Ejemplo n.º 1
0
    def test_preprocess_mapping_file(self):
        """Check correct preprocessing of metadata is done"""

        # test it concatenates columns together correctly
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB', 'Treatment&&DOB'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB',
            'Treatment&&DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_A)

        # test it has a different order in the concatenated columns i. e. the
        # value of DOB comes before the value of Treatment in the result
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB', 'DOB&&Treatment'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB',
            'DOB&&Treatment'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_B)

        # test it filter columns properly
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_C)

        # check it removes columns with unique values
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['SampleID', 'BarcodeSequence',
            'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description'],
            unique=True)
        self.assertEquals(out_headers, ['SampleID', 'LinkerPrimerSequence',
            'Treatment', 'DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_D)

        # check it removes columns where there is only one value
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['SampleID', 'BarcodeSequence',
            'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description'],
            single=True)
        self.assertEquals(out_headers,['SampleID', 'BarcodeSequence',
            'Treatment', 'DOB', 'Description'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_E)

        # keep only treatment concat treatment and DOB and remove all
        # categories with only one value and all with unique values for field
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'Treatment&&DOB'],
            unique=True, single=True)
        self.assertEquals(out_headers, ['SampleID', 'Treatment',
            'Treatment&&DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_F)

        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB'], clones=3)
        self.assertEquals(out_data, MAPPING_FILE_DATA_DUPLICATED)
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB'])
Ejemplo n.º 2
0
    def test_preprocess_mapping_file(self):
        """Check correct preprocessing of metadata is done"""

        # test it concatenates columns together correctly
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB', 'Treatment&&DOB'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB',
            'Treatment&&DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_A)

        # test it has a different order in the concatenated columns i. e. the
        # value of DOB comes before the value of Treatment in the result
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB', 'DOB&&Treatment'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB',
            'DOB&&Treatment'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_B)

        # test it filter columns properly
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment'])
        self.assertEquals(out_headers, ['SampleID', 'Treatment'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_C)

        # check it removes columns with unique values
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['SampleID', 'BarcodeSequence',
            'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description'],
            unique=True)
        self.assertEquals(out_headers, ['SampleID', 'LinkerPrimerSequence',
            'Treatment', 'DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_D)

        # check it removes columns where there is only one value
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['SampleID', 'BarcodeSequence',
            'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description'],
            single=True)
        self.assertEquals(out_headers,['SampleID', 'BarcodeSequence',
            'Treatment', 'DOB', 'Description'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_E)

        # keep only treatment concat treatment and DOB and remove all
        # categories with only one value and all with unique values for field
        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'Treatment&&DOB'],
            unique=True, single=True)
        self.assertEquals(out_headers, ['SampleID', 'Treatment',
            'Treatment&&DOB'])
        self.assertEquals(out_data, MAPPING_FILE_DATA_CAT_F)

        out_data, out_headers = preprocess_mapping_file(self.mapping_file_data,
            self.mapping_file_headers, ['Treatment', 'DOB'], clones=3)
        self.assertEquals(out_data, MAPPING_FILE_DATA_DUPLICATED)
        self.assertEquals(out_headers, ['SampleID', 'Treatment', 'DOB'])
Ejemplo n.º 3
0
    coords_headers, coords_data, coords_eigenvalues, coords_pct, coords_low,\
        coords_high, clones = preprocess_coords_file(coords_headers,coords_data,
        coords_eigenvalues, coords_pct, header, mapping_data, custom_axes,
        jackknifing_method=jackknifing_method, is_comparison=compare_plots,
        pct_variation_below_one=pct_variation_below_one)

    # process the otu table after processing the coordinates to get custom axes
    # (when available) or any other change that occurred to the coordinates
    otu_coords, otu_table, otu_lineages, otu_prevalence, lines =\
        preprocess_otu_table(otu_sample_ids, otu_table, lineages,
        coords_data, coords_headers, n_taxa_to_keep)

    # remove the columns in the mapping file that are not informative taking
    # into account the header names that were already authorized to be used
    # and take care of concatenating the fields for the && merged columns
    mapping_data, header = preprocess_mapping_file(mapping_data, header,
        color_by_column_names, unique=not add_unique_columns, clones=clones)

