Microarray
The aim of this real use-case is to illustrate the power of bioScience against a real data set.
Dataset information
For this real case, the GSE26910 dataset from the NCBI GEO database is used. This microarray dataset for the organism Homo sapiens has a total of 3016 rows and 6 samples.
To generate this dataset, six samples of stroma surrounding invasive breast primary tumors were recruited. Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast carcinoma. Invasive breast cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, breast tumor-specific dysregulated stromal genes were observed to cluster breast cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value.
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 microarray dataset: Load the microarray dataset from the input file.
import bioscience as bs dataset = bs.load(path="/home/user/Desktop/GSE26910.csv", index_lengths = 0, index_gene=0, naFilter=False, head = 0, separator=";")
2. Preprocessing: For this dataset it is necessary to scale data. Furthermore, this dataset must be binarised because the Biclustering algorithm used requires the dataset to contain binary values.
import bioscience as bs bs.scale(dataset) 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): 3016 , 6 Execution mode: CPU Sequential MNC value: 2 MNR value: 2 Results: ======================== Biclusters found: 737505
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 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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)
18,31 s.
6,14 s.
1,38 s.
0,67 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.