Identification of population-level differentially expressed genes in one-phenotype data

Xie, Jiajing; Chen, Haifeng; Chi, Meirong; He, Jun; Li, Meifeng; Liu, Hui; Xia, Jie; Guan, Qingzhou; Guo, Zheng; Yan, Haidan

doi:10.1093/bioinformatics/btaa523

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…To make sense of such public archives, reusability and reproducibility of data were highly recommended for providing new biological insight (Rung and Brazma, 2013). Many studies were conducted in this direction like genome upgradation (Cheng et al, 2017) and network interpretation in Arabidopsis (He et al, 2016), potential resistance gene identification in tomato (Torres-Avilés et al, 2014) and different integrative approaches in different plants (Mercatelli et al, 2020; Napolitano et al, 2020; Xie et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Unravelling cotton RNAseq repositories to the fiber development specific modules and their alliance with the fiber-related traits

Khatoon

Mohapatra³

et al. 2021

Preprint

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Cotton fiber development is still an intriguing question to understand the fiber commitment and development. Here, we remapped >350 publicly available cotton RNA sequencing data on recently published cotton genome with ~400 fold coverage. The differentially expressed genes were clustered in six modules whose functions are specific to commitment, initiation, elongation and Secondary Cell Wall (SCW) fiber development stages. Gene Ontology analysis of commitment and initiation specific modules suggests enrichment of genes involved in organ development. The modules specific for elongation and SCW showed significant enrichment of hydroxyproline-rich proteins and hydrolases. Transcription factors (TFs) binding frequency of defined modules suggested that homeodomain, MYB and NAC expresses at commitment stages but their expression was governed by other TFs. We also mined the stage-specific transcriptional biomarker and Exclusively Expressed Transcripts (EETs) for fiber. These EETs were positively selected during fiber evolution and cotton domestication. The extensive expression profiling of six EETs in 100 cotton genotypes at different fiber developmental stages using nCounter assay and their correlation with eight fiber-related suggests that several EETs are correlated with different fiber quality-related traits. Thus, our study reveals several important genes and pathways that may be important for cotton fiber development and future improvement of cotton.

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Section: Introductionmentioning

confidence: 99%

Unravelling cotton RNAseq repositories to the fiber development specific modules and their alliance with the fiber-related traits

Khatoon

Mohapatra³

et al. 2021

Preprint

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“…On bulk transcriptome data, several methods extract relevant biological knowledge from individual samples without requiring a large disease cohort, as reviewed in 13 . They either provide a genecentric view on differentially expressed (DE) genes or a pathway-centric view on deregulated pathways, comparing a single sample against a reference cohort or a control sample [13][14][15][16] . In addition, VIPER can predict protein activity from regulon enrichment on single-sample gene expression signatures obtained using a reference set 17 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of single-sample network inference methods for precision oncology

Deschildre,

Vandemoortele,

Loers

et al. 2023

Preprint

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A major challenge in precision oncology is to identify targetable cancer vulnerabilities in individual patients. Modelling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning and predicting gene interactions for a group of patients. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of the individual patient. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made. Moreover, many methods rely on normal tissue samples as reference point for the tumor samples, which is not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, iENA, CSN and SSPGI using expression profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed networks with distinct network topologies, as observed by edge weight distributions and other network characteristics. Further, hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by edge weight clustering, enrichment of known subtype-specific driver genes among hub gene sets, and differential node importance. Finally, we show that single-sample networks correlate better to other omics data from the same cell line as compared to aggregate networks. Our results point to the important role of single-sample network inference in precision medicine.

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“…Our lab previously developed two versions of an algorithm based on REOs, RankComp [7] and RankCompV2 [8]. We successfully applied these algorithms to microarray, RNA-seq, methylation, and proteomic data [7,[9][10][11][12][13][14]. RankComp can be used to identify DEGs at the population and individual levels, and it is insensitive to batch effects and normalization.…”

Section: Introductionmentioning

confidence: 99%

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

Yan,

Zeng,

Wang

2023

Preprint

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Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. Here, we present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3×3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available athttps://github.com/pathint/RankCompV3.jl.

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Identification of population-level differentially expressed genes in one-phenotype data

Cited by 9 publications

References 42 publications

Unravelling cotton RNAseq repositories to the fiber development specific modules and their alliance with the fiber-related traits

Unravelling cotton RNAseq repositories to the fiber development specific modules and their alliance with the fiber-related traits

Evaluation of single-sample network inference methods for precision oncology

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

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