Dynamic visualization of multi-level molecular data: The Director package in R

Icay, Katherine; Liu, Chengyu; Hautaniemi, Sampsa

doi:10.1016/j.cmpb.2017.10.013

Cited by 6 publications

(5 citation statements)

References 27 publications

(39 reference statements)

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“…In the current follow-up study, we used a validation set of patients and compared the prognostic accuracy of the WATSON Explorer with the trauma and injury severity score (TRISS) as an established prediction tool. Moreover, we looked at SIRS and sepsis within 21 days since admission and early death within 72 h since admission using Sankey diagrams [ 4 ]. Thereby, a comparison of the predictive value regarding mortality could be made in comparison with TRISS, which represents the closest predictive tool in terms of simplicity.…”

Section: Introductionmentioning

confidence: 99%

Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS

Niggli

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Niggli

et al. 2021

JCM

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Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X2 = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X2 = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.

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Section: Introductionmentioning

confidence: 99%

Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS

Niggli

Pape

Niggli

et al. 2021

JCM

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“…Then, we compared the patients’ stratification between the FRLP risk score model and the alternative GS model. A Sankey diagram was drawn to demonstrate the intersection of the two models using R package “Director” ( Icay et al, 2018 ; Xiang et al, 2021 ; Tang et al, 2021 ). Finally, we calculated the GS of each sample in two GEO cohorts and stratified the patients using the same cut-off value mentioned above.…”

Section: Methodsmentioning

confidence: 99%

A Novel Ferroptosis-Related LncRNA Pair Prognostic Signature Predicts Immune Landscapes and Treatment Responses for Gastric Cancer Patients

Xiang²,

Song³

et al. 2022

Front. Genet.

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Background: The construction of ferroptosis-related lncRNA prognostic models in malignancies has been an intense area of research recently. However, most of the studies focused on the exact expression of lncRNAs and had limited application values. Herein, we aim to establish a novel prognostic model for gastric cancer (GC) patients and discuss its correlation with immune landscapes and treatment responses.Methods: The present study retrieved transcriptional data of GC patients from the Cancer Genome Atlas (TCGA) database. We identified differentially expressed ferroptosis-related lncRNAs between tumor and normal controls of GC samples. Based on a new method of cyclically single pairing, we constructed a 0 or 1 matrix of ferroptosis-related lncRNA pairs (FRLPs). A risk score signature consisting of 10 FRLPs was established using multi-step Cox regression analysis. Next, we performed a series of systematic analyses to investigate the association of the FRLP model and tumor microenvironment, biological function, and treatment responses. An alternative model to the FRLP risk score signature, the gene set score (GS) model was also constructed, which could represent the former when lncRNA expression was not available.Results: We established a novel prognostic signature of 10 ferroptosis-related lncRNA pairs. High-risk patients in our risk score model were characterized by high infiltration of immune cells, upregulated carcinogenic and stromal activities, and heightened sensitivity to a wide range of anti-tumor drugs, whereas low-risk patients were associated with better responses to methotrexate treatment and elevated immunotherapeutic sensitivity. The practicability of the FRLP risk score model was also validated in two independent microarray datasets downloaded from Gene Expression Omnibus (GEO) using the GS model. Finally, two online dynamic nomograms were built to enhance the clinical utility of the study.Conclusion: In this study, we developed a ferroptosis-related lncRNA pair-based risk score model that did not rely on the exact lncRNA expression level. This novel model might provide insights for the accurate prediction and comprehensive management for GC patients.

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“…energy, material, or cost) within systems [37,38]. The Sankey diagrams have been useful in supporting the analysis of systems flows in medicine [39] and energy [40,41,38]. These studies show that Sankey diagrams help clearly visualize the connections among multiple variables.…”

Section: Visualizationmentioning

confidence: 97%