Data Insight-AI-powered data analysis platform

Example

Detailed steps on cleaning, normalizing, and merging TCGA RPPA datasets.

Scenario

A bioinformatician is preparing data from multiple sources, including TCGA, to analyze protein expression levels in different cancer types. Data Insight provides clear instructions on formatting, normalization, and handling outliers to ensure data consistency and compatibility for downstream analysis.

Example

Assistance with statistical analysis methods such as linear regression, clustering, and survival analysis.

Scenario

A cancer researcher wants to identify potential biomarkers by applying survival analysis on TCGA RPPA data. Data Insight offers a step-by-step guide to performing Kaplan-Meier survival analysis, including assumptions, data requirements, and interpretation of the results.

Example

Guidance on creating effective visualizations like heatmaps, volcano plots, and Kaplan-Meier curves.

Scenario

An analyst needs to create a heatmap to visualize differential protein expression across various tumor samples. Data Insight provides instructions on selecting the right color schemes, scaling methods, and clustering techniques to enhance the clarity and impact of the visualization.

Example

Understanding and interpreting results from complex bioinformatics tools and algorithms.

Scenario

A graduate student is using machine learning to classify cancer subtypes based on protein expression data. Data Insight helps explain the choice of algorithms, feature selection methods, and the interpretation of model performance metrics like accuracy, AUC, and F1 score.

Example

Identifying common errors in data processing or analysis and suggesting fixes.

Scenario

While running a statistical analysis, a researcher encounters errors related to data dimensionality. Data Insight provides suggestions for checking data structure, handling missing values, and reformatting datasets to resolve these issues.