A simple null model for inferences from network enrichment analysis

Pathway analysis through mutual information

MOTIVATION

In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships.

RESULTS

Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores.

AVAILABILITY AND IMPLEMENTATION

Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."

Effects of Fluoride on Submandibular Glands of Mice: Changes in Oxidative Biochemistry, Proteomic Profile, and Genotoxicity

Although fluoride (F) is well-known to prevent dental caries, changes in cell processes in different tissues have been associated with its excessive exposure. Thus, this study aimed to evaluate the effects of F exposure on biochemical, proteomic, and genotoxic parameters of submandibular glands. Twenty one old rats (n = 30) were allocated into three groups: 60 days administration of drinking water containing 10 mgF/L, 50 mgF/L, or only deionized water (control). The submandibular glands were collected for oxidative biochemistry, protein expression profile, and genotoxic potential analyses. The results showed that both F concentrations increased the levels of thiobarbituric acid–reactive substances (TBARS) and reduced glutathione (GSH) and changed the proteomic profile, mainly regarding the cytoskeleton and cellular activity. Only the exposure to 50 mgF/L induced significant changes in DNA integrity. These findings reinforce the importance of continuous monitoring of F concentration in drinking water and the need for strategies to minimize F intake from other sources to obtain maximum preventive/therapeutic effects and avoid potential adverse effects.

Parallelized calculation of permutation tests

Motivation

Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive.

Results

Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up—by orders of magnitude—is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test.

Availabilityand implementation

In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Pathway-specific model estimation for improved pathway annotation by network crosstalk

Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.