skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Joint Grid Discretization for Biological Pattern Discovery
The complexity, dynamics, and scale of data acquired by modern biotechnology increasingly favor model-free computational methods that make minimal assumptions about underlying biological mechanisms. For example, single-cell transcriptome and proteome data have a throughput several orders more than bulk methods. Many model-free statistical methods for pattern discovery such as mutual information and chi-squared tests, however, require discrete data. Most discretization methods minimize squared errors for each variable independently, not necessarily retaining joint patterns. To address this issue, we present a joint grid discretization algorithm that preserves clusters in the original data. We evaluated this algorithm on simulated data to show its advantage over other methods in maintaining clusters as measured by the adjusted Rand index. We also show it promotes global functional patterns over independent patterns. On single-cell proteome and transcriptome of leukemia and healthy blood, joint grid discretization captured known protein-to-RNA regulatory relationships, while revealing previously unknown interactions. As such, the joint grid discretization is applicable as a data transformation step in associative, functional, and causal inference of molecular interactions fundamental to systems biology. The developed software is publicly available at https://cran.r-project.org/package=GridOnClusters  more » « less
Award ID(s):
1661331
PAR ID:
10236467
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings. The 11th ACM Int'l Conf on Bioinform, Comput Biol and Health Inform
Page Range / eLocation ID:
Article No.: 57
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Proceedings of the 14th Machine Learning in Computational Biology (MLCB) Meeting)
    Inference of a combinatorial function from multiple independent variables (parents) to a dependent variable (child) in a discrete space can be useful in detecting nonlinear relationships in biological systems. Popular conditional independency measures, heavily used in combinatorial inference, are often insensitive to the direction of functional dependency. To address this issue, we define multivariate and conditional functional chi-squared statistics. We also present an algorithm called CFDF for bivariate discrete function inference via an exclusive-effect strategy, in order to identify a best parent set for a given child. It requires each parent to make sufficient contribution beyond any marginal effect. Simulation studies suggest a marked advantage of our framework over alternatives. Applying the method to transcriptome data in genetically perturbed biological systems, we reproduced combinatorial gene interactions known in the literature. Most importantly, we identified combinatorial patterns from joint RNA and protein data to rebut a dispute on the founding principle of molecular biology. 
    more » « less
  2. ; ; (, Bioinformatics)
    Abstract MotivationComputer inference of biological mechanisms is increasingly approachable due to dynamically rich data sources such as single-cell genomics. Inferred molecular interactions can prioritize hypotheses for wet-lab experiments to expedite biological discovery. However, complex data often come with unwanted biological or technical variations, exposing biases over marginal distribution and sample size in current methods to favor spurious causal relationships. ResultsConsidering function direction and strength as evidence for causality, we present an adapted functional chi-squared test (AdpFunChisq) that rewards functional patterns over non-functional or independent patterns. On synthetic and three biology datasets, we demonstrate the advantages of AdpFunChisq over 10 methods on overcoming biases that give rise to wide fluctuations in the performance of alternative approaches. On single-cell multiomics data of multiple phenotype acute leukemia, we found that the T-cell surface glycoprotein CD3 delta chain may causally mediate specific genes in the viral carcinogenesis pathway. Using the causality-by-functionality principle, AdpFunChisq offers a viable option for robust causal inference in dynamical systems. Availability and implementationThe AdpFunChisq test is implemented in the R package ‘FunChisq’ (2.5.2 or above) at https://cran.r-project.org/package=FunChisq. All other source code along with pre-processed data is available at Code Ocean https://doi.org/10.24433/CO.2907738.v1 Supplementary informationSupplementary materials are available at Bioinformatics online. 
    more » « less
  3. ; ; (, Bioinformatics)
    Kelso, Janet (Ed.)
