scholarly journals STR: Seasonal-Trend Decomposition Using Regression

2021 ◽  
Author(s):  
Alexander Dokumentov ◽  
Rob J. Hyndman
Keyword(s):  
Decomposition Methods ◽  
R Package ◽  
Seasonal Trend ◽  
Seasonal Patterns ◽  
Quarterly Data ◽  
Time Index ◽  
Regular Time ◽  
Complex Topology ◽  
Regularized Optimization ◽  
Seasonal Data

We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so it can be applied by anyone to their own data.

scholarly journals Detecting presence of mutational signatures in cancer with confidence

2017 ◽  
Author(s):  
Xiaoqing Huang ◽  
Damian Wojtowicz ◽  
Teresa M. Przytycka
Keyword(s):  
Breast Cancer ◽  
Somatic Mutations ◽  
Decomposition Methods ◽  
R Package ◽  
The Novel ◽  
Cancer Etiology ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Cancer Genomes ◽  
Additional Support

AbstractCancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA mainte-nance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Decomposition of cancer’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to not only be able to decompose a patient’s mutational profile into signatures but also to establish the accuracy of such decomposition. We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified very stable and highly unstable signatures, as well as signatures that have been missed altogether. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#signatureestimation.

Quantifying Seasonal Patterns in Disparate Environmental Variables Using the PolarMetrics R Package

2017 ◽  
Author(s):  
Bjorn-Gustaf J. Brooks ◽  
Danny C. Lee ◽  
Lars Y. Pomara ◽  
William W. Hargrove ◽  
Ankur R. Desai
Keyword(s):  
Environmental Variables ◽  
R Package ◽  
Seasonal Patterns

scholarly journals oCEM: Automatic detection and analysis of overlapping co-expressed gene modules

BMC Genomics ◽  
2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Quang-Huy Nguyen ◽  
Duc-Hau Le
Keyword(s):  
Statistical Approach ◽  
Decomposition Methods ◽  
R Package ◽  
Optimal Number ◽  
Gene Clustering ◽  
Gene Module ◽  
Biologically Relevant ◽  
Module Detection ◽  
Gene Modules ◽  
Selection Of

Abstract Background When it comes to the co-expressed gene module detection, its typical challenges consist of overlap between identified modules and local co-expression in a subset of biological samples. The nature of module detection is the use of unsupervised clustering approaches and algorithms. Those methods are advanced undoubtedly, but the selection of a certain clustering method for sample- and gene-clustering tasks is separate, in which the latter task is often more complicated. Results This study presented an R-package, Overlapping CoExpressed gene Module (oCEM), armed with the decomposition methods to solve the challenges above. We also developed a novel auxiliary statistical approach to select the optimal number of principal components using a permutation procedure. We showed that oCEM outperformed state-of-the-art techniques in the ability to detect biologically relevant modules additionally. Conclusions oCEM helped non-technical users easily perform complicated statistical analyses and then gain robust results. oCEM and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/oCEM.

Seasonal Variation of Suicide in the City of São Paulo, Brazil, 1996–2010

Crisis ◽  
2014 ◽  
Vol 35 (1) ◽  
pp. 5-9 ◽  
Author(s):  
Daniel Hideki Bando ◽  
Fernando Madalena Volpe
Keyword(s):  
Seasonal Pattern ◽  
Total Sample ◽  
Sao Paulo ◽  
Seasonal Patterns ◽  
São Paulo ◽  
Health Statistics ◽  
Males And Females ◽  
June July ◽  
The City ◽  
Over Time

Background: In light of the few reports from intertropical latitudes and their conflicting results, we aimed to replicate and update the investigation of seasonal patterns of suicide occurrences in the city of São Paulo, Brazil. Methods: Data relating to male and female suicides were extracted from the Mortality Information Enhancement Program (PRO-AIM), the official health statistics of the municipality of São Paulo. Seasonality was assessed by studying distribution of suicides over time using cosinor analyses. Results: There were 6,916 registered suicides (76.7% men), with an average of 39.0 ± 7.0 observed suicides per month. For the total sample and for both sexes, cosinor analysis estimated a significant seasonal pattern. For the total sample and for males suicide peaked in November (late spring) with a trough in May–June (late autumn). For females, the estimated peak occurred in January, and the trough in June–July. Conclusions: A seasonal pattern of suicides was found for both males and females, peaking in spring/summer and dipping in fall/winter. The scarcity of reports from intertropical latitudes warrants promoting more studies in this area.

Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽  
2016 ◽  
Vol 81 (S 01) ◽  
pp. S1-S381
Author(s):  
C Roullier ◽  
Y Guitton ◽  
S Prado ◽  
O Grovel ◽  
YF Pouchus
Keyword(s):  
R Package ◽  
Halogenated Compound

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Systematics, Biogeography, and Morphological Character Evolution of the Hemiepiphytic Subfamily Monsteroideae (Araceae)

2019 ◽  
Vol 104 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Alejandro Zuluaga ◽  
Martin Llano ◽  
Ken Cameron
Keyword(s):  
Dna Sequences ◽  
Pacific Islands ◽  
R Package ◽  
Morphological Characters ◽  
Ancestral State ◽  
Seed Shape ◽  
Long Distance ◽  
The Pacific ◽  
And Migration ◽  
The Tropics

The subfamily Monsteroideae (Araceae) is the third richest clade in the family, with ca. 369 described species and ca. 700 estimated. It comprises mostly hemiepiphytic or epiphytic plants restricted to the tropics, with three intercontinental disjunctions. Using a dataset representing all 12 genera in Monsteroideae (126 taxa), and five plastid and two nuclear markers, we studied the systematics and historical biogeography of the group. We found high support for the monophyly of the three major clades (Spathiphylleae sister to Heteropsis Kunth and Rhaphidophora Hassk. clades), and for six of the genera within Monsteroideae. However, we found low rates of variation in the DNA sequences used and a lack of molecular markers suitable for species-level phylogenies in the group. We also performed ancestral state reconstruction of some morphological characters traditionally used for genera delimitation. Only seed shape and size, number of seeds, number of locules, and presence of endosperm showed utility in the classification of genera in Monsteroideae. We estimated ancestral ranges using a dispersal-extinction-cladogenesis model as implemented in the R package BioGeoBEARS and found evidence for a Gondwanan origin of the clade. One tropical disjunction (Monstera Adans. sister to Amydrium Schott–Epipremnum Schott) was found to be the product of a previous Boreotropical distribution. Two other disjunctions are more recent and likely due to long-distance dispersal: Spathiphyllum Schott (with Holochlamys Engl. nested within) represents a dispersal from South America to the Pacific Islands in Southeast Asia, and Rhaphidophora represents a dispersal from Asia to Africa. Future studies based on stronger phylogenetic reconstructions and complete morphological datasets are needed to explore the details of speciation and migration within and among areas in Asia.

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