Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC

Aboveground Feeding by Soybean Aphid, Aphis glycines, Affects Soybean Cyst Nematode, Heterodera glycines, Reproduction Belowground

Heterodera glycines is a cyst nematode that causes significant lost soybean yield in the U.S. Recent studies observed the aphid Aphis glycines and H. glycines interacting via their shared host, soybean, Glycine max. A greenhouse experiment was conducted to discern the effect of A. glycines feeding on H. glycines reproduction. An H. glycines-susceptible cultivar, Kenwood 94, and a resistant cultivar, Dekalb 27–52, were grown in H. glycines-infested soil for 30 and 60 d. Ten days after planting, plants were infested with either zero, five, or ten aphids. At 30 and 60 d, the number of H. glycines females and cysts (dead females) and the number of eggs within were counted. In general, H. glycines were less abundant on the resistant than the susceptible cultivar, and H. glycines abundance increased from 30 to 60 d. At 30 d, 33% more H. glycines females and eggs were produced on the resistant cultivar in the ten-aphid treatment compared to the zero-aphid treatment. However, at 30 d the susceptible cultivar had 50% fewer H. glycines females and eggs when infested with ten aphids. At 60 d, numbers of H. glycines females and cysts and numbers of eggs on the resistant cultivar were unaffected by A. glycines feeding, while numbers of both were decreased by A. glycines on the susceptible cultivar. These results indicate that A. glycines feeding improves the quality of soybean as a host for H. glycines, but at higher herbivore population densities, this effect is offset by a decrease in resource quantity