Heterogeneity in the cell population of cancer tissues poses many challenges in cancer diagnosis and treatment. Studying the heterogeneity in cell populations from gene expression measurement data in the context of cancer research is a problem of paramount importance. In addition, reducing the computation time of the algorithms that deal with high volumes of data has its obvious merits. Parallelizable models using Markov chain Monte Carlo methods are typically slow. This article presents a novel, computationally efficient, and parallelizable model to analyze heterogeneity in cancer tissues using GPUs. Because our model is parallelizable, the input data size does not affect the computation time much, provided the hardware resources are not exhausted. Our model uses qPCR (quantitative polymerase chain reaction) gene expression measurements to study heterogeneity in cancer tissue. We compute the cell proportion breakup by accelerating variational methods on a GPU. We test this model on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of our algorithm with those of Markov chain Monte Carlo and expectation maximization. Our new model is computationally less complex and faster than existing Bayesian models for estimating cancer tissue heterogeneity.

In a cancer study, the heterogeneous nature of a cell population creates a lot of challenges. Efficient determination of the compositional breakup of a cell population, from gene expression measurements, is critical to the success in a cancer study. This paper presents a new model for analyzing heterogeneity in cancer tissue using Markov chain Monte Carlo (MCMC) algorithms; we aim to compute the proportion wise breakup of the cell population on a GPU. We also show that the model computation time does not depend on the input data size, because the computation required to estimate the compositional breakup are parallelized. This model uses qPCR (quantitative polymerase chain reaction) gene expression data to determine compositional breakup in the heterogeneous cell population. We test this model on synthetic data and real-world data collected from fibroblasts. We also show how well this model scales to hundreds of gene expression data.

MOTIVATION: De novo mutations (i.e. newly occurring mutations) are a pre-dominant cause of sporadic dominant monogenic diseases and play a significant role in the genetics of complex disorders. De novo mutation studies also inform population genetics models and shed light on the biology of DNA replication and repair. Despite the broad interest, there is room for improvement with regard to the accuracy of de novo mutation calling. RESULTS: We designed novoCaller, a Bayesian variant calling algorithm that uses information from read-level data both in the pedigree and in unrelated samples. The method was extensively tested using large trio-sequencing studies, and it consistently achieved over 97% sensitivity. We applied the algorithm to 48 trio cases of suspected rare Mendelian disorders as part of the Brigham Genomic Medicine gene discovery initiative. Its application resulted in a significant reduction in the resources required for manual inspection and experimental validation of the calls. Three de novo variants were found in known genes associated with rare disorders, leading to rapid genetic diagnosis of the probands. Another 14 variants were found in genes that are likely to explain the phenotype, and could lead to novel disease-gene discovery. AVAILABILITY AND IMPLEMENTATION: Source code implemented in C++ and Python can be downloaded from https://github.com/bgm-cwg/novoCaller. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. METHODS: In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. RESULTS: The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). CONCLUSION: The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.

The diagnosis and treatment of cancer is made difficult by the heterogeneous nature of the cell population. Determining its compositional breakup from measurements of various measurable traits (such as gene expression measurements) is an important problem in the field of cancer diagnosis and treatment. In addition, the computational aspect of the problem also needs attention. The processing of the collected data must be done as efficiently as possible in terms of computational speed and memory requirements. The use of Markov chain Monte Carlo methods is time consuming, and hence, other methods need to be used for the analysis. In this paper, we develop a model for heterogeneous cancer tissue, which uses quantitative polymerase chain reaction gene expression data to determine the compositional breakup of cell populations in the heterogeneous tissue. We develop a fast algorithm for the model using variational methods and demonstrate its use on synthetic and real-world gene expression data collected from fibroblasts and compare the performance of the algorithm with other methods such as Markov chain Monte Carlo and expectation maximization.