DOI: 10.26512/2015.12.d.19564
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Compressão de sinais ECG utilizando DWT com quantização não-linear e por sub-bandas

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Cited by 5 publications
(3 citation statements)
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“…Gene ontology enrichment analysis was performed on the top 250 genes from each component (from each side) using the enrichGO function from the clusterProfiler R package in which p-values are calculated based on the hypergeometric distribution and corrected for testing of multiple biological process GO terms using the Benjamini-Hochberg procedure (Yu et al, 2012). Plots were created using the ggplot2 package and extensions ggrepel, ggforce, ggseqlogo, ggnewscale, ggrastr, RColorBrewer, viridis, and cowplot (Campitelli, 2019; Garnier, 2018; Neuwirth, 2014; Pedersen, 2016; Petukhov, 2018; Wagih, 2017; Wickham, 2016; Wilke, 2018). In addition the following R packages were used: data.table, Matrix (for sparse large matrix computations), biomaRt (for gene identifiers), shiny and shinycssloaders (for creating the interactive web application), ComplexHeatmap, bigmemory (for creating a low-memory shiny app), and MASS (for kernel density estimation) (Bates and Maechler, 2018; Chang et al, 2018; Dowle and Srinivasan, 2019; Durinck et al, 2005; Gu et al, 2016; Kane et al, 2013; Sali, 2017; Venables and Ripley, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…Gene ontology enrichment analysis was performed on the top 250 genes from each component (from each side) using the enrichGO function from the clusterProfiler R package in which p-values are calculated based on the hypergeometric distribution and corrected for testing of multiple biological process GO terms using the Benjamini-Hochberg procedure (Yu et al, 2012). Plots were created using the ggplot2 package and extensions ggrepel, ggforce, ggseqlogo, ggnewscale, ggrastr, RColorBrewer, viridis, and cowplot (Campitelli, 2019; Garnier, 2018; Neuwirth, 2014; Pedersen, 2016; Petukhov, 2018; Wagih, 2017; Wickham, 2016; Wilke, 2018). In addition the following R packages were used: data.table, Matrix (for sparse large matrix computations), biomaRt (for gene identifiers), shiny and shinycssloaders (for creating the interactive web application), ComplexHeatmap, bigmemory (for creating a low-memory shiny app), and MASS (for kernel density estimation) (Bates and Maechler, 2018; Chang et al, 2018; Dowle and Srinivasan, 2019; Durinck et al, 2005; Gu et al, 2016; Kane et al, 2013; Sali, 2017; Venables and Ripley, 2002).…”
Section: Methodsmentioning
confidence: 99%
“…In instances where the hit rate was 100%, ( n -1)/ n was substituted as the hit rate in the d’ calculation, where n is the number of trials. Plots were generated in R with ggplot2 (Wickham et al, 2019), ggpubr (Kassambara, 2020), ggnewscale (Campitelli, 2020), and corrplot (Wei et al, 2017). Experiment and analysis code, as well as associated raw data, are available on KiltHub (CMU; digital object identifier: 10.1184/R1/12743276).…”
Section: Methodsmentioning
confidence: 99%
“…Supplementary Table1 [ 28 ] and openxlsx [ 29 ] were used for table output. Ggplot2 [ 30 ], ggpubr [ 31 ], ggh4x [ 32 ], ggnewscale [ 33 ], and officer [ 34 ] were used for visualization.…”
Section: Methodsmentioning
confidence: 99%