Core observation data

The core “observation” data is stored in either sparse or dense matrices in the BIOM format file, and sparse matrix support is carried through on the R side via the Matrix package. The variables x1 and x2, assigned above from BIOM files, have similar (but not identical) data. They are small enough to observe directly as tables in standard output in an R session:

biom_data(x1)

## 5 x 6 Matrix of class "dgeMatrix"
##          Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
## GG_OTU_1       0       0       1       0       0       0
## GG_OTU_2       5       1       0       2       3       1
## GG_OTU_3       0       0       1       4       2       0
## GG_OTU_4       2       1       1       0       0       1
## GG_OTU_5       0       1       1       0       0       0

biom_data(x2)

## 5 x 6 sparse Matrix of class "dgCMatrix"
##          Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
## GG_OTU_1       .       .       1       .       .       .
## GG_OTU_2       5       1       .       2       3       1
## GG_OTU_3       .       .       1       4       .       2
## GG_OTU_4       2       1       1       .       .       1
## GG_OTU_5       .       1       1       .       .       .

As you can see above, x1 and x2 are represented in R by slightly different matrix classes, assigned automatically based on the data. However, most operations in R will understand this automatically and you should not have to worry about the precise matrix class. However, if the R function you are attempting to use is having a problem with these fancier classes, you can easily coerce the data to the simple, standard "matrix" class using the as function:

as(biom_data(x2), "matrix")

##          Sample1 Sample2 Sample3 Sample4 Sample5 Sample6
## GG_OTU_1       0       0       1       0       0       0
## GG_OTU_2       5       1       0       2       3       1
## GG_OTU_3       0       0       1       4       0       2
## GG_OTU_4       2       1       1       0       0       1
## GG_OTU_5       0       1       1       0       0       0

Observation Metadata

Observation metadata is metadata associated with the individual units being counted/recorded in a sample, as opposed to measurements of properties of the samples themselves. For microbiome census data, for instance, observation metadata is often a taxonomic classification and anything else that might be known about a particular OTU/species. For other types of data, it might be metadata known about a particular genome, gene family, pathway, etc. In this case, the observations are counts of OTUs and the metadata is taxonomic classification, if present. The absence of observation metadata is also supported, as we see for the minimal cases of x1 and x2, where we see instead.

observation_metadata(x1)

## NULL

observation_metadata(x2)

## NULL

observation_metadata(x3)

##            taxonomy1         taxonomy2              taxonomy3
## GG_OTU_1 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
## GG_OTU_2 k__Bacteria  p__Cyanobacteria    c__Nostocophycideae
## GG_OTU_3  k__Archaea  p__Euryarchaeota     c__Methanomicrobia
## GG_OTU_4 k__Bacteria     p__Firmicutes          c__Clostridia
## GG_OTU_5 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
##                     taxonomy4             taxonomy5         taxonomy6
## GG_OTU_1 o__Enterobacteriales f__Enterobacteriaceae    g__Escherichia
## GG_OTU_2        o__Nostocales        f__Nostocaceae g__Dolichospermum
## GG_OTU_3 o__Methanosarcinales f__Methanosarcinaceae g__Methanosarcina
## GG_OTU_4   o__Halanaerobiales   f__Halanaerobiaceae  g__Halanaerobium
## GG_OTU_5 o__Enterobacteriales f__Enterobacteriaceae    g__Escherichia
##                                taxonomy7
## GG_OTU_1                             s__
## GG_OTU_2                             s__
## GG_OTU_3                             s__
## GG_OTU_4 s__Halanaerobiumsaccharolyticum
## GG_OTU_5                             s__

observation_metadata(x4)[1:2, 1:3]

##            taxonomy1         taxonomy2              taxonomy3
## GG_OTU_1 k__Bacteria p__Proteobacteria c__Gammaproteobacteria
## GG_OTU_2 k__Bacteria  p__Cyanobacteria    c__Nostocophycideae

class(observation_metadata(x4))

## [1] "data.frame"

Sample Metadata

Similarly, we can access metadata – if available – that describe properties of the samples. We access this sample metadata using the sample_metadata function.

sample_metadata(x1)

## NULL

sample_metadata(x2)

## NULL

sample_metadata(x3)

##         BarcodeSequence  LinkerPrimerSequence BODY_SITE Description
## Sample1    CGCTTATCGAGA CATGCTGCCTCCCGTAGGAGT       gut   human gut
## Sample2    CATACCAGTAGC CATGCTGCCTCCCGTAGGAGT       gut   human gut
## Sample3    CTCTCTACCTGT CATGCTGCCTCCCGTAGGAGT       gut   human gut
## Sample4    CTCTCGGCCTGT CATGCTGCCTCCCGTAGGAGT      skin  human skin
## Sample5    CTCTCTACCAAT CATGCTGCCTCCCGTAGGAGT      skin  human skin
## Sample6    CTAACTACCAAT CATGCTGCCTCCCGTAGGAGT      skin  human skin

sample_metadata(x4)[1:2, 1:3]

##         BarcodeSequence  LinkerPrimerSequence BODY_SITE
## Sample1    CGCTTATCGAGA CATGCTGCCTCCCGTAGGAGT       gut
## Sample2    CATACCAGTAGC CATGCTGCCTCCCGTAGGAGT       gut

class(sample_metadata(x4))

## [1] "data.frame"

Plots

The data really is accessible to other R functions.

plot(biom_data(x4))

boxplot(as(biom_data(x4), "vector"))

heatmap(as(biom_data(x4), "matrix"))