Breastcancer NKI data

Create views with SQL and with pandas (performance study)

Performance study case

The SQLlite database breastCancerNKI has three tables

• erStatus

• probes

• expressionData

The goal is to compose one dataframe with expression data and ER status for the PTEN gene his probe/gene mapping and sample ER status information is needed for further vizualisation In this study case we create the dataframe using SQL and using pandas. The SQL appears te be around 240 times faster!

Source: https://bioinformatics-core-shared-training.github.io/shiny-bioinformatics/database-back-end

You can create this database yourself by running an R script https://github.com/fenna/BFVM22PROG1/blob/main/scripts/breastCancerNKI.R

In order to create the dataframe we need to load the tables into pandas dataframes. After that we will merge them with pandas. But we also will merge them directly on the SQL server. These two different operations will result in the same but the performance is different.

First connect to the database and query the data into a dataframes.

import sqlite3
import pandas as pd

conn = sqlite3.connect('data/breastCancerNKI.sqlite')
status = pd.read_sql_query('SELECT * FROM erStatus', conn)
probes = pd.read_sql_query('SELECT * FROM probes', conn)
expression = pd.read_sql_query('SELECT * FROM expressionData', conn)
expression.head()

	probe	sample	expression
0	Contig45645_RC	NKI_4	-0.215
1	Contig44916_RC	NKI_4	-0.207
2	D25272	NKI_4	-0.158
3	J00129	NKI_4	-0.819
4	Contig29982_RC	NKI_4	-0.267

status.head()

	sample	er
0	NKI_4	1
1	NKI_6	1
2	NKI_7	0
3	NKI_8	0
4	NKI_9	1

probes.head()

	probe	gene
0	Contig45645_RC	GREM2
1	Contig44916_RC	SUHW2
2	D25272	None
3	J00129	FGB
4	Contig29982_RC	SCARA5

Next we use SQL statements to merge the data. We use select from to retrieve and where to filter the proper gene and join to merge the data. The execution time is 9 milliseconds

Use SQL to create the required dataframe

import time

start_time = int(round(time.time() * 1000))

S = """select E.probe, E.expression, E.sample, P.gene, S.er
    from expressionData E join probes P on E.probe = P.probe 
    join erStatus as S on E.sample = S.sample WHERE P.gene = 'PTEN'"""
view = pd.read_sql_query(S, conn )

end_time = int(round(time.time() * 1000))
print (f"Execution time {end_time - start_time} millis")
view

Execution time 9 millis

	probe	expression	sample	gene	er
0	AF019083	-0.062	NKI_4	PTEN	1
1	AF019083	-0.072	NKI_6	PTEN	1
2	AF019083	0.064	NKI_7	PTEN	0
3	AF019083	0.132	NKI_8	PTEN	0
4	AF019083	0.018	NKI_9	PTEN	1
...	...	...	...	...	...
663	NM_000314	-0.284	NKI_398	PTEN	0
664	NM_000314	-0.363	NKI_401	PTEN	1
665	NM_000314	-0.058	NKI_402	PTEN	0
666	NM_000314	-0.059	NKI_403	PTEN	1
667	NM_000314	-0.059	NKI_404	PTEN	1

668 rows × 5 columns

conn.close()

Using pandas to achieve the same result can be done by the usage of the dataframes loaded from the SQL tables. The execution time now is 1941 seconds.

Use pandas to create the required dataframe¶

start_time = int(round(time.time() * 1000))

view = expression.merge(probes[probes.gene=='PTEN'], on='probe').merge(status, on='sample')

end_time = int(round(time.time() * 1000))
print (f"Execution time {end_time - start_time} millis")

view

Execution time 1941 millis

	probe	sample	expression	gene	er
0	AF019083	NKI_4	-0.062	PTEN	1
1	NM_000314	NKI_4	0.005	PTEN	1
2	AF019083	NKI_6	-0.072	PTEN	1
3	NM_000314	NKI_6	0.023	PTEN	1
4	AF019083	NKI_7	0.064	PTEN	0
...	...	...	...	...	...
663	NM_000314	NKI_402	-0.058	PTEN	0
664	AF019083	NKI_403	0.090	PTEN	1
665	NM_000314	NKI_403	-0.059	PTEN	1
666	AF019083	NKI_404	0.097	PTEN	1
667	NM_000314	NKI_404	-0.059	PTEN	1

Conclusion: Operations on the SQL server with SQL statement is much more faster!

668 rows × 5 columns