    # create the output directory before creating any other output
    if not isdir(opts.output_dir):
        makedirs(opts.output_dir)

    fp_out = open(join(output_dir, 'index.html'),'w')
    fp_out.write(emperor_autograph+'\n')
    fp_out.write(EMPEROR_HEADER_HTML_STRING)

    # write the html file
    fp_out.write(format_mapping_file_to_js(mapping_data, header, header))

    # certain percents being explained cannot be displayed in the GUI
    try:
        fp_out.write(format_pcoa_to_js(coords_headers, coords_data,
Ejemplo n.º 4
0
    # axes is not in the coloring categories i. e. in the --colory_by categories
    coords_headers, coords_data, coords_eigenvalues, coords_pct, coords_low,\
        coords_high, clones = preprocess_coords_file(coords_headers,coords_data,
        coords_eigenvalues, coords_pct, header, mapping_data, custom_axes,
        jackknifing_method=jackknifing_method, is_comparison=compare_plots)

    # process the otu table after processing the coordinates to get custom axes
    # (when available) or any other change that occurred to the coordinates
    otu_coords, otu_table, otu_lineages, otu_prevalence, lines =\
        preprocess_otu_table(otu_sample_ids, otu_table, lineages,
        coords_data, coords_headers, n_taxa_to_keep)

    # remove the columns in the mapping file that are not informative taking
    # into account the header names that were already authorized to be used
    # and take care of concatenating the fields for the && merged columns
    mapping_data, header = preprocess_mapping_file(mapping_data, header,
        color_by_column_names, unique=not add_unique_columns, clones=clones)

    # create the output directory before creating any other output
    create_dir(opts.output_dir,False)

    fp_out = open(join(output_dir, 'index.html'),'w')
    fp_out.write(emperor_autograph+'\n')
    fp_out.write(EMPEROR_HEADER_HTML_STRING)

    # write the html file
    fp_out.write(format_mapping_file_to_js(mapping_data, header, header))

    # certain percents being explained cannot be displayed in the GUI
    try:
        fp_out.write(format_pcoa_to_js(coords_headers, coords_data,
            coords_eigenvalues, coords_pct, custom_axes, coords_low,
Ejemplo n.º 5
0
def main():
    #option_parser, opts, args = parse_command_line_parameters(**script_info)
    input_coords = args.input_coords
    map_fp = args.map_fp
    output_dir = args.output_dir
    
    color_by_column_names = None  #opts.color_by
    add_unique_columns = False      #opts.add_unique_columns
    custom_axes = None  #opts.custom_axes
    ignore_missing_samples = False      #opts.ignore_missing_samples
    missing_custom_axes_values = None   #opts.missing_custom_axes_values
    jackknifing_method = 'IQR'      #opts.ellipsoid_method
    master_pcoa = None      #opts.master_pcoa
    taxa_fp = None      #opts.taxa_fp
    n_taxa_to_keep = False      #opts.n_taxa_to_keep
    biplot_fp = None        #opts.biplot_fp
    add_vectors = [None, None]      #opts.add_vectors
    verbose_output = False      #opts.verbose
    number_of_axes = 10     #opts.number_of_axes
    compare_plots = False       #opts.compare_plots
    number_of_segments = 8      #opts.number_of_segments
    pct_variation_below_one = True  #opts.pct_variation_below_one

    # add some metadata to the output
    emperor_autograph = format_emperor_autograph(map_fp, input_coords, 'HTML')

    # verifying that the number of axes requested is greater than 3
    if number_of_axes < 3:
        print(('You need to plot at least 3 axes.'))

    # verifying that the number of segments is between the desired range
    if not (4 <= number_of_segments <= 14):
        print(('number_of_segments should be between 4 and 14.'))

