Method new()
Usage
microtable$new(
otu_table,
sample_table = NULL,
tax_table = NULL,
phylo_tree = NULL,
rep_fasta = NULL,
auto_tidy = FALSE
)
Arguments
otu_tabledata.frame class; Feature abundance table; rownames are features (e.g. OTUs/ASVs/species/genes); column names are samples.
sample_table
default NULL; data.frame; The sample information table; rownames are samples; columns are sample metadata;
If not provided, the function can generate a table automatically according to the sample names in otu_table.
tax_table
default NULL; data.frame class; Taxonomic information table; rownames are features; column names are taxonomic ranks.
This can also be other hierarchical information tables, such as traits, genes, or metabolic pathways.
phylo_tree
default NULL; phylo class; Phylogenetic tree; It must be read with the read.tree function of ape package.
rep_fasta
default NULL; DNAStringSet, list or DNAbin class; Representative sequences of OTUs/ASVs.
The sequences should be read with the readDNAStringSet function of Biostrings package (DNAStringSet class),
read.fasta function of seqinr package (list class),
or read.FASTA function of ape package (DNAbin class).
auto_tidy
default FALSE; Whether tidy the data in the microtable object automatically.
If TRUE, the function can invoke the tidy_dataset function.
Returns
an object of microtable class with the following components:
sample_tableThe sample information table.
otu_tableThe feature table.
tax_tableThe taxonomic table.
phylo_treeThe phylogenetic tree.
rep_fastaThe sequences.
taxa_abunddefault NULL; use cal_abund function to calculate.
alpha_diversitydefault NULL; use cal_alphadiv function to calculate.
beta_diversitydefault NULL; use cal_betadiv function to calculate.
Examples
data(otu_table_16S)
data(taxonomy_table_16S)
data(sample_info_16S)
data(phylo_tree_16S)
m1 <- microtable$new(otu_table = otu_table_16S)
m1 <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S,
tax_table = taxonomy_table_16S, phylo_tree = phylo_tree_16S)
# trim each data in the object
m1$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$filter_pollution(taxa = c("mitochondria", "chloroplast"))
Arguments
taxadefault c("mitochondria", "chloroplast"); filter mitochondria and chloroplast, or others as needed.
Returns
updated microtable object
Examples
m1$filter_pollution(taxa = c("mitochondria", "chloroplast"))
Method filter_taxa()
Filter the features with low abundance and/or low occurrence frequency for otu_table or taxa_abund list.
Usage
microtable$filter_taxa(
rel_abund = 0,
freq = 1,
include_lowest = TRUE,
for_taxa_abund = FALSE
)
Arguments
rel_abunddefault 0; the relative abundance threshold, such as 0.0001.
freq
default 1; the occurrence frequency threshold.
For example, the number 2 represents filtering the feature that occurs less than 2 times.
A number smaller than 1 is also allowable.
For instance, the number 0.1 represents filtering the feature that occurs in less than 10% samples.
include_lowest
default TRUE; whether include the feature with the threshold.
for_taxa_abund
default FALSE; whether apply this function to taxa_abund list. FALSE means using this function for otu_table
Returns
updated microtable object
Examples
\donttest{
d1 <- clone(m1)
d1$filter_taxa(rel_abund = 0.0001, freq = 0.2)
}
Method rarefy_samples()
Rarefy communities to make all samples have same count number.
Usage
microtable$rarefy_samples(
method = c("rarefy", "SRS")[1],
sample.size = NULL,
...
)
Arguments
methoddefault c("rarefy", "SRS")[1]; "rarefy" represents the classical resampling like rrarefy function of vegan package.
"SRS" is scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) <DOI:10.7717/peerj.9593>.
sample.size
default NULL; libray size. If not provided, use the minimum number across all samples.
For "SRS" method, this parameter is passed to Cmin parameter of SRS function of SRS package.
...
parameters pass to norm function of trans_norm class.
Returns
rarefied microtable object.
Examples
\donttest{
m1$rarefy_samples(sample.size = min(m1$sample_sums()))
}
Method tidy_dataset()
Trim all the data in the microtable object to make taxa and samples consistent. The results are intersections across data.
Usage
microtable$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. The data in the object are tidied up.
If tax_table is in object, its row names are completely same with the row names of otu_table.
Examples
m1$tidy_dataset(main_data = TRUE)
Method add_rownames2taxonomy()
Add the row names of microtable$tax_table as its last column.
This is especially useful when the row names of microtable$tax_table are required as a taxonomic level
for the taxonomic abundance calculation and biomarker identification.
Usage
microtable$add_rownames2taxonomy(use_name = "OTU")
Arguments
use_namedefault "OTU"; The name of the column added in the tax_table.
Returns
tax_table updated in the object.
Examples
\donttest{
m1$add_rownames2taxonomy()
}
Method sample_sums()
Sum the abundance for each sample.
Usage
microtable$sample_sums()
Returns
abundance in each sample.
Examples
\donttest{
m1$sample_sums()
}
Method taxa_sums()
Sum the abundance for each taxon.
Usage
microtable$taxa_sums()
Returns
abundance in each taxon.
Examples
\donttest{
m1$taxa_sums()
}
Method sample_names()
Show the sample names.
Usage
microtable$sample_names()
Examples
\donttest{
m1$sample_names()
}
Method taxa_names()
Show the taxa names.
Usage
microtable$taxa_names()
Examples
\donttest{
m1$taxa_names()
}
Method rename_taxa()
Rename the features, including the row names of otu_table, row names of tax_table, tip labels of phylo_tree and names in rep_fasta.
