Time Series Analysis Service

Introduction of Time Series Analysis

Time series analysis is a pivotal technique in the investigation of temporal gene and protein expression profiles. This analytical approach is crucial for deciphering the dynamic changes in expression levels over time, enabling the identification of underlying temporal patterns and potential functional relationships among genes. Through this method, researchers can better understand how gene expression evolves across various time points, contributing to insights into biological processes and disease mechanisms.

In the realm of genomics and proteomics, time series analysis allows for the clustering of genes or proteins based on their expression patterns over time. Unlike traditional methods, which often rely on hard clustering and assign each gene to a single group, time series analysis can employ more flexible clustering techniques. These approaches permit the assignment of a gene to multiple clusters, thereby capturing the complexity and subtlety of gene expression dynamics. This flexibility enhances the ability to detect co-regulated genes and understand their roles in biological pathways.

Application of Time-Series Analysis

• Identification of Temporal Expression Patterns: Time series analysis facilitates the identification of gene or protein expression patterns that change dynamically over time. By analyzing these temporal shifts, researchers can detect crucial periods of gene activity or inactivity, which may correspond to specific biological events such as cell cycle stages, developmental transitions, or responses to external stimuli.
• Deciphering Disease Progression Dynamics: Through the continuous monitoring of gene or protein expression profiles, time series analysis provides insights into the temporal progression of diseases. This approach is particularly valuable in identifying early molecular changes that precede clinical symptoms, thereby uncovering potential therapeutic windows and disease biomarkers that could be pivotal in early diagnosis or intervention.
• Gene Co-Expression and Network Analysis: Time series analysis enables the clustering of genes or proteins with synchronized temporal expression profiles, thereby aiding in the reconstruction of dynamic co-expression networks. These networks can reveal how genes are co-regulated over time, shedding light on the temporal coordination of biological pathways and allowing the identification of key regulatory genes involved in complex biological processes.
• Monitoring and Predicting Drug Response: Time series analysis is instrumental in tracking the temporal effects of pharmaceutical interventions on gene or protein expression. By analyzing how expression patterns evolve in response to treatment, this approach can identify biomarkers that predict therapeutic outcomes, help understand mechanisms of drug resistance, and guide the optimization of dosage and treatment schedules.
• Functional Annotation through Temporal Clustering: By clustering genes based on their temporal expression patterns, time series analysis can predict the functions of poorly characterized or novel genes. This is achieved by associating them with known gene clusters that exhibit similar temporal expression dynamics, thus providing hypotheses about their roles in specific biological processes or pathways.
• Comparative Temporal Dynamics Across Conditions and Species: Time series analysis can be applied to compare temporal gene or protein expression profiles across different experimental conditions, biological contexts, or species. This comparative approach is essential for identifying conserved or divergent temporal regulatory mechanisms, understanding species-specific adaptations, and exploring evolutionary changes in gene regulation.
• Incorporation into Systems Biology Approaches: Time series analysis plays a pivotal role in systems biology by enabling the development of dynamic models for gene regulatory networks. These models utilize temporal data to simulate and forecast the behavior of biological systems under diverse conditions. By integrating time-dependent variables, the analysis provides deeper insights into the temporal dynamics of gene regulation, signal transduction pathways, and metabolic processes, thereby enhancing our understanding of complex biological systems.
• Temporal Profiling: Time series analysis facilitates the development of detailed gene or protein expression profiles over time. This approach helps researchers understand disease trajectories and treatment responses by analyzing dynamic molecular signatures, ultimately contributing to more targeted and effective therapeutic strategies.

CD Genomics Time Series Analysis Workflow

Sample Submission Guidelines

CD Genomics Time Series Analysis Pipeline

Bioinformatics Analysis Content

What Are the Advantages of Our Services?

Expertise in Complex Time Series Analysis

Our team comprises highly skilled biostatisticians and bioinformaticians with extensive experience in handling complex time series data. Whether it's gene expression profiles, protein dynamics, or other omics data, our expertise ensures precise and insightful analysis tailored to your research needs.

Comprehensive Data Processing and Quality Control

We employ advanced data preprocessing techniques, including robust missing data imputation and rigorous normalization, to ensure that your data is of the highest quality before analysis. This meticulous approach minimizes noise and enhances the reliability of the results.

Customized Analytical Solutions

Our services are designed to adapt to the specific needs of your research project. We offer tailored analytical solutions, from trend decomposition to temporal clustering, ensuring that our methods are precisely aligned with your unique research objectives. Each analysis is customized to effectively address the biological questions central to your study.