    # append headernames that the script didn't find in the mapping file
    # according to different criteria to the following variables
    offending_fields = []
    non_numeric_categories = []

    serial_comparison = True

    # can't do averaged pcoa plots _and_ custom axes in the same plot
    if custom_axes is not None and isdir(input_coords):
        if custom_axes.count(',') > 0:
            print(('Jackknifed plots are limited to one custom '
                                 'axis, currently trying to use: %s. Make '
                                 'sure you use only one.' % custom_axes))

    # make sure the flag is not misunderstood from the command line interface
    if not isdir(input_coords) and compare_plots:
        print("Cannot use the '--compare_plots' flag unless the "
                            "input path is a directory.")

    # before creating any output, check correct parsing of the main input files
    #try:
    mapping_data, header, comments = parse_mapping_file(open(map_fp, 'U'))
    try:
        pass
    except:
        sys.exit(("The metadata mapping file '%s' does not seem "
                             "to be formatted correctly, verify the "
                             "formatting is QIIME compliant by using "
                             "validate_mapping_file.py") % map_fp)
            
    else:
        # use this set variable to make presence/absensce checks faster
        lookup_header = set(header)
        mapping_ids = {row[0] for row in mapping_data}

    # dir means jackknifing or coordinate comparison type of processing
    if isdir(input_coords):
        offending_coords_fp = []
        coords_headers = []
        coords_data = []
        coords_eigenvalues = []
        coords_pct = []

        coord_fps = guess_coordinates_files(input_coords)

        # QIIME generates folders of transformed coordinates for the specific
        # purpose of connecting all coordinates to a set of origin coordinates.
        # The name of this file is suffixed as _transformed_reference.txt
        trans_suf = '_transformed_reference.txt'
        transformed = [f for f in coord_fps if f.endswith(trans_suf)]

        # this could happen and we rather avoid this problem
        if len(coord_fps) == 0:

            print('Could not use any of the files in the input '
                                'directory.')

        # the master pcoa must be the first in the list of coordinates; however
        # if the visualization is not a jackknifed plot this gets ignored
        if master_pcoa and not compare_plots:
            if master_pcoa in coord_fps:  # remove it if duplicated
                coord_fps.remove(master_pcoa)
            coord_fps = [master_pcoa] + coord_fps  # prepend it to the list
        # passing a master file means that the comparison is not serial
        elif master_pcoa and compare_plots:
            serial_comparison = False

            # guarantee that the master is the first and is not repeated
            if master_pcoa in coord_fps:
                coord_fps.remove(master_pcoa)
                sorted_filenames = sort_comparison_filenames(coord_fps)
                coord_fps = [master_pcoa] + sorted_filenames

        elif master_pcoa is None and len(transformed):
            master_pcoa = transformed[0]
            serial_comparison = False

            # Note: the following steps are to guarantee consistency.
            # remove the master from the list and re-add it as a first element
            # the rest of the files must be sorted alphabetically so the result
            # will be: ['unifrac_transformed_reference.txt',
            # 'unifrac_transformed_q1.txt', 'unifrac_transformed_q2.txt'] etc
            coord_fps.remove(master_pcoa)
            coord_fps = [master_pcoa] + sort_comparison_filenames(coord_fps)

        for fp in coord_fps:
            try:
                parsed = parse_coords(open(fp, 'U'))
            except (ValueError, QiimeParseError):
                offending_coords_fp.append(fp)

                # do not add any of the data and move along
                continue
            else:
                # pack all the data correspondingly only if it was correctly
                # parsed
                coords_headers.append(parsed[0])
                coords_data.append(parsed[1])
                coords_eigenvalues.append(parsed[2])
                coords_pct.append(parsed[3])

        # in case there were files that couldn't be parsed
        if offending_coords_fp:
            errout = ', '.join(offending_coords_fp)
            sys.exit(("The following file(s): '%s' could not be "
                                 "parsed properly. Make sure the input folder "
                                 "only contains coordinates files.") % errout)

        # check all files contain the same sample identifiers by flattening the
        # list of available sample ids and returning the sample ids that are
        # in one of the sets of sample ids but not in the globablly shared ids
        _coords_headers = set(flatten(coords_headers))
        _per_file_missing = [_coords_headers - set(e) for e in coords_headers]
        non_shared_ids = set(flatten(_per_file_missing))
        if non_shared_ids:
            errout = ', '.join(non_shared_ids)
            sys.exit(("The following sample identifier(s): '%s' "
                                 "are not shared between all the files. The "
                                 "files used to make a jackknifed PCoA plot "
                                 "or coordinate comparison plot (procustes "
                                 "plot) must share all the same sample "
                                 "identifiers between each other.") % errout)

