Enums
Classification
Metric
- class onecodex.lib.enums.Metric(*values)
Metrics for taxonomic abundance data.
Taxonomic descendants refer to all taxa below a given taxon in the taxonomic hierarchy (superkingdom > phylum > class > order > family > genus > species > strain). For example, if a read maps to the species Escherichia coli, that read contributes to the readcount for E. coli as well as the
ReadcountWChildrenfor its genus (Escherichia), family (Enterobacteriaceae), and all ancestor taxa up the hierarchy.- Auto
Determine appropriate metric automatically (see
to_classification_df()).
- Readcount
Number of reads assigned to a given taxon.
- ReadcountWChildren
Read count for a taxon and all its taxonomic descendants.
- PropReadcount
Readcount as a proportion of total reads in the sample.
\[\frac{\text{readcount}}{\text{n\_reads\_total}}\]
- PropReadcountWChildren
ReadcountWChildren as a proportion of total reads in the sample.
- PropClassified
Readcount as a proportion of classified microbial reads.
\[\frac{\text{readcount}}{\text{n\_mapped\_microbial\_reads}}\]Where
n_mapped_microbial_reads = n_mapped_reads - n_host_reads - n_nonspecific_reads. Host reads are those mapping to detected host organisms (typically tax IDs 9606 for human and 10090 for mouse). Nonspecific reads are those mapping to tax IDs 1 (root) and 131567 (cellular organisms).
- PropClassifiedWChildren
ReadcountWChildren as a proportion of classified microbial reads.
- Abundance
Relative abundance estimate for a given taxon, computed by the classifier.
- AbundanceWChildren
Abundance for a taxon and all its descendants.
- NormalizedReadcount
Readcount normalized by the sum of readcounts for taxa at the specified rank. Values sum to 1.0 across taxa at that rank.
\[\frac{\text{readcount}}{\sum_{\text{taxa at rank}} \text{readcount}}\]
- NormalizedReadcountWChildren
ReadcountWChildren normalized by the sum of ReadcountWChildren for taxa at the specified rank. Represents the proportion of reads that classified to the specified rank or below. Values sum to 1.0 across taxa at that rank.
\[\frac{\text{readcount\_w\_children}}{\sum_{\text{taxa at rank}} \text{readcount\_w\_children}}\]
- Abundance = 'abundance'
- AbundanceWChildren = 'abundance_w_children'
- Auto = 'auto'
- NormalizedReadcount = 'normalized_readcount'
- NormalizedReadcountWChildren = 'normalized_readcount_w_children'
- PropClassified = 'prop_classified'
- PropClassifiedWChildren = 'prop_classified_w_children'
- PropReadcount = 'prop_readcount'
- PropReadcountWChildren = 'prop_readcount_w_children'
- Readcount = 'readcount'
- ReadcountWChildren = 'readcount_w_children'
- property display_name: str
Returns the human-readable name used for this metric. Useful for plotting and exports.
- property dtype
Returns the Python data type used to store this metric in a Numpy array or Pandas DataFrame.
- property includes_children: bool
Returns True if this metric aggregates over its own taxonomic descendants (children).
- property is_abundance_metric: bool
Returns True if this metric is an abundance metric (Abundance or AbundanceWChildren).
- property is_normalized: bool
Return true if the metric has been normalized (ie proportionalized) in some way.
- property results_key
Return the key used to fetch the raw value for this metric in Classifications.results.
Rank
Alpha & Beta Diversity
AlphaDiversityMetric
BetaDiversityMetric
- class onecodex.lib.enums.BetaDiversityMetric(*values)
Supported beta-diversity metrics.
- Aitchison = 'aitchison'
- BrayCurtis = 'braycurtis'
- CityBlock = 'cityblock'
- Euclidean = 'euclidean'
- Jaccard = 'jaccard'
- Manhattan = 'manhattan'
- UnweightedUnifrac = 'unweighted_unifrac'
- WeightedUnifrac = 'weighted_unifrac'