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Description:

dataintegrator 0.1.0

DataIntegrator
Data Integration tools for genetic data in Python
Primary Functions

Liftover Genome Build
Given a input genome build version and a output genome build version, liftover the genome build of the input chr:pos pair to the desired version.

Add RsID
Add rsID according to chr and pos position in the data file.

Flip Strand to Forward
Step 1: Query dbSnp153 databse to get the two alleles of the forward strand according to chr and pos.
Step 2: Compare the two alleles with the input GWAS summary file, if all ATCG appears, flip the GWAS summary file using the A-T and C-G rule.

Align Effect allele
Input two gwas summary files, then use the first one as reference and make sure the second file has the same effect allele. If the effect allele is changed, the effect size will also be changed accordingly.

Dependencies
Before installing the DataIntegrator package, the following packages are required to be installed in advance:

pyliftover

pip install pyliftover

numpy

pip install numpy

pandas

pip install pandas

pyBigWig

pip install pyBigWig

Usage
To use the pacakge, please follow the steps below:

Install dependencies mentioned above

Install the package

pip install dataintegrator

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

In python, get started with the following steps

from dataintegrator import DataIntegrator 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)

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

Data Integrating work flow:

Functions Provided
1. Function to read data in formatted ways
Read Data
read_data( input_path, Chr_col_name, BP_col_name, SNP_col_name, A1_col_name, A2_col_name, EAF_col_name, Beta_col_name, Se_col_name, P_col_name, separate_by="\t")

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

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.DataFrame: return formatted data in the form of pandas DataFrame 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
filter_bi_allelic(df, rest=False)

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

df (pandas.DataFrame): 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.DataFrame: return filtered data in the form of pandas DataFrame.

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
deduplicate(df)

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

df (pandas.DataFrame): The data frame to be deduplicated.

Returns:

pandas.DataFrame: return filtered data in the form of pandas DataFrame.

Example:
deduplicated = di.deduplicate(df)

Sort by Chr and BP
sort_by_chr_bp(df)

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

df (pandas.DataFrame): the data to be sorted

Returns:

pandas.DataFrame: 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
query_data(df, link="http://hgdownload.soe.ucsc.edu/gbdb/hg38/snp/dbSnp153.bb")

Function to query required data from dbSnp153
Parameters:

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

Returns:

pandas.DataFrame: 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
save_obj(obj, name )

Function to save python data structure on disk
Parameters:

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
load_obj(obj_path )

Function to load saved python data structure from disk
Parameters:

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

Returns:

pandas.DataFrame: return complete information from dbSnp153 as a python dictionary

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

4. Functions to process data
Lift Over
lift_over(df, lo_dict, keep_all=False, inplace= False, comment=False)

Function to lift over genome build
Parameters:

df (pandas.DataFrame): 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.DataFrame: return the data being lifted over to the desired genome build

Use two tables to illustrate output
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
add_rsid(df, data, keep_all=False, inplace=False, show_comment=False, show_errors=False)
Function to query and add rs ID for rows missing rsIDs.
Parameters:

df (pandas.DataFrame): the data to be added rs_ids

data (python dictionary): the dictionary containing required info from dbSnp153

keep_all (boolean): value indicating whether the function should keep all rows in the original dataset. Default to False.

inplace (boolean): value indicating whether the function should replace the original rsID column with the new added_rsid column. Default to True.

show_comment (boolean): value indicating whether the function should add a column indicating the status of adding rsID. Default to False.

show_errors (boolean): value indicating whether the function will output a table containing rows that cannot be properly added rsIDs. Default to False.

"added" : missing rsID in orginal dataset. The Chr+BP key can be found in dbSNP153 and the rsID is successfully added
"same": the original dataset have the same rsID as dbSnp153. No need to modify or add.
"different": the original dataset have different rsID as compared to dbSnp153. Use dbSnp153 153 as reference to repalce the original.
"key not found" : The Chr+BP key in original dataset cannot be found in dbSnp153. Fill in NA value. Mark in the comment column.

Returns:

pandas.DataFrame: return the data being added rs_ids.

