Python biom.Table方法代碼示例

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def denoise_single(demultiplexed_seqs: SingleLanePerSampleSingleEndFastqDirFmt,
                   trunc_len: int, trim_left: int = 0, max_ee: float = 2.0,
                   trunc_q: int = 2, pooling_method: str = 'independent',
                   chimera_method: str = 'consensus',
                   min_fold_parent_over_abundance: float = 1.0,
                   n_threads: int = 1, n_reads_learn: int = 1000000,
                   hashed_feature_ids: bool = True
                   ) -> (biom.Table, DNAIterator, qiime2.Metadata):
    return _denoise_single(
        demultiplexed_seqs=demultiplexed_seqs,
        trunc_len=trunc_len,
        trim_left=trim_left,
        max_ee=max_ee,
        trunc_q=trunc_q,
        max_len=0,
        pooling_method=pooling_method,
        chimera_method=chimera_method,
        min_fold_parent_over_abundance=min_fold_parent_over_abundance,
        n_threads=n_threads,
        n_reads_learn=n_reads_learn,
        hashed_feature_ids=hashed_feature_ids,
        homopolymer_gap_penalty='NULL',
        band_size='16') 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def denoise_pyro(demultiplexed_seqs: SingleLanePerSampleSingleEndFastqDirFmt,
                 trunc_len: int, trim_left: int = 0, max_ee: float = 2.0,
                 trunc_q: int = 2, max_len: int = 0,
                 pooling_method: str = 'independent',
                 chimera_method: str = 'consensus',
                 min_fold_parent_over_abundance: float = 1.0,
                 n_threads: int = 1, n_reads_learn: int = 250000,
                 hashed_feature_ids: bool = True
                 ) -> (biom.Table, DNAIterator, qiime2.Metadata):
    return _denoise_single(
        demultiplexed_seqs=demultiplexed_seqs,
        trunc_len=trunc_len,
        trim_left=trim_left,
        max_ee=max_ee,
        trunc_q=trunc_q,
        max_len=max_len,
        pooling_method=pooling_method,
        chimera_method=chimera_method,
        min_fold_parent_over_abundance=min_fold_parent_over_abundance,
        n_threads=n_threads,
        n_reads_learn=n_reads_learn,
        hashed_feature_ids=hashed_feature_ids,
        homopolymer_gap_penalty='-1',
        band_size='32') 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def pandas2biom(file_biom, table):
    """ Writes a Pandas.DataFrame into a biom file.

    Parameters
    ----------
    file_biom: str
        The filename of the BIOM file to be created.
    table: a Pandas.DataFrame
        The table that should be written as BIOM.

    Returns
    -------
    Nothing
    """
    bt = biom.Table(table.values,
                    observation_ids=list(map(str, table.index)),
                    sample_ids=table.columns)

    with biom_open(file_biom, 'w') as f:
        bt.to_hdf5(f, "example") 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def paired_heatmap(output_dir: str,
                   ranks: pd.DataFrame,
                   microbes_table: biom.Table,
                   metabolites_table: biom.Table,
                   features: str = None,
                   top_k_microbes: int = 2,
                   keep_top_samples: bool = True,
                   microbe_metadata: qiime2.CategoricalMetadataColumn = None,
                   normalize: str = 'log10',
                   color_palette: str = 'magma',
                   top_k_metabolites: int = 50,
                   level: int = -1,
                   row_center: bool = True) -> None:
    if microbe_metadata is not None:
        microbe_metadata = microbe_metadata.to_series()

    ranks = ranks.T

    if row_center:
        ranks = ranks - ranks.mean(axis=0)

    select_microbes, select_metabolites, hotmaps = paired_heatmaps(
        ranks, microbes_table, metabolites_table, microbe_metadata, features,
        top_k_microbes, top_k_metabolites, keep_top_samples, level, normalize,
        color_palette)

    hotmaps.savefig(join(output_dir, 'heatmap.pdf'), bbox_inches='tight')
    hotmaps.savefig(join(output_dir, 'heatmap.png'), bbox_inches='tight')
    select_microbes.to_csv(join(output_dir, 'select_microbes.tsv'), sep='\t')
    select_metabolites.to_csv(
        join(output_dir, 'select_metabolites.tsv'), sep='\t')

