Function to construct an object of class siamcat-class

siamcat(..., feat=NULL, label=NULL, meta=NULL,
    phyloseq=NULL, validate=TRUE, verbose=3)

Arguments

...

additional arguments

feat

feature information for SIAMCAT (see details)

label

label information for SIAMCAT (see details)

meta

(optional) metadata information for SIAMCAT (see details)

phyloseq

(optional) a phyloseq object for the creation of an SIAMCAT object (see details)

validate

boolean, should the newly constructed SIAMCAT object be validated? defaults to TRUE (we strongly recommend against setting this parameter to FALSE)

verbose

control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Value

A new siamcat-class object

Details

Build siamcat-class objects from their components.

This functions creates a SIAMCAT object (see siamcat-class). In order to do so, the function needs

  • feat the feature information for SIAMCAT, should be either a matrix, a data.frame, or a otu_table-class. The columns should correspond to the different samples (e.g. patients) and the rows the different features (e.g. taxa). Columns and rows should be named.

  • meta metadata information for the different samples in the feature matrix. Metadata is optional for the SIAMCAT workflow. Should be either a data.frame (with the rownames corresponding to the sample names of the feature matrix) or an object of class sample_data-class

  • phyloseq Alternatively to supplying both feat and meta, SIAMCAT can also work with a phyloseq object containing an otu_table and other optional slots (like sample_data for meta-variables).

Notice: do supply either the feature information as matrix/data.frame/otu_table (and optionally metadata) or a phyloseq object, but not both.

The label information for SIAMCAT can take several forms:

  • metadata column: if there is metadata (either via meta or as sample_data in the phyloseq object), the label object can be created by taking the information in a specific metadata column. In order to do so, label should be the name of the column, and case should indicate which group(s) should be the positive group(s). A typical example could look like that:

    siamcat <- siamcat(feat=feat.matrix, meta=metadata, label='DiseaseState', case='CRC')

    for the construction of a label to predict CRC status (which is encoded in the column "DiseaseState" of the metadata). For more control (e.g. specific labels for plotting or specific control state), the label can also be created outside of the siamcat function using the create.label function.

  • named vector: the label can also be supplied as named vector which encodes the label either as characters (e.g. "Healthy" and "Diseased"), as factor, or numerically (e.g. -1 and 1). The vector must be named with the names of samples (corresponding to the samples in features). Also here, the information about the positive group(s) is needed via the case parameter. Internally, the vector is given to the create.label function.

  • label object: A label object can be created with the create.label function or by reading a dedicated label file with read.label.

Examples

# example with package data data("feat_crc_zeller", package="SIAMCAT") data("meta_crc_zeller", package="SIAMCAT") siamcat <- siamcat(feat=feat.crc.zeller, meta=meta.crc.zeller, label='Group', case='CRC')
#> + starting create.label
#> Label used as case: #> CRC #> Label used as control: #> CTR
#> + finished create.label.from.metadata in 0.001 s
#> + starting validate.data
#> +++ checking overlap between labels and features
#> + Keeping labels of 141 sample(s).
#> +++ checking sample number per class
#> +++ checking overlap between samples and metadata
#> + finished validate.data in 0.035 s