Usage
microtable$rename_taxa(newname_prefix = "ASV_")
Arguments
newname_prefixdefault "ASV_"; the prefix of new names; new names will be newname_prefix + numbers according to the order of row names in otu_table.
Examples
\donttest{
m1$rename_taxa()
}
Method merge_samples()
Merge samples according to specific groups to generate a new microtable object.
Usage
microtable$merge_samples(group)
Arguments
groupa column name in sample_table of microtable object.
Returns
a merged microtable object.
Examples
\donttest{
m1$merge_samples("Group")
}
Method merge_taxa()
Merge taxa according to a specific taxonomic rank to generate a new microtable object.
Usage
microtable$merge_taxa(taxa = "Genus")
Arguments
taxadefault "Genus"; the specific rank in tax_table.
Returns
a 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.
sep
default ","; 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,
split_special_char = "&&"
)
Arguments
select_colsdefault NULL; numeric vector (column sequences) or character vector (column names of microtable$tax_table);
applied to select columns to calculate abundances according to ordered hierarchical levels.
This parameter is very useful when only part of the columns are needed to calculate abundances.
rel
default TRUE; if TRUE, relative abundance is used; if FALSE, absolute abundance (i.e. raw values) will be summed.
merge_by
default "|"; the symbol to merge and concatenate taxonomic names of different levels.
split_group
default FALSE; if TRUE, split the rows to multiple rows according to one or more columns in tax_table
when there is multiple mapping information.
split_by
default "&"; Separator delimiting collapsed values; only available when split_group = TRUE.
split_column
default NULL; one column name used for the splitting in tax_table for each abundance calculation;
only available when split_group = TRUE. If not provided, the function will split each column that containing the split_by character.
split_special_char
default "&&"; special character that will be used forcibly to split multiple mapping information in tax_table by default
no matter split_group setting.
For example, the hierarchical information of MetaCyc metabolic pathways from the file2meco package may have multiple ontology entries linked together.
In this case, the default parameters are automatically changed to split_group = TRUE and split_by = split_special_char, which is the default "&&".
If users have other multi-label data in tax_table, they can adjust the split_group, split_by, and split_column parameters accordingly.
Returns
taxa_abund list in object.
Examples
\donttest{
m1$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_all
default FALSE; Whether merge all tables into one. The merged file format is generally called 'mpa' style.
rm_un
default FALSE; Whether remove unclassified taxa in which the name ends with '__' generally.
rm_pattern
default "__$"; The pattern searched through the merged taxonomic names. See also pattern parameter in grepl function.
Only available when rm_un = TRUE. The default "__$" means removing the names end with '__'.
sep
default ","; 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 in c("Observed", "Coverage", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson", "Fisher", "Pielou");
The default NULL represents that all the measures are calculated. 'Shannon', 'Simpson' and 'InvSimpson' are calculated based on vegan::diversity function;
'Chao1' and 'ACE' depend on the function vegan::estimateR.
'Fisher' index relies on the function vegan::fisher.alpha.
"Observed" means the observed species number in a community, i.e. richness.
"Coverage" represents good's coverage. It is defined:
$$Coverage = 1 - \frac{f1}{n}$$
where n is the total abundance of a sample, and f1 is the number of singleton (species with abundance 1) in the sample.
"Pielou" denotes the Pielou evenness index. It is defined:
$$J = \frac{H'}{\ln(S)}$$
where H' is Shannon index, and S is the species number.
PD
default FALSE; whether Faith's phylogenetic diversity is calculated. The calculation depends on the function picante::pd.
Note that the phylogenetic tree (phylo_tree object in the data) is required for PD.
Returns
alpha_diversity stored in the object. The se.chao1 and se.ACE are the standard erros of Chao1 and ACE, respectively.
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$save_alphadiv(dirpath = "alpha_diversity")
Arguments
dirpathdefault "alpha_diversity"; directory name to save the alpha_diversity.csv file.
Method cal_betadiv()
Calculate beta diversity dissimilarity matrix, such as Bray-Curtis, Jaccard, and UniFrac.
See An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035> and Lozupone et al. (2005) <doi:10.1128/AEM.71.12.8228–8235.2005>.
Usage
microtable$cal_betadiv(
method = NULL,
unifrac = FALSE,
binary = FALSE,
force_jaccard_binary = TRUE,
...
)
Arguments
methoddefault NULL; a character vector with one or more elements; c("bray", "jaccard") is used when method = NULL;
See the method parameter in vegdist function for more available options, such as 'aitchison' and 'robust.aitchison'.
unifrac
default FALSE; whether UniFrac indexes (weighted and unweighted) are calculated. Phylogenetic tree is necessary when unifrac = TRUE.
binary
default FALSE; Whether convert abundance to binary data (presence/absence).
force_jaccard_binary
default TRUE; Whether forcibly convert abundance to binary data (presence/absence) when method = "jaccard".
The reason for this setting is that the Jaccard metric is commonly used for binary data.
If force_jaccard_binary = FALSE is set, the conversion will not be enforced, but will instead be based on the setting of the binary parameter.
...
parameters passed to vegdist function of vegan package.
Returns
beta_diversity list stored in the object.
Examples
\donttest{
m1$cal_betadiv(unifrac = FALSE)
class(m1$beta_diversity)
}
Method save_betadiv()
Save beta diversity matrix to the computer.
Usage
microtable$save_betadiv(dirpath = "beta_diversity")
Arguments
dirpathdefault "beta_diversity"; directory name to save the beta diversity matrix files.
Print the microtable object.
Method clone()
The objects of this class are cloneable with this method.
Usage
microtable$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.