    index = join(TEMPLATES, 'index.html')
    q2templates.render(index, output_dir, context={
        'title': 'Paired Feature Abundance Heatmaps',
        'pdf_fp': 'heatmap.pdf',
        'png_fp': 'heatmap.png',
        'table1_fp': 'select_microbes.tsv',
        'download1_text': 'Download microbe abundances as TSV',
        'table2_fp': 'select_metabolites.tsv',
        'download2_text': 'Download top k metabolite abundances as TSV'}) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def setUp(self):
        _ranks = pd.DataFrame([[4.1, 1.3, 2.1], [0.1, 0.3, 0.2],
                               [2.2, 4.3, 3.2], [-6.3, -4.4, 2.1]],
                              index=pd.Index([c for c in 'ABCD'], name='id'),
                              columns=['m1', 'm2', 'm3']).T
        self.ranks = Artifact.import_data('FeatureData[Conditional]', _ranks)
        self.taxa = CategoricalMetadataColumn(pd.Series([
            'k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; '
            'o__Desulfobacterales; f__Desulfobulbaceae; g__; s__',
            'k__Bacteria; p__Cyanobacteria; c__Chloroplast; o__Streptophyta',
            'k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; '
            'o__Rickettsiales; f__mitochondria; g__Lardizabala; s__biternata',
            'k__Archaea; p__Euryarchaeota; c__Methanomicrobia; '
            'o__Methanosarcinales; f__Methanosarcinaceae; g__Methanosarcina'],
            index=pd.Index([c for c in 'ABCD'], name='feature-id'),
            name='Taxon'))
        metabolites = biom.Table(
            np.array([[9, 8, 2], [2, 1, 2], [9, 4, 5], [8, 8, 7]]),
            sample_ids=['s1', 's2', 's3'],
            observation_ids=['m1', 'm2', 'm3', 'm4'])
        self.metabolites = Artifact.import_data(
            'FeatureTable[Frequency]', metabolites)
        microbes = biom.Table(
            np.array([[1, 2, 3], [3, 6, 3], [1, 9, 9], [8, 8, 7]]),
            sample_ids=['s1', 's2', 's3'], observation_ids=[i for i in 'ABCD'])
        self.microbes = Artifact.import_data(
            'FeatureTable[Frequency]', microbes) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def setUp(self):
        np.random.seed(1)
        res = random_multimodal(
            num_microbes=8, num_metabolites=8, num_samples=150,
            latent_dim=2, sigmaQ=2,
            microbe_total=1000, metabolite_total=10000, seed=1
        )
        (self.microbes, self.metabolites, self.X, self.B,
         self.U, self.Ubias, self.V, self.Vbias) = res
        n, d1 = self.microbes.shape
        n, d2 = self.metabolites.shape

        self.microbes = biom.Table(self.microbes.values.T,
                                   self.microbes.columns,
                                   self.microbes.index)
        self.metabolites = biom.Table(self.metabolites.values.T,
                                      self.metabolites.columns,
                                      self.metabolites.index)
        U_ = np.hstack(
            (np.ones((self.U.shape[0], 1)), self.Ubias, self.U))
        V_ = np.vstack(
            (self.Vbias, np.ones((1, self.V.shape[1])), self.V))

        uv = U_ @ V_
        h = np.zeros((d1, 1))
        self.exp_ranks = clr_inv(np.hstack((h, uv))) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def setUp(self):

        omat = np.array([
            [104, 10, 2, 0, 0],
            [4, 100, 20, 0, 0],
            [0, 1, 0, 0, 4],
            [4, 0, 21, 0, 2],
            [40, 0, 2, 1, 39],
            [0, 0, 32, 10, 3],
            [59, 1, 0, 0, 3]
        ])
        mmat = np.array([
            [104, 1, 31, 0, 8],
            [4, 100, 20, 0, 0],
            [0, 8, 0, 0, 4],
            [0, 0, 2, 1, 2],
            [0, 0, 20, 10, 3],
            [0, 8, 0, 0, 4],
            [0, 0, 2, 10, 3],
            [0, 0, 320, 139, 3],
            [59, 9, 0, 0, 33]
        ]) * 10e6

        oids = list(map(lambda x: 'o'+str(x), np.arange(omat.shape[0])))
        mids = list(map(lambda x: 'm'+str(x), np.arange(mmat.shape[0])))
        sids = list(map(lambda x: 'm'+str(x), np.arange(mmat.shape[1])))

        self.otu_table = Table(omat, oids, sids)
        self.metabolite_table = Table(mmat, mids, sids)

        self.metadata = pd.DataFrame(
            {
                'testing': ['Train', 'Test', 'Train', 'Test', 'Train'],
                'bad': [True, False, True, False, True]
            }, index=sids
        ) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def format_barplots(table: biom.Table, normalize: bool):
    barplots = []
    barplots.append('DATASET_MULTIBAR')
    barplots.append('SEPARATOR TAB')
    barplots.append('DATASET_LABEL\tRelative Abundance')
    if normalize:
        table = table.norm(axis='observation', inplace=False)
    table = table.to_dataframe(dense=True)

