def preprocess_otu_table(otu_sample_ids,
otu_table,
lineages,
coords_data,
coords_headers,
N=0):
"""Preprocess the OTU table to to generate the required data for the biplots
Input:
otu_sample_ids: sample identifiers for the otu_table
otu_table: contingency table
lineages: taxonomic assignments for the OTUs in the otu_table
coords_data: principal coordinates data where the taxa will be mapped
N: number of most prevalent taxa to keep, by default will use all
Output:
otu_coords: coordinates representing the N most prevalent taxa in otu_table
otu_table: N most prevalent OTUs from the input otu_table
otu_lineages: taxonomic assignments corresponding to the N most prevalent
OTUs
otu_prevalence: vector with the prevalence scores of the N highest values
lines: coords where the N most prevalent taxa will be positioned in the
biplot
"""
# return empty values if any of the taxa data is empty
if (otu_sample_ids == []) or (otu_table == array([])) or (lineages == []):
return [], [], [], [], ''
# this means there's only one or fewer rows in the contingency table
if len(otu_table) <= 1 or len(lineages) <= 1:
raise EmperorUnsupportedComputation, "Biplots are not supported for "+\
"contingency tables with one or fewer rows"
# if this element is a list take the first headers and coordinates
# both of these will be the master coordinates, i. e. where data is centered
if type(coords_data) == list and type(coords_headers) == list:
coords_data = coords_data[0]
coords_headers = coords_headers[0]
# re-arrange the otu table so it matches the order of the samples in the
# coordinates data & remove any sample that is not in the coordinates header
otu_sample_ids, otu_table = sort_taxa_table_by_pcoa_coords(
coords_headers, otu_table, otu_sample_ids)
# retrieve the prevalence and the coords prior the filtering
prevalence = get_taxa_prevalence(otu_table)
bi_plot_coords = get_taxa_coords(otu_table, coords_data)
o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence =\
extract_taxa_data(bi_plot_coords, otu_table, lineages, prevalence, N)
lines = '\n'.join(
make_biplot_scores_output({
'coord': o_otu_coords,
'lineages': o_otu_lineages
}))
return o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence, lines
def test_get_taxa_coords(self):
otu_table = np.array([[2, 0, 0, 1], [1, 1, 1, 1], [0, 2, 2, 1]], float)
sample_names = list('WXYZ')
otu_names = list('abc')
res = bp.get_taxa_coords(otu_table, [.4, .2, .1, .9])
otu_coords = range(3)
otu_coords[0] = .4 * 2 / 3 + .9 * 1 / 3
otu_coords[1] = .4 * 1 / 4 + .2 * 1 / 4 + .1 * 1 / 4 + .9 * 1 / 4
otu_coords[2] = .4 * 0 / 5 + .2 * 2 / 5 + .1 * 2 / 5 + .9 * 1 / 5
assert_almost_equal(res, otu_coords)
def preprocess_otu_table(otu_sample_ids, otu_table, lineages,
coords_data, coords_headers, N=0):
"""Preprocess the OTU table to to generate the required data for the biplots
Input:
otu_sample_ids: sample identifiers for the otu_table
otu_table: contingency table
lineages: taxonomic assignments for the OTUs in the otu_table
coords_data: principal coordinates data where the taxa will be mapped
N: number of most prevalent taxa to keep, by default will use all
Output:
otu_coords: coordinates representing the N most prevalent taxa in otu_table
otu_table: N most prevalent OTUs from the input otu_table
otu_lineages: taxonomic assignments corresponding to the N most prevalent
OTUs
otu_prevalence: vector with the prevalence scores of the N highest values
lines: coords where the N most prevalent taxa will be positioned in the
biplot
"""
# return empty values if any of the taxa data is empty
if (otu_sample_ids == []) or (otu_table == array([])) or (lineages == []):
return [], [], [], [], ''
# this means there's only one or fewer rows in the contingency table
if len(otu_table) <= 1 or len(lineages) <= 1:
raise EmperorUnsupportedComputation, "Biplots are not supported for "+\
"contingency tables with one or fewer rows"
# if this element is a list take the first headers and coordinates
# both of these will be the master coordinates, i. e. where data is centered
if type(coords_data) == list and type(coords_headers) == list:
coords_data = coords_data[0]
coords_headers = coords_headers[0]
# re-arrange the otu table so it matches the order of the samples in the
# coordinates data & remove any sample that is not in the coordinates header
otu_sample_ids, otu_table = sort_taxa_table_by_pcoa_coords(coords_headers,
otu_table, otu_sample_ids)
# retrieve the prevalence and the coords prior the filtering
prevalence = get_taxa_prevalence(otu_table)
bi_plot_coords = get_taxa_coords(otu_table, coords_data)
o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence =\
extract_taxa_data(bi_plot_coords, otu_table, lineages, prevalence, N)
lines = '\n'.join(make_biplot_scores_output({'coord': o_otu_coords,
'lineages': o_otu_lineages}))
return o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence, lines
def test_get_taxa_coords(self):
otu_table = np.array([ [2,0,0,1],
[1,1,1,1],
[0,2,2,1]],float)
sample_names = list('WXYZ')
otu_names = list('abc')
res = bp.get_taxa_coords(otu_table, [.4,.2,.1,.9])
otu_coords= range(3)
otu_coords[0] = .4*2/3 + .9*1/3
otu_coords[1] = .4*1/4 + .2*1/4 + .1*1/4 + .9*1/4
otu_coords[2] = .4*0/5 + .2*2/5 + .1*2/5 + .9*1/5
assert_almost_equal(res, otu_coords)
