dataintegrator 0.0.4

Python data processing and formatting tools for gwas summary stats

DataIntegration

Data Integration tool set for genetic data

Main functions (create a index and link)

1. liftover genome build Add chr and pos columns according to hg18, hg19 or GRCh38 in the file and then output the other version chr and pos into the new gwas summary statistics. Additional we should check and flag failed chr:pos;
2. add rsID Add rsID according to chr and pos position in the file Add rsID and chr and pos columns according to marker name like: chr:pos:a1:a2
3. flip strand to forward According to chr and pos, you can get the two alleles of the forward strand, then compare with the input GWAS summary file, if all ATCG appears, we can flip the GWAS summary file using the A-T and C-G rule.
4. process alleles and effect size (if need to make all summary data using the same effect alleles for each SNP?); Input two gwas summary files, then use the first one as reference and make sure the second file has the same effect allele. If it needs to switch then effect size need to multiply by -1.
5. filter options, such as remove SNPs based on hwe pvalue or imputation quality or MAF, delete duplicates, non-biallelic SNPs, insertions, and deletions
6. sort by chr and BP in the output file
7. write out as txt.gz use Python to output gz directly and index file if possible

Dependencies

1. pyliftover
   • pip install pyliftover
2. numpy
   • pip install numpy
3. pandas
   • pip install pandas
4. pyBigWig
   • pip install pyBigWig

Usage

To use the pacakge, please follow the steps below:

1. Install dependencies mentioned above

2. Install the package

   • pip install dataintegrator

3. To use the query_data() function, you ahve to provide the link/path to the local dbSnp153.bb downloaded from the UCSC website; otherwise, the function will query the data online instead of querying data from local file (which will significantly reduce the run time).

4. In python, get started with the following steps

   •   from dataintegrator import DataIntegrater as di
     

   • Setting inital parameters:

     • input_path (the path of the data to be processed)
     • output_path (the path you want the processed result to be saved to)
     • input_format (e.g. "hg19")
     • output_format (e.g. "hg38")

   • Start using the provided functions, e.g.:

     •   input_path = "<path_to_your_data_file>.gz"
         df = di.read_data(input_path, '\t', "#chrom","pos", "rsids" ,"alt", "ref", "maf",    "beta", "sebeta", "pval")
         #print(df)
       

5. To view example calls of the main functions, clone this repository and see the .py files under the /examples directory.

6. Data Integrating work flow:

   • The general data processing pipe line includes the following five steps:
     • read_data >>> clean data >>> (query data from dbSnp153 if needed) >>> process data >>> dave data
   • Clean data with the following pipe lines:
     • filter bi-allelic cases >>> deduplicate data >>> sort data (recommended)

Functions Provided

1. Function to read data in formatted ways

Read Data

Description: This function reads the data to be processed and output it in a formatted way

Args:

• input_path (str): the complete or relative path of the input data
• Chr_col_name (str): the column name in the original data representing chromosome
• BP_col_name (str): the column name in the original data representing base pair position
• SNP_col_name (str): the column name in the original data representing rsID
• A1_col_name (str): the column name in the original data representing effect allele
• A2_col_name (str): the column name in the original data representing non-effect allele
• EAF_col_name (str): the column name in the original data represneting allele frequency for effect allele
• Beta_col_name (str): the column name in the original data represneting effect size for effect allele
• Se_col_name (str): the column name in the original data represneting standard error for effect size
• P_col_name (str): the column name in the original data represneting p-value
• separate_by (str): the delimiter of the original data, '\t' by default (tab separated)

Returns:

• pandas.Data.Frame: return formatted data in the form of pandas Data.Frame in the following ways:

Chr	BP	SNP	A1	A2	EAF	Beta	Se	P
1	438956	rs4596	G	A	0.0021	-0.538	0.5802	0.3533
X	704956	rs1234	T	C	0.0242	0.1685	0.2469	0.0843

Example Usage:

    input_path = "<path_to_your_data_file>.gz"
    df = di.read_data(input_path, '\t', "#chrom","pos", "rsids" ,"alt", "ref", "maf",    "beta", "sebeta", "pval")
    #print(df)

2. Functions to clean data for further processing

Filter bi-allelic

Description: Function to filter only bi-allelic cases in the data

Args:

• df (pandas.Data.Frame): The data frame to be filtered.
• rest (boolean): value indicating wether or not to keep (mark only) the non-bi-allelic cases. Default to False.

Returns:

• pandas.Data.Frame: return filtered data in the form of pandas Data.Frame.

Example:

    bi_allelic = di.filter_bi_allelic(df)
    
    # if you want to check the non-bi-allelic cases, use the following command
    bi_allelic = di.filter_bi_allelic(df, rest=True)

Deduplicate

Description: Function to drop rows in data containing dduplicate keys (Chr + BP)

Args:

• df (pandas.Data.Frame): The data frame to be deduplicated.

Returns:

• pandas.Data.Frame: return filtered data in the form of pandas Data.Frame.