State-of-the-Art Tools and Technologies

We utilize cutting-edge tools and methodologies, including DTW and ARIMA modeling, to capture and interpret the complexities of time-dependent data. Our integration of these advanced techniques ensures accurate pattern recognition and reliable forecasting.

In-Depth Functional Interpretation

Beyond data analysis, we offer in-depth functional interpretation of your results. Our team conducts comprehensive GO and pathway enrichment analyses to link temporal patterns with underlying biological processes, providing you with a deeper understanding of the mechanisms driving your observations.

High-Quality Visualizations and Reporting

We deliver clear, publication-ready visualizations that effectively communicate your data's temporal dynamics. Our reports include detailed visual summaries, such as heatmaps, line plots, and phase plots, accompanied by expert interpretations to support your scientific findings.

Commitment to Data Security and Confidentiality

We are committed to maintaining the highest standards of data security, ensuring that your research data is kept confidential and secure at all stages of analysis. Our rigorous privacy protocols and ethical practices are designed to safeguard your sensitive information with the greatest care.

Personalized Support and Consultation

We offer continuous, personalized support and consultation, delivering expert advice throughout the entire analysis process. Our dedicated team assists with everything from initial project design to the final interpretation of results, ensuring your research objectives are successfully met.

What Does Time Series Analysis Show?

Data Matrices for Time Series Analysis

In our analysis, we employed time series data to capture dynamic changes in gene expression, protein levels, or other biological markers across different conditions or time points. Below, we provide examples of the types of data matrices used and a visual representation of the results.

Table 1: Example Input Matrix for Time Series Analysis

Time Series Clustering Analysis

Time series clustering analysis is utilized to identify distinct patterns in data across various time points or conditions. This method involves applying clustering algorithms to time series data to categorize samples according to their expression profiles and to observe changes in these profiles over time or under different conditions. Such analysis is crucial for understanding the progression and variability of molecular features linked to specific states or diseases.

Figure 1. Example of Time Series Clustering Analysis

Heatmap of Gene Expression Patterns

The heatmap analysis presents gene expression profiles under varying conditions across multiple time points. Utilizing a computational approach, the study identified distinct gene expression patterns in response to rhizobia infection over 72 hours, focusing on 142 root genes and 190 shoot genes recognized by GeneShift. The heatmaps illustrate these genes' expression trajectories, categorizing them into two primary patterns: shift and non-shift. Each row in the heatmap corresponds to a gene, and each column represents a time point, with log2 (x+1) transformed FPKM values indicating relative expression levels. The trajectory groups (R for root and S for shoot) provide insights into the temporal dynamics of gene expression and their potential functional roles.

Figure 2. Gene Expression Profile in Medicago Plants. (A) Heatmap of 142 root genes. (B) Heatmap of 190 shoot genes. Each row represents a gene, and columns indicate hours post-inoculation. The color bar shows gene expression levels. (Gao, 2022)

Title: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Publication: Cell

Main Methods: Proteomics Data Analysis, Metabolomics Data Analysis, Time Series Analysis

Abstract: The study investigates the molecular alterations in the sera of COVID-19 patients using proteomic and metabolomic approaches. The research is motivated by the critical need to identify early indicators of disease severity, given that severe cases of COVID-19 often require immediate and intensive medical interventions. By analyzing the proteome and metabolome of serum samples from COVID-19 patients, the study aims to uncover specific molecular signatures associated with severe disease. These findings could potentially inform the development of therapeutic strategies and improve the management of patients at risk of severe outcomes.

Research Results:

Profiling and Machine Learning in Severe COVID-19

This study analyzed a cohort of 28 severe COVID-19 patients, alongside matched control groups, to identify molecular changes associated with disease severity. Serum samples were collected soon after hospital admission. The study employed machine learning to develop a classifier for predicting severe cases, validated across independent cohorts. The analysis highlighted significant clinical differences between severe and non-severe patients, including reduced lymphocyte and monocyte counts and elevated CRP and AST levels.