        # number of samples ids that are shared between coords and mapping
        # files
        sids_intersection = mapping_ids.intersection(_coords_headers)

        # sample ids that are not mapped but are in the coords
        sids_difference = _coords_headers.difference(mapping_ids)

        # used to perform different validations in the script, very similar for
        # the case where the input is not a directory
        number_intersected_sids = len(sids_intersection)
        required_number_of_sids = len(coords_headers[0])

    else:
        try:
            parsed = parse_coords(open(input_coords, 'U'))
        # this exception was noticed when there were letters in the coords file
        # other exeptions should be catched here; code will be updated then
        except (ValueError, QiimeParseError):
            sys.exit(("The PCoA file '%s' does not seem to be a "
                                 "coordinates formatted file, verify by "
                                 "manually inspecting the contents.") %
                                input_coords)
        else:
            coords_headers = parsed[0]
            coords_data = parsed[1]
            coords_eigenvalues = parsed[2]
            coords_pct = parsed[3]

        # number of samples ids that are shared between coords and mapping
        # files
        sids_intersection = mapping_ids.intersection(coords_headers)

        # sample ids that are not mapped but are in the coords
        sids_difference = set(coords_headers).difference(mapping_ids)

        number_intersected_sids = len(sids_intersection)
        required_number_of_sids = len(coords_headers)

    if taxa_fp:
        try:
            # This should really use BIOM's Table.from_tsv
            # for summarized tables the "otu_ids" are really the "lineages"
            parsed = parse_otu_table(open(taxa_fp, 'U'), count_map_f=float,
                                     remove_empty_rows=True)
        except ValueError(e):
            sys.exit(("There was a problem parsing the --taxa_fp: "
                                 "%s" % e.message))
        else:
            otu_sample_ids = parsed[0]
            lineages = parsed[1]
            otu_table = parsed[2]

        # make sure there are matching sample ids with the otu table
        if not sids_intersection.issuperset(otu_sample_ids):
            sys.exit("The sample identifiers in the OTU table must "
                                "have at least one match with the data in the "
                                "mapping file and with the coordinates file. "
                                "Verify you are using input files that belong "
                                "to the same dataset.")
        if len(lineages) <= 1:
            sys.exit("Contingency tables with one or fewer rows "
                                "are not supported, please try passing a "
                                "contingency table with more than one row.")
    else:
        # empty lists indicate that there was no taxa file passed in
        otu_sample_ids, lineages, otu_table = [], [], []

    # sample ids must be shared between files
    if number_intersected_sids <= 0:
        sys.exit('None of your sample identifiers match between the'
                            ' mapping file and the coordinates file. Verify '
                            'you are using a coordinates file and a mapping '
                            'file that belong to the same dataset.')

    # the intersection of the sample ids in the coords and the sample ids in
    # the mapping file must at the very least include all ids in the coords
    # file Otherwise it isn't valid; unless --ignore_missing_samples is set
    # True
    if number_intersected_sids != required_number_of_sids:
        if ignore_missing_samples:
            # keep only the samples that are mapped in the mapping file
            coords_headers, coords_data = keep_samples_from_pcoa_data(
                coords_headers, coords_data, sids_intersection)
        else:
            message = ("The metadata mapping file has fewer sample "
                       "identifiers than the coordinates file. Verify you are "
                       "using a mapping file that contains at least all the "
                       "samples contained in the coordinates file(s). You can "
                       "force the script to ignore these samples by passing "
                       "the '--ignore_missing_samples' flag.")

            if verbose_output:
                missing_ids = ', '.join(sids_difference)
                message += ' Offending sample identifier(s): %s.' % missing_ids

            sys.exit(message)