Example:

    deduplicated = di.deduplicate(df)

Sort by Chr and BP

Description: Function to sort the data based on Chr and BP

Args:

• df (pandas.Data.Frame): the data to be sorted

Returns:

• pandas.Data.Frame: return the sorted data

Example:

    sorted_data = di.sort_by_chr_bp(df)

3. Functions to query data from dbSnp153 for further processing

Query UCSC Database for dbSNP153 info

Description: Function to query required data from dbSnp153

Args:

• df (pandas.Data.Frame): the data we want more info
• link (str): path or link of the '.bb' file of dbSnp153

Returns:

• pandas.Data.Frame: return complete information from dbSnp153 as a python dictionary

Example:

    link = "<path_to_your_dbSnp153_path>.bb"
    dbSnp153 = di.query_data(df, link) # This will usually take longer time

Save Object

Description: Function to save python data structure on disk

Args:

• obj (obj): the data structure/object to be saved on disk.
• name (str): the name for the obj to be saved as.

Returns:

• return nothing

Example:

    di.save_obj(dbSnp153, "obj/dbSnp153")

Load Object

Description: Function to load saved python data structure from disk

Args:

• name (str): the name of the saved obj on disk to be loaded

Returns:

• pandas.Data.Frame: return complete information from dbSnp153 as a python dictionary

Example:

    dbSnp153 = di.load_obj("obj/dbSnp153.pkl")

4. Functions to process data

Lift Over

Description: Function to lift over genome build

Args:

• df (pandas.Data.Frame): the data to be lifted over
• lo_dict (python dictionary): the lift over dictionary return from the create_lo function
• keep_unconvertible (boolean): if true, the function will keep and mark the rows that are not convertible. Default to False.
• keep_original_version (boolean): if true, the function will keep the Chr + BP of original genome build. Default to False.

Returns: pandas.Data.Frame: return the data being lifted over to the desired genome build

Example:

    input_format = "hg<**>"
    output_format = "hg<**>"
    lo = create_lo(input_format, output_format) # create chain file as reference for genome-build-lift-over.

    # drop unconvertible rows and keep only the result after lift over.
    lift_over = di.lift_over(df, lo)
    print(lift_over)

    # keep and mark rows that are not convertible
    lift_over = di.lift_over(df, lo, keep_unconvertible=True)

    # keep the original genome build version as separate columns in the data set
    lift_over = di.lift_over(df, lo, keep_orginal_version=True)

Add Rsid's

Descriptions: Function to query and add rs ID for rows missing rsIDs.

Args:

• df (pandas.Data.Frame): the data to be added rs_ids
• data (python dictionary): the dictionary containing required info from dbSnp153

Returns:

• pandas.Data.Frame: return the data being added rs_ids.

Example:

    added_rsid = di.add_rsid(df, dbSnp153)

Flip Strand

Descriptions: Function to flip the input data to forward strand

Args:

• df (pandas.Data.Frame): the data to be flipped to forward strand
• data (python dictionary): the dictionary containing required info from dbSnp153
• keep_unconvertible (boolean): if true, the function will keep and mark the rows that are not flipped. Default to False.

Returns:

• pandas.Data.Frame: return the data being flipped to forward strand

Example:

    flipped = di.flip_strand(df, dbSnp153)

    # If your want to keep and mark the columns that are unable to flip
    flipped = di.flip_strand(df, dbSnp153, keep_all=True)

Align Effect Allele and Effect Size between Two Datasets

Descriptions: this function will align the effect allele of input data based on a reference data

Args:

• reference (pandas.Data.Frame): the reference table
• df (pandas.Data.Frame): the data to be aligned
• check_error_rows (boolean): if true, the function will output the rows that cannot be aligned. Default to False.

Returns:

• pandas.Data.Frame: return the data with its effect allele being aligned with the reference table.

Example:

    reference_path = "<path_to_your_reference_data_file>.gz"
    reference_df = di.read_data(input_path, "chromosome","base_pair_location", "variant_id" ,"effect_allele", "other_allele", "effect_allele_frequency", "beta", "standard_error", "p_value") # for example
    aligned = align_effect_allele(reference_df, df) # be sure df and reference_df are using the same genome build, and both data are properlly cleaned!!

    # if you want to see rows that cannot be aligned
    aligned = align_effect_allele(reference_df, df, check_error_rows=True)

5. Functions to save result

Save Data

Description: function to save the processed data in the tsv form as a gz file

Args:

• output_path (str): the path you want the data to be saved.
• df (pandas.Data.Frame): the processed data to be saved.
• name (str): the output name of the data.
• save_format (str): the saving format. Choose between 'gzip' or 'csv'. Default to gz.

Returns: pandas.Data.Frame: return filtered data in the form of pandas Data.Frame

Example:

    output_path = "<path_to_your_output_directory>" # "result" for example
    di.save_data(output_path, aligned, "aligned")

    # if you want to save the file as csv
    di.save_data(output_path, aligned, "csv")

Functions to be Implemented

Insert/ Filter/ Delete

Create Tbi Index