Letter-Value Plots: Boxplots for Large Data

Hofmann, Heike; Wickham, Hadley; Kafadar, Karen

doi:10.1080/10618600.2017.1305277

Cited by 203 publications

(130 citation statements)

References 10 publications

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“…tSNE dimensional reduction suggested that enteroids grown in low EGF (0, 1 ng/mL) clustered together, whereas enteroids grown in higher EGF (10, 100ng/mL) clustered together (Figure 4E). We further examined individual genes expressed in the various samples that are associated with the absorptive ( FABP1, FAPB2, RBP2 ), secretory ( LYZ, PRSS1, TFF1, TFF2 ) and stem cell populations ( YBX1, OLFM4 ) as depicted in boxen plots (letter-value plots) (Hofmann et al, 2017) (Figure 4F). We observed that low-EGF conditions had higher expression of individual absorptive and stem cell markers whereas high-EGF conditions had higher expression of secretory markers, including those associated with the gastric epithelium ( TFF1, TFF2 ) (Lennerz et al, 2010; Leung et al, 2002; Newton et al, 2000) (Figure 4F).…”

Section: Resultsmentioning

confidence: 99%

In vitroandin vivodevelopment of the human intestinal niche at single cell resolution

Czerwinski

Holloway

Tsai

et al. 2020

Preprint

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SUMMARYThe human intestinal stem cell (ISC) niche supports ISC self-renewal and epithelial function, yet little is known about the development of the human ISC niche. We used single-cell mRNA sequencing (scRNA-seq) to interrogate the human intestine across 7-21 weeks of gestation. Using these data coupled with marker validation in situ, molecular identities and spatial locations were assigned to several cell populations that comprise the epithelial niche, and the cellular origins of many niche factors were determined. The major source of WNT and RSPONDIN ligands were ACTA2+ cells of the muscularis mucosa. EGF was predominantly expressed in the villus epithelium and the EGF-family member NEUREGULIN1 (NRG1) was expressed by subepithelial mesenchymal cells. Functional data from enteroid cultures showed that NRG1 improved cellular diversity, enhanced the stem cell gene signature, and increased enteroid forming efficiency, whereas EGF supported a secretory gene expression profile and stimulated rapid proliferation. This work highlights unappreciated complexities of intestinal EGF/ERBB signaling and identifies NRG1 as a stem cell niche factor.

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Section: Resultsmentioning

confidence: 99%

In vitroandin vivodevelopment of the human intestinal niche at single cell resolution

Czerwinski

Holloway

Tsai

et al. 2020

Preprint

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“…Letter-value plots (Hofmann et al, 2017) are an extension of the standard boxplot for large-scale data. The seaborn python package (see Key Resources Table) was used with the depth parameter “proportion,” where 0.007 is assumed the fraction of samples which are outliers in a given cohort.…”

Section: Methodsmentioning

confidence: 99%

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Seiler¹,

Peng²,

Agrawal³

et al. 2018

Cell Reports

353

311

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SUMMARY Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis.

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“…Primarily, we focused on plausibility for both runs regarding the behavioral strategy behind each app, and in a second level we drew conclusions from possible differences between the research and public users. To visualize differences and similarities in the distribution of the replicated features, we will largely rely on the Letter-Value Plots [35], which are especially suited to compare data similar to our case. We start by describing the data subsets as well as their statistic properties and constraints in more detail.…”

Section: Resultsmentioning

confidence: 99%

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

Theilig

Knapp

Nicholas

et al. 2020

Preprint

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Background: Using smartphones and wearable sensor technology has sparked a broad engagement of data science and machine learning methods to leverage the complex, assorted amount of data. Despite verified processes, there is a reported underdevelopment of user engagement concepts, and the desire for high accuracy or significance has shown to lead to low explicability and irreproducibility. To overcome these issues, we aim to analyze principal characteristics of everyday behavior in digital mental health. Methods: We generated five latent features based on previous research, expert opinions from digital mental health, and informed by data. The features were analyzed with descriptive statistics and data visualization. We carried out two rounds of evaluations with data from 12,400 users of IntelliCare, a mental health platform with 12 apps. First, we focused to proof concept and second, we assessed reproducibility by drawing conclusion from distribution differences. User data was drawn from both research trials and public deployment on Google Play. Results: Our algorithms showed increased rationale for the basic usage of apps with different underlying behavioral strategies. Measures of the distribution of user’s allocated attention, the user’s circadian behavior, their consecutive commitment to a specific strategy, and users’ interaction trajectory curve are perceived as transferable to the public data set. Because distributions between research trial and public deployment were similar, consistency was shown regarding the underlying behavioral strategies: psychoeducation and goal setting are used as a catalyst to overcome the users’ primary obstacles, sleep hygiene is addressed most regularly, while regular emotional exposure is avoided. Relaxation as well as cognitive reframing have increased variance in commitment among public users, indicating the challenging nature of these apps. The relative course of the engagement (learning curve) is similar in research and public data. Conclusions: The deliberate, a-priori engineered features were reproducible across app users from both data sets. These features led to improved results as well as increased interpretability, providing an increased understanding of how people engage with multiple mental health apps over time. Since we based the generation of features on generic interaction proxies, these methods are applicable to other cases in artificial intelligence and digital health.

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Letter-Value Plots: Boxplots for Large Data

Cited by 203 publications

References 10 publications

In vitroandin vivodevelopment of the human intestinal niche at single cell resolution

In vitroandin vivodevelopment of the human intestinal niche at single cell resolution

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Characteristic Latent Features for Analyzing Digital Mental Health Interaction and Improved Explainability

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