Source code for methylprep.processing.postprocess

# Lib
import numpy as np
import pandas as pd
import os
import pickle
from pathlib import Path
import logging
# app
from ..utils import is_file_like
#from ..utils.progress_bar import * # context tqdm

os.environ['NUMEXPR_MAX_THREADS'] = "8" # suppresses warning

__all__ = ['calculate_beta_value', 'calculate_m_value', 'consolidate_values_for_sheet', 'consolidate_mouse_probes']

LOGGER = logging.getLogger(__name__)

def calculate_beta_value(methylated_noob, unmethylated_noob, offset=100):
    """ the ratio of (methylated_intensity / total_intensity)
    where total_intensity is (meth + unmeth + 100) -- to give a score in range of 0 to 1.0.
    minfi offset is 100 and sesame (default) offset is zero."""
    methylated = np.clip(methylated_noob, 1, None)
    unmethylated = np.clip(unmethylated_noob, 1, None)

    total_intensity = methylated + unmethylated + offset
    with np.errstate(all='raise'):
        intensity_ratio = np.true_divide(methylated, total_intensity)
    return intensity_ratio

def calculate_m_value(methylated_noob, unmethylated_noob, offset=0):
    """ the log(base 2) (1+meth / 1+unmeth) intensities (with a min clip intensity of 1 to avoid divide-by-zero-errors, like sesame)"""
    methylated = np.clip(methylated_noob, 1, None) + offset
    unmethylated = np.clip(unmethylated_noob, 1, None) + offset

    with np.errstate(all='raise'):
        intensity_ratio = np.true_divide(methylated, unmethylated)
    return np.log2(intensity_ratio)

def calculate_copy_number(methylated_noob, unmethylated_noob, offset=None):
    """ the log(base 2) of the combined (meth + unmeth AKA green and red) intensities """
    # Note: offset is a kwarg to match other calculate functions above, but there is no offset used in this function.
    total_intensity = methylated_noob + unmethylated_noob
    copy_number = np.log2(total_intensity)
    return copy_number