    # ignore samples that exist in the coords but not in the mapping file,
    # note: we're using sids_intersection so if --ignore_missing_samples is
    # enabled we account for unmapped coords, else the program will exit before
    # this point
    header, mapping_data = filter_mapping_file(mapping_data, header,
                                               sids_intersection,
                                               include_repeat_cols=True)

    # catch the errors that could occur when filling the mapping file values
    if missing_custom_axes_values:
        # the fact that this uses parse_metadata_state_descriptions makes the
        # following option '-x Category:7;PH:12' to work as well as the
        # script-interface-documented '-x Category:7 -x PH:12' option
        for val in missing_custom_axes_values:
            if ':' not in val:
                sys.exit("Not valid missing value for custom "
                                    "axes: %s" % val)
        _mcav = ';'.join(missing_custom_axes_values)
        try:
            mapping_data = fill_mapping_field_from_mapping_file(mapping_data,
                                                                header, _mcav)
        except AssertionError(e):
            print(e.message)
        except EmperorInputFilesError(e):
            print(e.message)

    # check that all the required columns exist in the metadata mapping file
    if color_by_column_names:
        color_by_column_names = color_by_column_names.split(',')

        # check for all the mapping fields
        for col in color_by_column_names:
            # for concatenated columns check each individual field
            parts = col.split('&&')
            offending_fields.extend(p for p in parts if p not in lookup_header)
    else:
        # if the user didn't specify the header names display everything
        color_by_column_names = [None]

    # extract a list of the custom axes provided and each element is numeric
    if custom_axes:
        custom_axes = custom_axes.strip().strip("'").strip('"').split(',')

        # the MetadataMap object makes some checks easier
        map_object = MetadataMap(mapping_file_to_dict(mapping_data, header),
                                 [])
        for axis in custom_axes:
            # append the field to the error queue that it belongs to
            if axis not in lookup_header:
                offending_fields.append(axis)
                break
            # make sure this value is in the mapping file
            elif axis not in color_by_column_names:
                color_by_column_names.append(axis)
        # perform only if the for loop does not call break
        else:
            # make sure all these axes are numeric
            for axis in custom_axes:
                if not map_object.isNumericCategory(axis):
                    non_numeric_categories.append(axis)

    # make multiple checks for the add_vectors option
    if add_vectors != [None, None]:
        add_vectors = add_vectors.split(',')
        # check there are at the most two categories specified for this option
        if len(add_vectors) > 2:
            print("The '--add_vectors' option can accept up to "
                                "two different fields from the mapping file; "
                                "currently trying to use %d (%s)." %
                                (len(add_vectors), ', '.join(add_vectors)))
        # make sure the field(s) exist
        for col in add_vectors:
            # concatenated fields are allowed now so check for each field
            if '&&' in col:
                for _col in col.split('&&'):
                    if _col not in lookup_header:
                        offending_fields.append(col)
                        break
                # only execute this block of code if all checked fields exist
                else:
                    # make sure that if it's going to be used for vector
                    # creation it gets used for coloring and map postprocessing
                    if col not in color_by_column_names:
                        color_by_column_names.append(col)
            # if it's a column without concatenations
            elif col not in lookup_header:
                offending_fields.append(col)
                break
            else:
                # check this vector value is in the color by category
                if col not in color_by_column_names:
                    color_by_column_names.append(col)
        # perform only if the for loop does not call break
        else:
            # check that the second category is all with numeric values
            if len(add_vectors) == 2:
                map_object = MetadataMap(mapping_file_to_dict(mapping_data,
                                                              header),
                                         [])
                # if it has non-numeric values add it to the list of offenders
                if not map_object.isNumericCategory(add_vectors[1]):
                    msg = add_vectors[1] + '(used in --add_vectors)'
                    non_numeric_categories.append(msg)
            else:
                add_vectors.append(None)

    # terminate the program for the cases where a mapping field was not found
    # or when a mapping field didn't meet the criteria of being numeric
    if offending_fields:
        sys.exit("Invalid field(s) '%s'; the valid field(s) are: "
                            "'%s'" % (', '.join(offending_fields),
                                      ', '.join(header)))
    if non_numeric_categories:
        sys.exit(("The following field(s): '%s' contain values "
                             "that are not numeric, hence not suitable for "
                             "'--custom_axes' nor for '--add_vectors'. Try "
                             "the '--missing_custom_axes_values' option to "
                             "fix these values." %
                             ', '.join(non_numeric_categories)))

