Biology subjects

Laha, N.

Publications and source records attributed to Laha, N..

5 recordsLinked to original sources

bioRxiv2024

De-biased sparse canonical correlation for identifying cancer-related trans-regulated genes

SO_SCPLOWUMMARYC_SCPLOWIn cancer multi-omic studies, identifying the effects of somatic copy number aberrations (CNA) on physically distal gene expressions (trans-associations) can potentially uncover genes critical for cancer pathogenesis. Sparse canonical correlation analysis (SCCA) has emerged as a promising method for identifying associations in high-dimensional settings, owing to its ability to aggregate weaker associations and its improved interpretability. Traditional SCCA lacks hypothesis testing capabilities, which are critical for controlling false discoveries. This limitation has recently been addressed through a bias correction technique that enables calibrated hypothesis testing. In this article, we leverage the theoretical advancements in de-biased SCCA to present a computationally efficient pipeline for multi-omics analysis. This pipeline identifies and tests associations between multi-omics data modalities in biomedical settings, such as the trans-effects of CNA on gene expression. We propose a detailed algorithm to choose the tuning parameters of de-biased SCCA. Applying this pipeline to data on estrogen receptor (ER)-associated CNAs and 10,756 gene expressions from 1,904 breast cancer patients in the METABRIC study, we identified 456 CNAs trans-associated with 256 genes. Among these, 5 genes were identified only through de-biased SCCA and not by the standard pairwise regression approach. Downstream analysis with the 256 genes revealed that these genes were overrepresented in pathways relevant to breast cancer.

genetics↗

bioRxiv2024

False discovery rate control: Moving beyond the Benjamini-Hochberg method

Bioinformatics studies often involve numerous simultaneous statistical tests, increasing the risk of false discoveries. To control the false discovery rate (FDR), these studies typically apply a statistical method called the Benjamini-Hochberg (BH) method. However, BH can be overly conservative, particularly in small-sample studies, and it does not take advantage of relevant structural information among the hypotheses, such as groupings. Group structures can arise, for example, when genomic features located in close proximity are co-regulated. Recent statistical developments have yielded group-adaptive BH methods that can leverage pre-existing group information to improve statistical power while maintaining FDR control. However, these methods remain underutilized in bioinformatics practice. In this study, we illustrate the practical application of group-adaptive BH methods using a previously published, moderately scaled microRNA (miRNA) dataset. Even under simple groupings based on chromosomal location, these methods identified more miRNAs with significantly deregulated expression (FDR-adjusted p-value < 0.05) compared to the traditional BH method. Most of the new discoveries are supported by prior literature and a related 2017 study. Although sensitivity to grouping strategy varied across methods, our control analysis indicated that, for most methods, the additional detections may be attributable to the incorporation of group information. Our results highlight the potential of specialized BH methods for controlling the FDR in omics studies with pre-defined group structures, and motivate further evaluation of their generalizability across diverse datasets.

bioinformatics↗

bioRxiv2023

Discovery of new deregulated miRNAs in gingivo buccal carcinoma using Group Benjamini Hochberg method: a commentary on "A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers"

This formal comment is in response to "A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers" written by De Sarkar and colleagues in 2014. The above-mentioned paper found seven miRNAs to be significantly deregulated in 18 gingivo-buccal cancer samples. However, they suspected more miRNAs to be deregulated based on their exploratory statistical analysis. To control the false discovery rate (FDR), the authors used the Benjamini Hochberg (BH) method, which does not leverage any available biological information on the miRNAs. In this work, we show that some specialized versions of the BH method, which can exploit positional information on the miRNAs, can lead to seven more discoveries with this data. Specifically, we group the closely located miRNAs, and use the group Benjamini Hochberg (GBH) methods (Hu et al., 2010), which reportedly have more statistical power than the BH method (Liu et al., 2019). The whole transcriptome analysis of Sing et al. (2017) and previous literature on the miRNAs suggest that most of the newly discovered miRNAs play a role in oncogenesis. In particular, the newly discovered miRNAs include hsa-miR-1 and hsa-miR-21-5p, whose cancer-related activities are well-established. Our findings indicate that incorporating the GBH method into suitable microarray studies may potentially enhance scientific discoveries via the exploitation of additional biological information.

cancer biology↗

bioRxiv2020

A framework to efficiently smooth L1 penalties for linear regression

Penalized linear regression approaches that include an L1 term have become an important tool in statistical data analysis. One prominent example is the least absolute shrinkage and selection operator (Lasso), though the class of L1 penalized regression operators also includes the fused and graphical Lasso, the elastic net, etc. Although the L1 penalty makes their objective function convex, it is not differentiable everywhere, motivating the development of proximal gradient algorithms such as Fista, the current gold standard in the literature. In this work, we take a different approach based on smoothing in a fixed parameter setting (the problem size n and number of parameters p are fixed). The methodological contribution of our article is threefold: (1) We introduce a unified framework to compute closed-form smooth surrogates of a whole class of L1 penalized regression problems using Nesterov smoothing. The surrogates preserve the convexity of the original (unsmoothed) objective functions, are uniformly close to them, and have closed-form derivatives everywhere for efficient minimization via gradient descent; (2) We prove that the estimates obtained with the smooth surrogates can be made arbitrarily close to the ones of the original (unsmoothed) objective functions, and provide explicitly computable a priori error bounds on the accuracy of our estimates; (3) We propose an iterative algorithm to progressively smooth the L1 penalty which increases accuracy and is virtually free of tuning parameters. The proposed methodology is applicable to a large class of L1 penalized regression operators, including all the operators mentioned above. Although the resulting estimates are typically dense, sparseness can be enforced again via thresholding. Using simulation studies, we compare our framework to current gold standards such as Fista, glmnet, gLasso, etc. Our results suggest that our proposed smoothing framework provides predictions of equal or higher accuracy than the gold standards while keeping the aforementioned theoretical guarantees and having roughly the same asymptotic runtime scaling.

bioinformatics↗

bioRxiv2020

A fast and efficient smoothing approach to LASSO regression and an application in statistical genetics: polygenic risk scores for Chronic obstructive pulmonary disease (COPD)

High dimensional linear regression problems are often fitted using Lasso approaches. Although the Lasso objective function is convex, it is not differentiable everywhere, making the use of gradient descent methods for minimization not straightforward. To avoid this technical issue, we apply Nesterov smoothing to the original (unsmoothed) Lasso objective function. We introduce a closed-form smoothed Lasso which preserves the convexity of the Lasso function, is uniformly close to the unsmoothed Lasso, and allows us to obtain closed-form derivatives everywhere for efficient and fast minimization via gradient descent. Our simulation studies are focused on polygenic risk scores using genetic data from a genome-wide association study (GWAS) for chronic obstructive pulmonary disease (COPD). We compare accuracy and run-time of our approach to the current gold standard in the literature, the FISTA algorithm. Our results suggest that the proposed methodology provides estimates with equal or higher accuracy than the FISTA algorithm while having the same asymptotic runtime scaling. The proposed methodology is implemented in the R-package smoothedLasso, available on the Comprehensive R Archive Network (CRAN).

bioinformatics↗