Clipper: p-value-free FDR control on high-throughput data from two conditions

Ge, Xinzhou; Chen, Yiling Elaine; Song, Dongyuan; McDermott, MeiLu; Woyshner, Kyla; Manousopoulou, Antigoni; Wang, Ning; Li, Wei; Wang, Leo D.; Li, Jingyi Jessica

doi:10.1186/s13059-021-02506-9

Cited by 31 publications

(28 citation statements)

References 85 publications

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“…In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives. However, if users are concerned about FDR control, our recent method Clipper provides a p -value-free FDR control solution for small-sample-size data [ 43 ]; for large-sample-size data, the Wilcoxon rank-sum test is our recommended choice for its solid FDR control and good power.…”

Section: Discussionmentioning

confidence: 99%

Exaggerated false positives by popular differential expression methods when analyzing human population samples

et al. 2022

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When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

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Section: Discussionmentioning

confidence: 99%

Exaggerated false positives by popular differential expression methods when analyzing human population samples

et al. 2022

Self Cite

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“…The Youden index (calculated as J = Sensitivity + Specificity − 1) was used to determine the optimal sensitivity and specificity. p-value less than 0.05 was set as statistical significance (Ge et al, 2021). GraphPad Prism 8 (GraphPad Software Inc., LaJolla, CA), Medcalc (Version 19), and SPSS software (SPSS 26.0 Inc., Chicago, IL) were used to analyze the data.…”

Section: Discussionmentioning

confidence: 99%

A Four-MicroRNA Panel in Serum as a Potential Biomarker for Screening Renal Cell Carcinoma

et al. 2022

Front. Genet.

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Background: Renal cell carcinoma (RCC) has been a major health problem and is one of the most malignant tumors around the world. Serum microRNA (miRNA) profiles previously have been reported as non-invasive biomarkers in cancer screening. The aim of this study was to explore serum miRNAs as potential biomarkers for screening RCC.Methods: A three-phase study was conducted to explore serum miRNAs as potential biomarkers for screening RCC. In the screening phase, 12 candidate miRNAs related to RCC were selected for further study by the ENCORI database with 517 RCC patients and 71 NCs. A total of 220 participants [108 RCC patients and 112 normal controls (NCs)] were enrolled for training and validation. The dysregulated candidate miRNAs were further confirmed with 30 RCC patients and 30 NCs in the training phase and with 78 RCC patients and 82 NCs in the validation phase. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used for assessing the diagnostic value of miRNAs. Bioinformatic analysis and survival analysis were also included in our study.Results: Compared to NCs, six miRNAs (miR-18a-5p, miR-138-5p, miR-141-3p, miR-181b-5p, miR-200a-3p, and miR-363-3p) in serum were significantly dysregulated in RCC patients. A four-miRNA panel was built by combining these candidate miRNAs to improve the diagnostic value with AUC = 0.908. ABCG1 and RNASET2, considered potential target genes of the four-miRNA panel, may play a significant role in the development of RCC.Conclusion: A four-miRNA panel in serum was identified for RCC screening in our study. The four-–miRNA panel has a great potential to be a non-invasive biomarker for RCC screening.

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“…For each subtype, DGE analysis was performed between paired tumor and normal samples, using the R package limma ( Ritchie et al, 2015 ). The differential expression genes (DEGs) were identified as adj.P.Val cutoff <0.05 (Benjamini Hochberg false discovery rate (FDR) correction) ( Ge et al, 2021 ) (FDR <0.05) and |log 2 fold change (log 2 FC)| > 1.…”

Section: Methodsmentioning

confidence: 99%

Molecular subtyping of esophageal squamous cell carcinoma by large-scale transcriptional profiling: Characterization, therapeutic targets, and prognostic value

et al. 2022

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The tumor heterogeneity of the transcriptional profiles is independent of genetic variation. Several studies have successfully identified esophageal squamous cell carcinoma (ESCC) subtypes based on the somatic mutation profile and copy number variations on the genome. However, transcriptome-based classification is limited. In this study, we classified 141 patients with ESCC into three subtypes (Subtype 1, Subtype 2, and Subtype 3) via tumor sample gene expression profiling. Differential gene expression (DGE) analysis of paired tumor and normal samples for each subtype revealed significant difference among subtypes. Moreover, the degree of change in the expression levels of most genes gradually increased from Subtype 1 to Subtype 3. Gene set enrichment analysis (GSEA) identified the representative pathways in each subtype: Subtype 1, abnormal Wnt signaling pathway activation; Subtype 2, inhibition of glycogen metabolism; and Subtype 3, downregulation of neutrophil degranulation process. Weighted gene co-expression network analysis (WGCNA) was used to elucidate the finer regulation of biological pathways and discover hub genes. Subsequently, nine hub genes (CORO1A, CD180, SASH3, CD52, CD300A, CD14, DUSP1, KIF14, and MCM2) were validated to be associated with survival in ESCC based on the RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) database. The clustering analysis of ESCC granted better understanding of the molecular characteristics of ESCC and led to the discover of new potential therapeutic targets that may contribute to the clinical treatment of ESCC.

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Clipper: p-value-free FDR control on high-throughput data from two conditions

Cited by 31 publications

References 85 publications

Exaggerated false positives by popular differential expression methods when analyzing human population samples

Exaggerated false positives by popular differential expression methods when analyzing human population samples

A Four-MicroRNA Panel in Serum as a Potential Biomarker for Screening Renal Cell Carcinoma

Molecular subtyping of esophageal squamous cell carcinoma by large-scale transcriptional profiling: Characterization, therapeutic targets, and prognostic value

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