Fold-Change-Specific Enrichment Analysis (FSEA): Quantification of Transcriptional Response Magnitude for Functional Gene Groups

Addressing erroneous scale assumptions in microbe and gene set enrichment analysis

By applying Differential Set Analysis (DSA) to sequence count data, researchers can determine whether groups of microbes or genes are differentially enriched. Yet sequence count data suffer from a scale limitation: these data lack information about the scale (i.e., size) of the biological system under study, leading some authors to call these data compositional (i.e., proportional). In this article, we show that commonly used DSA methods that rely on normalization make strong, implicit assumptions about the unmeasured system scale. We show that even small errors in these scale assumptions can lead to positive predictive values as low as 9%. To address this problem, we take three novel approaches. First, we introduce a sensitivity analysis framework to identify when modeling results are robust to such errors and when they are suspect. Unlike standard benchmarking studies, this framework does not require ground-truth knowledge and can therefore be applied to both simulated and real data. Second, we introduce a statistical test that provably controls Type-I error at a nominal rate despite errors in scale assumptions. Finally, we discuss how the impact of scale limitations depends on a researcher's scientific goals and provide tools that researchers can use to evaluate whether their goals are at risk from erroneous scale assumptions. Overall, the goal of this article is to catalyze future research into the impact of scale limitations in analyses of sequence count data; to illustrate that scale limitations can lead to inferential errors in practice; yet to also show that rigorous and reproducible scale reliant inference is possible if done carefully.

Highly sex specific gene expression in Jojoba

BACKGROUND

Dioecious plants have male and female flowers on separate plants. Jojoba is a dioecious plant that is drought-tolerant and native to arid areas. The genome sequence of male and female plants was recently reported and revealed an X and Y chromosome system, with two large male-specific insertions in the Y chromosome.

RESULTS

A total of 16,923 differentially expressed genes (DEG) were identified between the flowers of the male and female jojoba plants. This represented 40% of the annotated genes in the genome. Many genes, including those responsible for plant environmental responses and those encoding transcription factors (TFs), were specific to male or female reproductive organs. Genes involved in plant hormone metabolism were also found to be associated with flower and pollen development. A total of 8938 up-regulated and 7985 down-regulated genes were identified in comparison between male and female flowers, including many novel genes specific to the jojoba plant. The most differentially expressed genes were associated with reproductive organ development. The highest number of DEG were linked with the Y chromosome in male plants. The male specific parts of the Y chromosome encoded 12 very highly expressed genes including 9 novel genes and 3 known genes associated with TFs and a plant hormone which may play an important role in flower development.

CONCLUSION

Many genes, largely with unknown functions, may explain the sexual dimorphisms in jojoba plants and the differentiation of male and female flowers.

Identification of MATN3 as a Novel Prognostic Biomarker for Gastric Cancer through Comprehensive TCGA and GEO Data Mining

Gastric cancer (GC) is still a vital malignant cancer across the world with unsatisfactory prognostic results. Matrilin-3 (MATN3) is a member of the extracellular matrix (ECM) protein family. The present research intends to explore the expression level of MATN3 in patients with GC and to explore the prognosis significance of MATN3. In this study, we observed that the MATN3 expression was remarkably upregulated in GC samples in contrast to noncancer samples. Clinical analyses unveiled that high MATN3 expression was related to age, tumor status, and clinical stages. Survival analyses unveiled that patients with high MATN3 expression displayed a poorer overall survival and progression-free survival than those with low MATN3 expression. The AUC of the relevant ROC curve for 1 year, 3 years, and 5 years of survival is 0.571, 0.596, and 0.720, separately. Multivariate assays revealed that MATN3 expression and stage were independent predictors of poor prognosis of GC patients. A meta-analysis unveiled that high MATN3 expression was tightly associated with better overall survival. Overall, our data indicated that MATN3 may have a diagnostic and prognostic value for patients with advanced gastric cancer and assist to improve clinical outcomes for GC patients.

Gene Set Analysis: Challenges, Opportunities, and Future Research

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.