کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8339989 | 1541186 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data
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کلمات کلیدی
RNAseqsingle cell RNA sequencingFPRTPMGDRNGSROCCDRCDFEMDFDRAUC - AUCCumulative Distribution Function - تابع توزیع تجمعیRNA sequencing - ترتیب RNANext generation sequencing - توالی نسل بعدیMultimodal data - داده های چندجمله ایNegative binomial - دو طرفه منفیTPR - روش پاسخ فیزیکیDifferentially expressed - متفاوت بیان شده استtranscript per million - متن در هر میلیونtrue positive - مثبت واقعیfalse positive - مثبت کاذبData imputation - محاسبه داده هاNonparametric models - مدل های غیر پارامتریarea under curve - منطقه تحت منحنیfalse negative - منفی اشتباهtrue negative - منفی واقعیfalse discovery rate - میزان کشف کاذبFalse positive rate - نرخ مثبت کاذبTrue positive rate - نرخ واقعی مثبتreceiver operating characteristic - گیرنده عامل عامل
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
زیست شیمی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a data imputation approach, a logistic regression model and a nonparametric method based on the Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model and data imputation are used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated datasets and real datasets to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Methods - Volume 145, 1 August 2018, Pages 25-32
Journal: Methods - Volume 145, 1 August 2018, Pages 25-32
نویسندگان
Tianyu Wang, Sheida Nabavi,