    field_labels = list(table.columns)
    field_colors = values_to_colors(field_labels, 'husl').values()

    barplots.append('FIELD_COLORS\t'+'\t'.join(field_colors))
    barplots.append('FIELD_LABELS\t'+'\t'.join(field_labels))

    barplots.append('LEGEND_TITLE\tRelative Abundance')
    barplots.append('LEGEND_SHAPES\t'+'\t'.join(['1']*len(field_colors)))
    barplots.append('LEGEND_COLORS\t'+'\t'.join(field_colors))
    barplots.append('LEGEND_LABELS\t'+'\t'.join(field_labels))
    barplots.append('WIDTH\t100')

    barplots.append('DATA')
    table = table.reset_index()
    for idx in table.index:
        barplots.append('\t'.join(table.loc[idx].apply(str)))

    return '\n'.join(barplots) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def gibbs(table_fp: Table,
          mapping_fp: pd.DataFrame,
          output_dir: str,
          loo: bool,
          jobs: int,
          alpha1: float,
          alpha2: float,
          beta: float,
          source_rarefaction_depth: int,
          sink_rarefaction_depth: int,
          restarts: int,
          draws_per_restart: int,
          burnin: int,
          delay: int,
          per_sink_feature_assignments: bool,
          sample_with_replacement: bool,
          source_sink_column: str,
          source_column_value: str,
          sink_column_value: str,
          source_category_column: str):
    '''Gibb's sampler for Bayesian estimation of microbial sample sources.

    For details, see the project README file.
    '''
    # Create results directory. Click has already checked if it exists, and
    # failed if so.
    os.mkdir(output_dir)

    # Load the metadata file and feature table.
    sample_metadata = parse_sample_metadata(open(mapping_fp, 'U'))
    feature_table = biom_to_df(load_table(table_fp))

    # run the gibbs sampler helper function (same used for q2)
    results = gibbs_helper(feature_table, sample_metadata, loo, jobs,
                           alpha1, alpha2, beta, source_rarefaction_depth,
                           sink_rarefaction_depth, restarts, draws_per_restart,
                           burnin, delay, per_sink_feature_assignments,
                           sample_with_replacement, source_sink_column,
                           source_column_value, sink_column_value,
                           source_category_column)
    # import the results (will change based on per_sink_feature_assignments)
    if len(results) == 3:
        mpm, mps, fas = results
        # write the feature tables from fas
        for sink, fa in zip(mpm.columns, fas):
            fa.to_csv(os.path.join(output_dir, sink + '.feature_table.txt'),
                      sep='\t')
    else:
        # get the results (without fas)
        mpm, mps = results

    # Write results.
    mpm.to_csv(os.path.join(output_dir, 'mixing_proportions.txt'), sep='\t')
    mps.to_csv(os.path.join(output_dir, 'mixing_proportions_stds.txt'),
               sep='\t')

    # Plot contributions.
    fig, ax = plot_heatmap(mpm.T)
    fig.savefig(os.path.join(output_dir, 'mixing_proportions.pdf'), dpi=300) 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def _denoise_helper(biom_fp, track_fp, hashed_feature_ids):
    _check_featureless_table(biom_fp)
    with open(biom_fp) as fh:
        table = biom.Table.from_tsv(fh, None, None, None)

    df = pd.read_csv(track_fp, sep='\t', index_col=0)
    df.index.name = 'sample-id'
    df = df.rename(index=_filepath_to_sample)

    PASSED_FILTER = 'percentage of input passed filter'
    NON_CHIMERIC = 'percentage of input non-chimeric'

    round_cols = {PASSED_FILTER: 2, NON_CHIMERIC: 2}

    df[PASSED_FILTER] = df['filtered'] / df['input'] * 100
    df[NON_CHIMERIC] = df['non-chimeric'] / df['input'] * 100

    col_order = ['input', 'filtered', PASSED_FILTER, 'denoised',
                 'non-chimeric', NON_CHIMERIC]

    # only calculate percentage of input merged if paired end
    if 'merged' in df:
        MERGED = 'percentage of input merged'
        round_cols[MERGED] = 2
        df[MERGED] = df['merged'] / df['input'] * 100
        col_order.insert(4, 'merged')
        col_order.insert(5, MERGED)

    df = df[col_order]
    df.fillna(0, inplace=True)
    df = df.round(round_cols)
    metadata = qiime2.Metadata(df)

