Commentary: A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

Kadota, Koji; Shimizu, Kentaro

doi:10.3389/fgene.2020.00941

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Although single-cell expression analysis is an important topic, it is still in its infancy and has its own characteristics. This topic has also been supported by previous studies such as [120] . Clearly, there is a need that future studies can and should include analytical techniques for single-cell data as an emerging topic, and that deserves a dedicated review.…”

Section: Discussionsupporting

confidence: 85%

See 1 more Smart Citation

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Costa-Silva¹,

Domingues²,

Menotti³

et al. 2023

Computational and Structural Biotechnology Journal

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Section: Discussionsupporting

confidence: 85%

“…In this context, a comparison considering different methods for DEG analysis among scRNA-Seq populations is presented in [21] , [120] . Three different scenarios were considered, showing significant differences between the methods in terms of the number of genes identified with differential expression.…”

Section: Discussionmentioning

confidence: 99%

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Costa-Silva¹,

Domingues²,

Menotti³

et al. 2023

Computational and Structural Biotechnology Journal

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“…Several bioinformatics studies have made comparisons with typical conventional analysis methods and claimed about superior results by using the methods of interest [ 51 ]. However, a typical package may not be the best suited for the analysis.…”

Section: Discussionmentioning

confidence: 99%

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

2021

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Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering is used to classify DEGs with similar expression patterns for the subsequent analyses of data from experiments such as time-courses or multi-group comparisons. However, gene clustering has rarely been used for analyzing simple two-group data or differential expression (DE). In this study, we report that a model-based clustering algorithm implemented in an R package, MBCluster.Seq, can also be used for DE analysis. Results The input data originally used by MBCluster.Seq is DEGs, and the proposed method (called MBCdeg) uses all genes for the analysis. The method uses posterior probabilities of genes assigned to a cluster displaying non-DEG pattern for overall gene ranking. We compared the performance of MBCdeg with conventional R packages such as edgeR, DESeq2, and TCC that are specialized for DE analysis using simulated and real data. Our results showed that MBCdeg outperformed other methods when the proportion of DEG (PDEG) was less than 50%. However, the DEG identification using MBCdeg was less consistent than with conventional methods. We compared the effects of different normalization algorithms using MBCdeg, and performed an analysis using MBCdeg in combination with a robust normalization algorithm (called DEGES) that was not implemented in MBCluster.Seq. The new analysis method showed greater stability than using the original MBCdeg with the default normalization algorithm. Conclusions MBCdeg with DEGES normalization can be used in the identification of DEGs when the PDEG is relatively low. As the method is based on gene clustering, the DE result includes information on which expression pattern the gene belongs to. The new method may be useful for the analysis of time-course and multi-group data, where the classification of expression patterns is often required.

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“…We evaluated three versions of MBCdeg using three conventional methods (edgeR, DESeq2, and TCC_DE) as benchmarks. Although all of these algorithms were developed with bulk RNA-seq data in mind, the basic properties of single-cell RNA-seq data are the same as bulk RNA-seq, so they are applicable to all RNA-seq data [43]. The potential of MBCdeg is remarkable because it performs signi cantly better than conventional methods for simulation conditions with P DEG ≤ 0.45, even when the wrong normalization factors are used in the previous MBCdeg2 [29].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of clustering-based differential expression analysis methods for RNA-seq data

Makino,

Shimizu,

Kadota

2023

Preprint

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Background RNA-seq is a tool for measuring gene expression and is commonly used to identify differentially expressed genes (DEGs). Gene clustering has been widely used to classify DEGs with similar expression patterns, but rarely used to identify DEGs themselves. We recently reported that the clustering-based method (called MBCdeg) for identifying DEGs has great potential. However, a thorough investigation of its feasibility is still needed. Results We compared a total of six competing methods: three conventional R packages (edgeR, DESeq2, and TCC) and three versions of MBCdeg (denoted as MBCdeg1, 2, and 3) corresponding to three different normalization algorithms. Different scenarios of simulated data generated from three R packages (TCC, compcodeR, and PROPER) were mainly evaluated based on the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the modified version of MBCdeg2 performed well for many scenarios on simulated data. However, MBCdeg showed very unstable results between trials for identical real data. Conclusions The current MBCdeg2 shows excellent performance in simulation analysis, but not at a practical level for real data. Since further improvements are needed for MBCdeg to reach a practical level, we cannot recommend the use of the current MBCdeg. Our report suggests not only the need for method developers to carefully describe their shortcomings, but also the need for the reader to critically decipher the conclusions reached under what conditions.

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Commentary: A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

Cited by 6 publications

References 17 publications

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods

Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data

Evaluation of clustering-based differential expression analysis methods for RNA-seq data

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