RNA-Seq

The aim of this real use-case is to illustrate the power of bioScience against a real data set.

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Dataset information

For this real case, the GSE60450 dataset from the NCBI GEO database is used. This RNA-Seq dataset for the organism Homo sapiens has a total of 27179 rows and 12 samples.

To generate this dataset, comparison of gene expression in luminal and basal cells harvested from the mammary glands of virgin, 18.5 day pregnant and 2 day lactating mice (2 mice per stage) were examinated. To identify genes specifically expressed in lactating mammary glands, the gene expression profiles of luminal and basal cells from different developmental stages were compared.

Biclustering analysis

The aim of this analysis is to run a biclustering algorithm called BiBit to obtain those biclusters in which certain genes in the dataset show a similar pattern of behaviour in their gene expression values against a subset of samples.

Once this Biclustering algorithm has been executed, the execution times of each of the versions will be compared to demonstrate the power and usefulness of this library.

1. Load RNA-Seq dataset: Load the RNA-Seq dataset from the input file.

import bioscience as bs

dataset = bs.load(path="/home/user/Desktop/GSE246622.csv", index_lengths = 0, index_gene=0, naFilter=False, head = 0, separator=";")

2. Preprocessing: For this dataset it is not necessary to normalise the data as they are pre-processed before log2. However, this dataset must be binarised because the Biclustering algorithm used requires the dataset to contain binary values.

import bioscience as bs

bs.binarize(dataset)

3. Biclustering: In this phase, the BiBit Biclustering algorithm is run to generate useful knowledge from this dataset. This algorithm can be run in three modes: sequential, parallel CPU and multi-GPU.

For the sequential mode, the BiBit algorithm is processed sequentially on the CPU processors and for this mode to be executed a value of 1 must be specified for the mode parameter of the bibit function.

import bioscience as bs

listModels = bs.bibit(dataset, cMnr=2, cMnc=2, mode=1)

If the user wishes to process the algorithm in parallel on CPU processors, the mode parameter must contain a value of 2.

import bioscience as bs

listModels = bs.bibit(dataset, cMnr=2, cMnc=2, mode=2)

If the user wishes to execute it in a multi-GPU environment, the mode parameter must contain a value of 3. In addition, for environments with GPU devices, there is a deviceCount parameter in which the user indicates the number of GPU devices to be used for processing.

import bioscience as bs

listModels = bs.bibit(dataset, cMnr=2, cMnc=2, mode=3, deviceCount=2)

Once the Biclustering algorithm is executed, it is detected that a total of 149 biclusters are generated. The generated results are displayed on the terminal as follows:

Resume:
========================
Dataset size (rows,columns):  27179 , 12
Execution mode:  CPU Sequential
MNC value:  2
MNR value:  2
Results:
========================
Biclusters found:  3596

4. Results: It is possible to save the name of the genes of each bicluster generated by BiBit:

bs.saveGenes(path="/home/user/Desktop/", models=listModels, data=dataset)

A row in this file represents a bicluster, while the content of each row represents the genes present in each bicluster. Some of the biclusters contained in this file are the following:

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Execution times

The execution times of each of the versions are compared to demonstrate the power and usefulness of this library.

This experiment was conducted on a system equipped with an Intel Xeon E5-2686 v4 processor featuring 18 cores operating at 2.30 GHz, 32 GB of RAM, and 8 NVIDIA K80 12 GB graphics cards, each offering a combined total of 2496 CUDA cores.

Biclustering execution times in seconds.

Sequential mode	CPU Parallel	GPU Parallel (1 GPU)	GPU Parallel (2 GPU)
24156,49 s.	2021,16 s.	507,37 s.	251,45 s.

The run times shown in the table above are in seconds. As can be seen, the interest in the use of High Performance Computing (HPC) in the field of Bioinformatics is gaining more and more relevance due to the increasing volume of datasets and the complexity of data mining techniques to extract useful knowledge.