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DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R. It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Monocle [121] Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments. SCANPY [122] [123] Scalable Python-based implementation for preprocessing, visualization, clustering, trajectory inference and differential expression testing. SCell [124] integrated analysis of single-cell RNA-seq data.
Transcriptomics method use over time. Published papers referring to RNA-Seq (black), RNA microarray (red), expressed sequence tag (blue), digital differential display (green), and serial/cap analysis of gene expression (yellow) since 1990.
5 Time series analysis. 6 Charts and diagrams. 7 Other abilities. 8 See also. 9 Footnotes. 10 References. 11 Further reading. Toggle the table of contents. Comparison ...
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [1] [2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
In policy analysis, forecasting future production of biofuels is key data for making better decisions, and statistical time series models have recently been developed to forecast renewable energy sources, and a multiplicative decomposition method was designed to forecast future production of biohydrogen. The optimum length of the moving average ...
In time series analysis, the Box–Jenkins method, [1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.