[docs]def consolidate_values_for_sheet(data_containers, postprocess_func_colname='beta_value', bit='float32', poobah=True, poobah_sig=0.05, exclude_rs=True): """ Transforms results into a single dataframe with all of the function values, with probe names in rows, and sample beta values for probes in columns. Input: data_containers -- the output of run_pipeline() is this, a list of data_containers. (a list of processed SampleDataContainer objects) Arguments for postprocess_func_colname: calculate_beta_value --> 'beta_value' calculate_m_value --> 'm_value' calculate_copy_number --> 'cm_value' note: these functions are hard-coded in as part of process_all() step. note: if run_pipeline included 'sesame' option, then quality mask is automatically applied to all pickle outputs, and saved as column in processed CSV. Options: bit (float16, float32, float64) -- change the default data type from float32 to another type to save disk space. float16 works fine, but might not be compatible with all numnpy/pandas functions, or with outside packages, so float32 is default. This is specified from methylprep process command line. poobah If true, filters by the poobah_pval column. (beta m_val pass True in for this.) data_container.quality_mask (True/False) If 'quality_mask' is present in df, True filters these probes from pickle output. exclude_rs as of v1.5.0 SigSet keeps snp ('rs') probes with other probe types (if qualityMask is false); need to separate them here before exporting to file.""" poobah_column = 'poobah_pval' quality_mask = 'quality_mask' for idx,sample in enumerate(data_containers): sample_id = f"{sample.sample.sentrix_id}_{sample.sample.sentrix_position}" if poobah == True and poobah_column in sample._SampleDataContainer__data_frame.columns: # remove all failed probes by replacing with NaN before building DF. sample._SampleDataContainer__data_frame.loc[sample._SampleDataContainer__data_frame[poobah_column] >= poobah_sig, postprocess_func_colname] = np.nan elif poobah == True and poobah_column not in sample._SampleDataContainer__data_frame.columns: LOGGER.warning('DEBUG: missing poobah') if sample.quality_mask == True and quality_mask in sample._SampleDataContainer__data_frame.columns: # blank there probes where quality_mask == 0 sample._SampleDataContainer__data_frame.loc[sample._SampleDataContainer__data_frame[quality_mask] == 0, postprocess_func_colname] = np.nan this_sample_values = sample._SampleDataContainer__data_frame[postprocess_func_colname] if exclude_rs: # dropping rows before exporting mask_snps = (sample._SampleDataContainer__data_frame.index.str.startswith('rs')) this_sample_values = this_sample_values.loc[ ~mask_snps ] if idx == 0: merged = pd.DataFrame(this_sample_values, columns=[postprocess_func_colname]) merged.rename(columns={postprocess_func_colname: sample_id}, inplace=True) continue merged = pd.concat([merged, this_sample_values], axis=1) merged.rename(columns={postprocess_func_colname: sample_id}, inplace=True) if bit != 'float32' and bit in ('float64','float16'): merged = merged.astype(bit) return merged
def one_sample_control_snp(container): """Creates the control_probes.pkl dataframe for export Unlike all the other postprocessing functions, this uses a lot of SampleDataContainer objects that get removed to save memory, so calling this immediately after preprocessing a sample so whole batches of data are not kept in memory. Input: one SampleDataContainer object. Returns: a pickled dataframe with non-CpG probe values. Control: red and green intensity SNP: beta values, based on uncorrected meth and unmeth intensity. Where 0-0.25 ~~ homogyzous-recessive 0.25--0.75 ~~ heterozygous 0.75--1.00 ~~ homozygous-dominant for each of 50-60 SNP locii on the genome. methylcheck can plot these to genotype samples. Notes: Each data container has a ctrl_red and ctrl_green dataframe. This shuffles them into a single dictionary of dataframes with keys as Sample_ID matching the meta_data. Where Sample_ID is: {container.sentrix_id_sentrix_position} and each dataframe contains both the ctrl_red and ctrl_green data. Column names will specify which channel (red or green) the data applies to. snp values are stored in container.snp_methylated.data_frame and container.snp_unmethylated.data_frame methylcheck.plot_controls requires these columns in output: ['Control_Type','Color','Extended_Type'] copy from container.ctl_man v1.5.0+ saves either RAW or NOOB meth/unmeth SNP values, depending on whether NOOB/dye was processed. """ RED = container.ctrl_red.rename(columns={'mean_value': 'Mean_Value_Red'})[['Mean_Value_Red']] GREEN = container.ctrl_green.rename(columns={'mean_value': 'Mean_Value_Green'})[['Mean_Value_Green']] CONTROL = RED.join(GREEN) CONTROL = pd.concat([CONTROL, container.ctl_man], axis=1) meth_col = 'Meth' if container.do_noob == False else 'noob_Meth' unmeth_col = 'Unmeth' if container.do_noob == False else 'noob_Unmeth' SNP = container.snp_methylated.rename(columns={meth_col: 'snp_meth'}) SNP_UNMETH = container.snp_unmethylated.rename(columns={unmeth_col: 'snp_unmeth'})[['snp_unmeth']] SNP = SNP.join(SNP_UNMETH) # below (snp-->beta) is analogous to: # SampleDataContainer._postprocess(input_dataframe, calculate_beta_value, 'beta_value') # except that it doesn't use the predefined noob columns. vectorized_func = np.vectorize(calculate_beta_value) SNP['snp_beta'] = vectorized_func( SNP['snp_meth'].values, SNP['snp_unmeth'].values, ) SNP = SNP[['snp_beta','snp_meth','snp_unmeth']] # space saving, but catches NaN bugs without breaking. if SNP[['snp_meth','snp_unmeth']].isna().sum().sum() == 0: SNP['snp_meth'] = SNP['snp_meth'].apply(pd.to_numeric, downcast='unsigned') SNP['snp_unmeth'] = SNP['snp_unmeth'].apply(pd.to_numeric, downcast='unsigned') if CONTROL[['Mean_Value_Green','Mean_Value_Red']].isna().sum().sum() == 0: CONTROL['Mean_Value_Green'] = CONTROL['Mean_Value_Green'].apply(pd.to_numeric, downcast='unsigned') CONTROL['Mean_Value_Red'] = CONTROL['Mean_Value_Red'].apply(pd.to_numeric, downcast='unsigned') CONTROL = CONTROL.join(SNP, how='outer').round({'snp_beta':3}) # finally, copy 'design' col from manifest, if exists if 'design' in probe_designs =[['design']] CONTROL = CONTROL.join(probe_designs, how='left') return CONTROL def consolidate_mouse_probes(data_containers, filename_or_fileobj, object_name='mouse_data_frame', poobah_column='poobah_pval', poobah_sig=0.05): """ these probes have 'Multi'|'Random' in `design` col of mouse manifest. used to populate 'mouse_probes.pkl'. pre v1.4.6: ILLUMINA_MOUSE specific probes (starting with 'rp' for repeat sequence or 'mu' for murine, 'uk' for unknown-experimental) stored as data_container.mouse_data_frame. saves as a dataframe just like controls: a dict of dataframes like processed.csv format, but only mouse probes. keys are sample_ids -- values are dataframes""" out = dict() for idx,sample in enumerate(data_containers): sample_id = f"{sample.sample.sentrix_id}_{sample.sample.sentrix_position}" data_frame = getattr(sample, object_name) data_frame = data_frame.round({'noob_meth':0, 'noob_unmeth':0, 'm_value':3, 'beta_value':3, 'cm_value':3, 'meth':0, 'unmeth':0, 'poobah_pval':3}) out[sample_id] = data_frame if is_file_like(filename_or_fileobj): pickle.dump(out, filename_or_fileobj) else: #except TypeError: # File must have a write attribute with open(filename_or_fileobj, 'wb') as f: pickle.dump(out, f) return def merge_batches(num_batches, data_dir, filepattern): """for each of the output pickle file types, this will merge the _1, _2, ..._X batches into a single file in data_dir. """ dfs = [] for num in range(num_batches): try: filename = f"{filepattern}_{num+1}.pkl" part = Path(data_dir, filename) if part.exists(): dfs.append( pd.read_pickle(part) ) except Exception as e: LOGGER.error(f'error merging batch {num} of {filepattern}') #tqdm.pandas() if dfs: # pipeline passes in all filenames, but not all exist dfs = pd.concat(dfs, axis='columns', join='inner') #.progress_apply(lambda x: x) outfile_name = Path(data_dir, f"{filepattern}.pkl")"{filepattern}: {dfs.shape}") dfs.to_pickle(str(outfile_name)) del dfs # save memory. # confirm file saved ok. if not Path(outfile_name).exists(): print("error saving consolidated file: {outfile_name}; use methylcheck.load() to merge the parts") return # now delete the parts for num in range(num_batches): filename = f"{filepattern}_{num+1}.pkl" part = Path(data_dir, filename) if part.exists(): part.unlink() # delete it