Example Call:
added_rsid = di.add_rsid(df, dbSnp153)

A few example output:
inplace = False, show_comment=False, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
added_rsid

1
438956

G
A
0.0021
-0.538
0.5802
0.3533
rs12445

X
704956
rs1234
T
C
0.0242
0.1685
0.2469
0.0843
rs1234

inplace = True, show_comment=False, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P

1
438956
rs12445
G
A
0.0021
-0.538
0.5802
0.3533

X
704956
rs1234
T
C
0.0242
0.1685
0.2469
0.0843

inplace = False, show_comment=True, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
added_rsid
comment

1
438956

G
A
0.0021
-0.538
0.5802
0.3533
rs12445
"added"

X
704956
rs1234
T
C
0.0242
0.1685
0.2469
0.0843
rs1234
"same"

check_errors=True

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
added_rsid
comment

1
438956

G
A
0.0021
-0.538
0.5802
0.3533

"key not found"

X
704956

T
C
0.0242
0.1685
0.2469
0.0843

"key not found"

Flip Strand
flip_strand( df, data, keep_all=False, inplace = False, show_comment=False, show_errors=False)

Function to flip the input data to forward strand
Parameters:

df (pandas.DataFrame): the data to be flipped to forward strand

data (python dictionary): the dictionary containing required info from dbSnp153

keep_all (boolean): value indicating whether the function should keep all rows in the original dataset. Default to False.

inplace (boolean): value indicating whether the function should replace the original A1 and A2 columns with the new_A1 and new_A2 columns. Default to False.

show_comment (boolean): value indicating whether the function should add a column indicating the status of flipping strand. Default to False.

show_errors (boolean): value indicating whether the function will output a table containing rows where strand cannot be properly flipped. Default to False.

"flipped" : The Chr+BP key can be found in dbSNP153 and the strand is successfully flipped.
"same": the original dataset uses the same strand as dbSnp153. No need to modify or add.
"different": the original data set and its correspondence in dbSnp153 show completely different strand patter that cannot be flipped (e.g. T/C vs. C/A)
"dbSnp153: Indel" : the A1 and A2 in dbSnp153 correponds to the Chr+BP in the processed data set contain Indel
"key not found" : The Chr+BP key in original dataset cannot be found in dbSnp153. Fill in NA value. Mark in the comment column.

Returns:

pandas.DataFrame: return the data being flipped to forward strand

Example:
flipped = di.flip_strand(df, dbSnp153)

A few examples:
inplace = False, show_comment=False, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
new_A1
new_A2

1
438956

G
A
0.0021
-0.538
0.5802
0.3533
A
G

X
704956
rs1234
T
C
0.0242
0.1685
0.2469
0.0843
C
T

inplace = True, show_comment=False, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P

1
438956

A
G
0.0021
-0.538
0.5802
0.3533

X
704956
rs1234
C
T
0.0242
0.1685
0.2469
0.0843

inplace = False, show_comment=True, keep_all=False

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
new_A1
new_A2
comment

1
438956

G
A
0.0021
-0.538
0.5802
0.3533
G
A
"kept original"

13
704956

T
C
0.0242
0.1235
0.2469
0.0673
C
T
"flipped"

22
568952

A
G
0.0267
0.7485
0.7869
0.0843

"key not found"

X
274586

C
T
0.0243
0.1357
0.2435
0.1243
G
T
"different"

check_errors=True

Chr
BP
SNP
A1
A2
EAF
Beta
Se
P
new_A1
new_A2
comment

22
568952

A
G
0.0267
0.7485
0.7869
0.0843

"key not found"

X
274586

C
T
0.0243
0.1357
0.2435
0.1243
G
T
"different"

Align Effect Allele and Effect Size between Two Datasets
align_effect_allele( reference, df, show_errors=False)

This function will align the effect allele of input data based on a reference data
Parameters:

reference (pandas.DataFrame): the reference table
df (pandas.DataFrame): 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.DataFrame: 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
save_data(output_path, df, name, save_format="gzip")

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

output_path (str): the path you want the data to be saved.
df (pandas.DataFrame): 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.DataFrame: return filtered data in the form of pandas DataFrame
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