ggplot2

Wickham, Hadley

doi:10.1007/978-0-387-98141-3

Cited by 13,484 publications

(1,555 citation statements)

References 3 publications

Supporting

Mentioning

1,467

Contrasting

Unclassified

Order By: Relevance

“…In addition, DNA contamination from RNASeq reads was identified and removed. Statistics, graphs, and tables were all generated using custom software written in Python, R, and the ggplot2 package (49 …”

Section: Methodsmentioning

confidence: 99%

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Kosuri

Goodman

Cambray

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

415

453

View full text Add to dashboard Cite

The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.next-generation sequencing | synthetic biology | systems biology O rganisms can be engineered to produce chemical, material, fuel, and medical products that are often superior to nonbiological alternatives (1). Biotechnologists have sought to discover, improve, and industrialize such products through the use of recombinant DNA technologies (2, 3). In recent years, these efforts have increased in complexity from expressing a few genes at once to optimizing multicomponent circuits and pathways (4-7). To attain desired systems-level function reliably, careful and time-consuming optimization of individual components is required (8-11).To mitigate this slow trial-and-error optimization, two dominant approaches have taken hold. The first approach seeks to predict expression levels by elucidating the biophysical relationships between sequence and function. For example, several groups have modified promoters (12, 13) and ribosome binding sites (RBSs) (14-16) to see how small sequence changes affect transcription or translation. Such studies are fundamentally challenging due to the vastness of sequence space. In addition, because these approaches mostly look at either transcription or translation individually, they are rarely able to investigate interactions between these processes.The second approach uses combinations of individually characterized elements to attain desired expression without directly considering their DNA sequences (17-25). Current efforts have focused on approaches to limit the number of time-consuming steps required to characterize potential interactions and on identifying existing or engineered elements that act predictably when used in combination (26-28). However, these studies still suggest there are enough idiosyncratic interactions and context effects that it will be necessary to construct and measure many variants of a circuit to achieve desired function (29). For larger circuits, such approaches are necessarily limited in scope due to the difficulty in measuring large numbers of combinations (26, 27...

show abstract

Section: Methodsmentioning

confidence: 99%

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Kosuri

Goodman

Cambray

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

415

453

View full text Add to dashboard Cite

show abstract

“…Novel green objects were noticed at virtually identical rates to an unexpected object that matched another color in the display when subjects ignored colors, suggesting a category-based attention set. All plots were generated with the ggplot2 package for R (Wickham, 2009).…”

Section: Methodsmentioning

confidence: 99%

Selective Attention in Inattentional Blindness: Selection is Specific but Suppression is Not

Wood

Simons

2017

Collabra: Psychology

View full text Add to dashboard Cite

When we selectively attend to one set of objects and ignore another, we often fail to notice unexpected events. The likelihood of noticing varies depending on the similarity of an unexpected object to other items in the display, a process thought to be controlled by the attention set that we create for the attended and ignored objects. It remains unclear, though, how attention sets are formed and structured. Do they enhance features of attended objects ("white") and suppress features of ignored objects ("black"), or do they distinguish objects based on relations or categories ("darker" versus "lighter," or "dark objects" versus "light objects")? In previous work, these explanations are confounded; the objects would be partitioned into the same groups regardless the structure of the attention set. In the present three experiments, the attended or ignored set of objects was a constant color while the other set was variable. When people attended white and ignored a multicolored set of objects (Experiment 1), novel colors were suppressed just as much as display colors, suggesting nonselective filtering of nonwhite objects. When the color of one set of objects varied across displays but was constant within them ( Experiments 2 and 3), we again found as much suppression for task-irrelevant and novel colors as for actively ignored ones. Whenever people ignored a set of objects that varied in color, they suppressed unexpected objects that matched the ignored colors and that differed from the actively ignored items on the critical trial. In contrast, when people attended a varying set, noticing was enhanced only for unexpected objects that matched the currently attended color. In this task, attentional filtering is category-based and did not depend on the features of the individual objects.

show abstract

“…Additional R packages were used for analyses of mcmc chains and graphics (Plummer, Best, Cowles, & Vines, 2006; Wickham, 2009). In addition to the validation discussed in Section 2.2.3, we performed a range of posterior predictive checks and comparisons between simulated and real‐world datasets to assess model adequacy (following Gelman & Hill, 2006; Kéry & Schaub, 2012).…”

Section: Methodsmentioning

confidence: 99%

Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values

Carroll

Gillingham

Stafford

et al. 2018

Ecology and Evolution

View full text Add to dashboard Cite

Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi‐quantitative description of species’ ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities—particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models—fitted with and without group‐level predictors (e.g., habitat type)—to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot‐level differences in mean EIV scores between time‐periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R 2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.

show abstract

ggplot2

Cited by 13,484 publications

References 3 publications

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Selective Attention in Inattentional Blindness: Selection is Specific but Suppression is Not

Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values

Contact Info

Product

Resources

About