    Abstract Motivation Genetic or epigenetic events can rewire molecular networks to induce extraordinary phenotypical divergences. Among the many network rewiring approaches, no model-free statistical methods can differentiate gene-gene pattern changes not attributed to marginal changes. This may obscure fundamental rewiring from superficial changes. Results Here we introduce a model-free Sharma-Song test to determine if patterns differ in the second order, meaning that the deviation of the joint distribution from the product of marginal distributions is unequal across conditions. We prove an asymptotic chi-squared null distribution for the test statistic. Simulation studies demonstrate its advantage over alternative methods in detecting second-order differential patterns. Applying the test on three independent mammalian developmental transcriptome datasets, we report a lower frequency of co-expression network rewiring between human and mouse for the same tissue group than the frequency of rewiring between tissue groups within the same species. We also find secondorder differential patterns between microRNA promoters and genes contrasting cerebellum and liver development in mice. These patterns are enriched in the spliceosome pathway regulating tissue specificity. Complementary to previous mammalian comparative studies mostly driven by first-order effects, our findings contribute an understanding of system-wide second-order gene network rewiring within and across mammalian systems. Second-order differential patterns constitute evidence for fundamentally rewired biological circuitry due to evolution, environment, or disease. Availability The generic Sharma-Song test is available from the R package ‘DiffXTables’ at https://cran.r-project.org/package=DiffXTables. Other code and data are described in Methods. Supplementary information Supplementary data are available at Bioinformatics online. 
    more » « less
  4. ; ; ; ; (, American Society of Mechanical Engineers)
    Abstract Expanding on the insights from our initial investigation into railway accident patterns, this paper delves deeper into the predictive capabilities of machine learning to forecast potential accident trends in railway crossings. Focusing on critical factors such as “Highway User Position” and “Equipment Involved,” we integrate Kernel Ridge Regression (KRR) models tailored to distinct clusters, as well as a global model for the entire dataset. These models, trained on historical data, discern patterns and correlations that might elude traditional statistical methods. Our findings are compelling: certain clusters, despite limited data points, showcase remarkably Root Mean Squared Error (RMSE) values between predictions and real data, indicating superior model performance. However, certain clusters hint at potential overfitting, given the disparities between model predictions and actual data. Conversely, clusters with vast datasets underperform compared to the global model, suggesting intricate interactions within the data that might challenge the model’s capabilities. The performance nuances across clusters emphasize the value of specialized, cluster-specific models in capturing the intricacies of each dataset segment. This study underscores the efficacy of KRR in predicting future railway crossing incidents, fostering the implementation of data-driven strategies in public safety. 
    more » « less
  5. ; ; ; (, The Plant Journal)
    SUMMARY Cotton fibers are aerial trichoblasts that employ a highly polarized diffuse growth mechanism to emerge from the developing ovule epidermis. After executing a complicated morphogenetic program, the cells reach lengths over 2 cm and serve as the foundation of a multi‐billion‐dollar textile industry. Important traits such as fiber diameter, length, and strength are defined by the growth patterns and cell wall properties of individual cells. At present, the ability to engineer fiber traits is limited by our lack of understanding regarding the primary controls governing the rate, duration, and patterns of cell growth. To gain insights into the compartmentalized functions of proteins in cotton fiber cells, we developed a label‐free liquid chromatography mass spectrometry method for systems‐level analyses of fiber proteome. Purified fibers from a single locule were used to fractionate the fiber proteome into apoplast (APOT), membrane‐associated (p200), and crude cytosolic (s200) fractions. Subsequently, proteins were identified, and their localizations and potential functions were analyzed using combinations of size exclusion chromatography, statistical and bioinformatic analyses. This method had good coverage of the p200 and APOTfractions, the latter of which was dominated by proteins associated with particulate membrane‐enclosed compartments. The apoplastic proteome was diverse, the proteins were not degraded, and some displayed distinct multimerization states compared to their cytosolic pool. This quantitative proteomic pipeline can be used to improve coverage and functional analyses of the cotton fiber proteome as a function of developmental time or differing genotypes. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email