    # Currently the sample IDs in DADA2 are the file names. We make
    # them the sample id part of the filename here.
    sid_map = {id_: _filepath_to_sample(id_)
               for id_ in table.ids(axis='sample')}
    table.update_ids(sid_map, axis='sample', inplace=True)
    # The feature IDs in DADA2 are the sequences themselves.
    if hashed_feature_ids:
        # Make feature IDs the md5 sums of the sequences.
        fid_map = {id_: hashlib.md5(id_.encode('utf-8')).hexdigest()
                   for id_ in table.ids(axis='observation')}
        table.update_ids(fid_map, axis='observation', inplace=True)

        rep_sequences = DNAIterator((skbio.DNA(k, metadata={'id': v})
                                     for k, v in fid_map.items()))
    else:
        rep_sequences = DNAIterator(
            (skbio.DNA(id_, metadata={'id': id_})
             for id_ in table.ids(axis='observation')))
    return table, rep_sequences, metadata


# Since `denoise-single` and `denoise-pyro` are almost identical, break out
# the bulk of the functionality to this helper util. Typechecking is assumed
# to have occurred in the calling functions, this is primarily for making
# sure that DADA2 is able to do what it needs to do. 

# 需要導入模塊: import biom [as 別名]
# 或者: from biom import Table [as 別名]
def denoise_paired(demultiplexed_seqs: SingleLanePerSamplePairedEndFastqDirFmt,
                   trunc_len_f: int, trunc_len_r: int,
                   trim_left_f: int = 0, trim_left_r: int = 0,
                   max_ee_f: float = 2.0, max_ee_r: float = 2.0,
                   trunc_q: int = 2, pooling_method: str = 'independent',
                   chimera_method: str = 'consensus',
                   min_fold_parent_over_abundance: float = 1.0,
                   n_threads: int = 1, n_reads_learn: int = 1000000,
                   hashed_feature_ids: bool = True
                   ) -> (biom.Table, DNAIterator, qiime2.Metadata):
    _check_inputs(**locals())
    if trunc_len_f != 0 and trim_left_f >= trunc_len_f:
        raise ValueError("trim_left_f (%r) must be smaller than trunc_len_f"
                         " (%r)" % (trim_left_f, trunc_len_f))
    if trunc_len_r != 0 and trim_left_r >= trunc_len_r:
        raise ValueError("trim_left_r (%r) must be smaller than trunc_len_r"
                         " (%r)" % (trim_left_r, trunc_len_r))
    with tempfile.TemporaryDirectory() as temp_dir:
        tmp_forward = os.path.join(temp_dir, 'forward')
        tmp_reverse = os.path.join(temp_dir, 'reverse')
        biom_fp = os.path.join(temp_dir, 'output.tsv.biom')
        track_fp = os.path.join(temp_dir, 'track.tsv')
        filt_forward = os.path.join(temp_dir, 'filt_f')
        filt_reverse = os.path.join(temp_dir, 'filt_r')
        for fp in tmp_forward, tmp_reverse, filt_forward, filt_reverse:
            os.mkdir(fp)
        for rp, view in demultiplexed_seqs.sequences.iter_views(FastqGzFormat):
            fp = str(view)
            if 'R1_001.fastq' in rp.name:
                qiime2.util.duplicate(fp, os.path.join(tmp_forward, rp.name))
            elif 'R2_001.fastq' in rp.name:
                qiime2.util.duplicate(fp, os.path.join(tmp_reverse, rp.name))

        cmd = ['run_dada_paired.R',
               tmp_forward, tmp_reverse, biom_fp, track_fp, filt_forward,
               filt_reverse,
               str(trunc_len_f), str(trunc_len_r),
               str(trim_left_f), str(trim_left_r),
               str(max_ee_f), str(max_ee_r), str(trunc_q),
               str(pooling_method),
               str(chimera_method), str(min_fold_parent_over_abundance),
               str(n_threads), str(n_reads_learn)]
        try:
            run_commands([cmd])
        except subprocess.CalledProcessError as e:
            if e.returncode == 2:
                raise ValueError(
                    "No reads passed the filter. trunc_len_f (%r) or"
                    " trunc_len_r (%r) may be individually longer than"
                    " read lengths, or trunc_len_f + trunc_len_r may be"
                    " shorter than the length of the amplicon + 12"
                    " nucleotides (the length of the overlap). Alternatively,"
                    " other arguments (such as max_ee or trunc_q) may be"
                    " preventing reads from passing the filter."
                    % (trunc_len_f, trunc_len_r))
            else:
                raise Exception("An error was encountered while running DADA2"
                                " in R (return code %d), please inspect stdout"
                                " and stderr to learn more." % e.returncode)
        return _denoise_helper(biom_fp, track_fp, hashed_feature_ids)