run)
## ------------------------------------------------
## Method `microtable$cal_abund`
## ------------------------------------------------
m1$cal_abund()
## ------------------------------------------------
## Method `microtable$save_abund`
## ------------------------------------------------
## Not run:
m1$save_abund(dirpath = "taxa_abund")
m1$save_abund(merge_all = TRUE, rm_un = TRUE, sep = "\t")
## End(Not run)
## ------------------------------------------------
## Method `microtable$cal_alphadiv`
## ------------------------------------------------
m1$cal_alphadiv(measures = NULL, PD = FALSE)
class(m1$alpha_diversity)
## ------------------------------------------------
## Method `microtable$cal_betadiv`
## ------------------------------------------------
m1$cal_betadiv(unifrac = FALSE)
class(m1$beta_diversity)
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