    # process the coordinates file first, preventing the case where the custom
    # axes is not in the coloring categories i. e. in the --colory_by
    # categories
    preprocessed_coords = preprocess_coords_file(coords_headers, coords_data,
                                                 coords_eigenvalues,
                                                 coords_pct, header,
                                                 mapping_data, custom_axes,
                                                 jackknifing_method,
                                                 compare_plots,
                                                 pct_variation_below_one)
    coords_headers = preprocessed_coords[0]
    coords_data = preprocessed_coords[1]
    coords_eigenvalues = preprocessed_coords[2]
    coords_pct = preprocessed_coords[3]
    coords_low = preprocessed_coords[4]
    coords_high = preprocessed_coords[5]
    clones = preprocessed_coords[6]

    # process the otu table after processing the coordinates to get custom axes
    # (when available) or any other change that occurred to the coordinates
    preprocessed_otu_table = preprocess_otu_table(otu_sample_ids, otu_table,
                                                  lineages, coords_data,
                                                  coords_headers,
                                                  n_taxa_to_keep)
    otu_coords = preprocessed_otu_table[0]
    otu_table = preprocessed_otu_table[1]
    otu_lineages = preprocessed_otu_table[2]
    otu_prevalence = preprocessed_otu_table[3]
    lines = preprocessed_otu_table[4]

    # remove the columns in the mapping file that are not informative taking
    # into account the header names that were already authorized to be used
    # and take care of concatenating the fields for the && merged columns
    mapping_data, header = preprocess_mapping_file(mapping_data, header,
                                                   color_by_column_names,
                                                   not add_unique_columns,
                                                   clones=clones)

    # create the output directory before creating any other output
    if not isdir(opts.output_dir):
        makedirs(opts.output_dir)

    fp_out = open(join(output_dir, 'index.html'), 'w')
    fp_out.write(emperor_autograph+'\n')
    fp_out.write(EMPEROR_HEADER_HTML_STRING)

    # write the html file
    fp_out.write(format_mapping_file_to_js(mapping_data, header, header))

    # certain percents being explained cannot be displayed in the GUI
    try:
        fp_out.write(format_pcoa_to_js(coords_headers, coords_data,
                                       coords_eigenvalues, coords_pct,
                                       custom_axes, coords_low,
                                       coords_high,
                                       number_of_axes=number_of_axes,
                                       number_of_segments=number_of_segments))
    except EmperorLogicError(e):
        sys.exit(e.message)

    fp_out.write(format_taxa_to_js(otu_coords, otu_lineages, otu_prevalence))
    fp_out.write(format_vectors_to_js(mapping_data, header, coords_data,
                                      coords_headers, add_vectors[0],
                                      add_vectors[1]))
    fp_out.write(format_comparison_bars_to_js(coords_data, coords_headers,
                                              clones, serial_comparison))
    has_taxa = taxa_fp is not None
    has_input_coords = isdir(input_coords) and not compare_plots
    has_add_vectors = add_vectors != [None, None]
    has_clones = clones > 0
    fp_out.write(format_emperor_html_footer_string(has_taxa, has_input_coords,
                                                   has_add_vectors,
                                                   has_clones))
    fp_out.close()
    copy_support_files(output_dir)

    # write the biplot coords in the output file if a path is passed
    if biplot_fp and taxa_fp:
        if biplot_fp.endswith('/') or isdir(biplot_fp):
            print("Do not specify a path to a new (path ending "
                                "in a slash) or existing directory for "
                                "biplot_fp. The output file will be a "
                                "tab-delimited text file.")

        # make sure this file can be created
        try:
            fd = open(biplot_fp, 'w')
        except IOError:
            sys.exit("There was a problem creating the file with "
                                "the coordinates for the biplots (%s)."
                                % biplot_fp)
        else:
            fd.writelines(lines)
            fd.close()