The Experts below are selected from a list of 20226 Experts worldwide ranked by ideXlab platform
Boris P Hejblum - One of the best experts on this subject based on the ideXlab platform.
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dearseq a variance Component Score test for rna seq differential analysis that effectively controls the false discovery rate
bioRxiv, 2019Co-Authors: Marine Gauthier, Denis Agniel, Rodolphe Thiebaut, Boris P HejblumAbstract:RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We present dearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show that dearseq controls the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.
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variance Component Score test for time course gene set analysis of longitudinal rna seq data
Biostatistics, 2017Co-Authors: Denis Agniel, Boris P HejblumAbstract:As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance Component Score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric Component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.
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variance Component Score test for time course gene set analysis of longitudinal rna seq data
arXiv: Applications, 2016Co-Authors: Denis Agniel, Boris P HejblumAbstract:As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance Component Score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric Component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq. Gene Set Analysis; Longitudinal data; RNA-seq data; Variance Component testing; Heteroscedasticity
Satoru Tsuchikawa - One of the best experts on this subject based on the ideXlab platform.
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nir spectral kinetic analysis for thermally degraded sugi cryptomeria japonica wood
Applied Physics A, 2016Co-Authors: Tetsuya Inagaki, Miyuki Matsuo, Satoru TsuchikawaAbstract:Kinetic analysis was conducted on principal Component Scores calculated from second-derivative near-infrared (NIR) spectra of thermally treated Sugi (Cryptomeria japonica) wood samples. NIR reflectance spectra were measured for wood samples thermally treated at 90, 120, 150 and 180 °C in an air-circulating oven for periods ranging from 5 min to approximately 1.4 years. The Arrhenius approach, which involves the time–temperature superposition method, is used to understand the change in the principal Component Score. The master curve corresponded well with the change in principal Component Scores at each temperature and yielded a determination coefficient between the measured and estimated data of 0.99 for second principal Component Score. This report shows that kinetic analysis is useful to understand changes in the principal Component Score calculated from NIR spectra of wood subjected to thermal treatment.
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NIR spectral–kinetic analysis for thermally degraded Sugi (Cryptomeria japonica) wood
Applied Physics A, 2016Co-Authors: Tetsuya Inagaki, Miyuki Matsuo, Satoru TsuchikawaAbstract:Kinetic analysis was conducted on principal Component Scores calculated from second-derivative near-infrared (NIR) spectra of thermally treated Sugi (Cryptomeria japonica) wood samples. NIR reflectance spectra were measured for wood samples thermally treated at 90, 120, 150 and 180 °C in an air-circulating oven for periods ranging from 5 min to approximately 1.4 years. The Arrhenius approach, which involves the time–temperature superposition method, is used to understand the change in the principal Component Score. The master curve corresponded well with the change in principal Component Scores at each temperature and yielded a determination coefficient between the measured and estimated data of 0.99 for second principal Component Score. This report shows that kinetic analysis is useful to understand changes in the principal Component Score calculated from NIR spectra of wood subjected to thermal treatment.
Denis Agniel - One of the best experts on this subject based on the ideXlab platform.
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dearseq a variance Component Score test for rna seq differential analysis that effectively controls the false discovery rate
bioRxiv, 2019Co-Authors: Marine Gauthier, Denis Agniel, Rodolphe Thiebaut, Boris P HejblumAbstract:RNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We present dearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show that dearseq controls the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.
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variance Component Score test for time course gene set analysis of longitudinal rna seq data
Biostatistics, 2017Co-Authors: Denis Agniel, Boris P HejblumAbstract:As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance Component Score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric Component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.
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variance Component Score test for time course gene set analysis of longitudinal rna seq data
arXiv: Applications, 2016Co-Authors: Denis Agniel, Boris P HejblumAbstract:As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance Component Score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric Component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq. Gene Set Analysis; Longitudinal data; RNA-seq data; Variance Component testing; Heteroscedasticity
Toshizumi Ohta - One of the best experts on this subject based on the ideXlab platform.
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WCSS - Stock BBS Factor Model Using Principal Component Score
Advances in Computational Social Science, 2014Co-Authors: Hirohiko Suwa, Eiichi Umehara, Toshizumi OhtaAbstract:Our aim is to develop a new factor for stock bulletin board system (BBS) postings that is different from our bullish-bearish model (BMB) factor. In our previous study, the content of stock BBS postings was classified into two categories, i.e., bullish postings and bearish postings, and our BMB factor is based on these categories. The results of recent studies suggest that the content of stock BBS postings may be represented by employing more than one index. To develop new factors on the basis of the principal Component Score, we use morphological analysis and principal Component analysis to analyze the content of stock BBS postings. As a result, we find candidates for new factors that can explain stock returns.
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stock bbs factor model using principal Component Score
WCSS, 2014Co-Authors: Hirohiko Suwa, Eiichi Umehara, Toshizumi OhtaAbstract:Our aim is to develop a new factor for stock bulletin board system (BBS) postings that is different from our bullish-bearish model (BMB) factor. In our previous study, the content of stock BBS postings was classified into two categories, i.e., bullish postings and bearish postings, and our BMB factor is based on these categories. The results of recent studies suggest that the content of stock BBS postings may be represented by employing more than one index. To develop new factors on the basis of the principal Component Score, we use morphological analysis and principal Component analysis to analyze the content of stock BBS postings. As a result, we find candidates for new factors that can explain stock returns.
Tetsuya Inagaki - One of the best experts on this subject based on the ideXlab platform.
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nir spectral kinetic analysis for thermally degraded sugi cryptomeria japonica wood
Applied Physics A, 2016Co-Authors: Tetsuya Inagaki, Miyuki Matsuo, Satoru TsuchikawaAbstract:Kinetic analysis was conducted on principal Component Scores calculated from second-derivative near-infrared (NIR) spectra of thermally treated Sugi (Cryptomeria japonica) wood samples. NIR reflectance spectra were measured for wood samples thermally treated at 90, 120, 150 and 180 °C in an air-circulating oven for periods ranging from 5 min to approximately 1.4 years. The Arrhenius approach, which involves the time–temperature superposition method, is used to understand the change in the principal Component Score. The master curve corresponded well with the change in principal Component Scores at each temperature and yielded a determination coefficient between the measured and estimated data of 0.99 for second principal Component Score. This report shows that kinetic analysis is useful to understand changes in the principal Component Score calculated from NIR spectra of wood subjected to thermal treatment.
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NIR spectral–kinetic analysis for thermally degraded Sugi (Cryptomeria japonica) wood
Applied Physics A, 2016Co-Authors: Tetsuya Inagaki, Miyuki Matsuo, Satoru TsuchikawaAbstract:Kinetic analysis was conducted on principal Component Scores calculated from second-derivative near-infrared (NIR) spectra of thermally treated Sugi (Cryptomeria japonica) wood samples. NIR reflectance spectra were measured for wood samples thermally treated at 90, 120, 150 and 180 °C in an air-circulating oven for periods ranging from 5 min to approximately 1.4 years. The Arrhenius approach, which involves the time–temperature superposition method, is used to understand the change in the principal Component Score. The master curve corresponded well with the change in principal Component Scores at each temperature and yielded a determination coefficient between the measured and estimated data of 0.99 for second principal Component Score. This report shows that kinetic analysis is useful to understand changes in the principal Component Score calculated from NIR spectra of wood subjected to thermal treatment.