Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data

By Jun Li, Robert Tibshirani. Statistical Methods in Medical Research, 2013

449 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/62f8a69c7e3809949dd023852e0e0a54c49eaf74

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Citations in the corpus

Combining real-time PCR and next-generation DNA sequencing to provide quantitative comparisons of fungal aerosol populations (2014, methodology): “00a (Li and Tibshirani, 2011; Tusher et al., 2001) was used on all species, genera, and classes to determine which taxa were associated with sampling in spring or fall (Table 2).”
A comparison of methods for differential expression analysis of RNA-seq data (2013, methodology): “Nine of them work on the count data directly: DESeq [7], edgeR [23], NBPSeq [15], TSPM [13], baySeq [14], EBSeq [24], NOISeq [25], SAMseq [26] and ShrinkSeq [27].”
Comparison of software packages for detecting differential expression in RNA-seq studies (2013, methodology): “Soneson and Delorenzi [16] included in their comparison a total of 11 approaches (edgeR, DESeq, baySeq, NBPSeq, TSPM, EBSeq, NOIseq, SAMseq, ShrinkSeq and two versions of limma) but again focused mainly on simulated data sets.”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, methodology): “We also present the statistical formulation of the new NOISeqBIO and compare this approach to popular RNA-seq DE methods (edgeR (18), DESeq2 (19) and SAMseq (38)) using both real and simulated datasets to demonstrate the efficiency of the method to control false calls in a wide variety of analysis s”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, unknown): “Other methods compared were the voom normalization method followed by linear modeling using the limma package [35] and the SAMseq permutation method of the samr package [23].”
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “In addition, we include a resampling based method, SAMseq [35], as an optional method for identifying differentially expressed genes.”
Robustly detecting differential expression in RNA sequencing data using observation weights (2013, methodology): “In their manuscript, Li and Tibshirani (27) show some extreme examples of outliers affecting differential count analysis of miRNA-seq data (in particular, see their Figure 2).”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, methodology): “Fourthly, there are several other count matrices based tools available for the differential expression analysis, such as NBPSeq [46], baySeq [47], SAMSeq [48] and ShrinkSeq [49], among others, but a thorough comparison of all”
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, unknown): “To generate an abundance heatmap we identified lncRNAs that had a mean FPKM ≥ 5 and a SAM multiclass (samr 2.0) (Li and Tibshirani, 2013) q ≤ 0.01 across the unsupervised clusters (see differential abundance, below), transformed each row of the matrix by log10(FPKM + 1), then used the pheatmap R…”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Finally, nonparametric methods have been developed, which are more robust to outliers and do not require distributional assumptions, such as SAMSeq [149] , which adopts the Wilcoxon test to assess DE between groups, and uses resampling procedures to adjust for differences in sequencing depth.”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “To generate a clustering heatmap we used a SAM (Li and Tibshirani, 2013) (samr v2.”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Finally, non-parametric approaches such as NOISeq [10] or SAMseq [61] make minimal assumptions about the data and estimate the null distribution for inferential analysis from the actual data alone.”
Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma (2017, unknown): “(C) Heatmap of a subset of highlyranked SAM multiclass contrasts for miRs that were differentially abundant across the 3 miR clusters in the high purity samples (Fig.”
Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas. (2017, unknown): “0 Li and Tibshirani, 2013 https://cran.r-project.org/package=samr Samtools v0.”
Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, methodology): “We and others have developed non-parametric tools to identify those features fulfilling those criteria [8,32].”