Figure 3. Summary of COVID-19 Patients and Machine Learning Design. (A) Patient demographics and chronic infection indicators. (B) Workflow for developing and validating the machine-learning classifier for severe COVID-19. (Shen, 2020)

Dysregulated Macrophage and Lipid Metabolism in Severe COVID-19

The study identified significant alterations in proteins and metabolites in the sera of severe COVID-19 patients. Notably, multiple apolipoproteins (APOA1, APOA2, APOH, APOL1, APOD, and APOM), associated with macrophage functions, were downregulated in severe cases. This dysregulation aligns with prior findings linking APOA1 levels to the progression from mild to severe COVID-19. Additionally, the study observed substantial downregulation of lipids, including glycerophospholipids and sphingolipids, which are crucial for viral envelope formation, suggesting potential therapeutic targets.

Figure 4. Dysregulated Proteins and Lipids in Severe COVID-19. (A) Heatmap showcasing 50 proteins with significant alterations, particularly in pathways related to macrophage function and lipid metabolism. (B) The expression changes of key apolipoproteins and their statistical significance between non-severe and severe COVID-19 cases. (Shen, 2020)

Identification of Clusters in Proteomic and Metabolomic Data

This study utilized time series analysis to investigate changes in proteomic and metabolomic profiles across different stages of COVID-19 progression. The clustering analysis of 791 proteins and 941 metabolites was performed to identify distinct patterns based on disease status. Proteins were grouped into clusters representing healthy individuals, non-COVID-19 patients, non-severe COVID-19 cases, and severe COVID-19 patients. This clustering highlights how protein expression patterns evolve with disease severity, providing insights into disease progression and the biological processes involved. Similarly, metabolites were categorized into clusters reflecting their expression changes across the same patient groups. This analysis identifies significant shifts in metabolite levels associated with different disease stages.

The clustering approach effectively maps out the temporal changes in molecular profiles, offering a comprehensive view of how proteomic and metabolomic signatures are altered throughout the progression of COVID-19. This time series analysis can be crucial for understanding disease mechanisms and identifying potential biomarkers for early intervention and treatment.

Figure 5. Clustering of Proteins and Metabolites. (A) Clustering of 791 proteins into discrete groups, representing the expression variations across four patient categories: Healthy, non-COVID-19, non-Severe COVID-19, and Severe COVID-19. (B) Clustering of 941 metabolites, highlighting significant changes in levels among the same patient groups. (Shen, 2020)

Lipid Metabolism Dysregulation in Severe COVID-19

In this study, significant alterations in lipid metabolism were observed in severe COVID-19 patients. Over 100 lipids, including sphingolipids and glycerophospholipids, were downregulated. These lipids are crucial components of biomembranes involved in signal transduction and immune responses. The study highlights the continuous decrease of glycerophospholipids post-SARS-CoV-2 infection and the potential therapeutic implications of lipid modulation. Additionally, choline derivatives were notably downregulated, particularly in severe cases, with an upregulation of phosphocholine, likely due to activated macrophage-mediated immunity.

Figure 6. Dysregulated Metabolites in Severe COVID-19. (A) Heatmap displaying 80 regulated metabolites across 10 major classes. (B) The expression changes of eight selected metabolites with significant differences between non-severe and severe COVID-19 cases. (Shen, 2020)

Conclusion

This study identifies key molecular changes in severe COVID-19 through a time series analysis of proteomic and metabolomic data. The research reveals dysregulation in immune pathways and lipid metabolism, offering insights for potential biomarkers and therapeutic targets to improve the management of severe cases.

1. What is Time Series Analysis and why is it important?

Time Series Analysis focuses on evaluating data points that are recorded at consistent time intervals. This type of analysis is essential as it enables researchers to identify and understand underlying patterns, trends, and seasonal variations over time. In various fields such as finance, economics, biology, and environmental science, Time Series Analysis plays a key role in predicting future trends, recognizing cyclic behaviors, and making data-driven decisions based on historical data. This approach helps in gaining insights into temporal dynamics, allowing for more informed and strategic planning.

2. What are the key methods used in Time Series Analysis?

Common methods in Time Series Analysis include Autoregressive Integrated Moving Average (ARIMA) models, Exponential Smoothing, and Seasonal Decomposition of Time Series. These methods help in modeling trends, seasonality, and irregular components of time-dependent data. Advanced techniques like DTW and Long Short-Term Memory (LSTM) networks are also employed for more complex time series data.

3. How do you handle missing data in Time Series Analysis?

Handling missing data is critical to ensuring the integrity of Time Series Analysis. Techniques like linear interpolation, moving averages, and Kalman filtering are commonly used to estimate and fill in missing values. More advanced approaches include Multiple Imputation and Expectation-Maximization (EM) algorithms, which can provide more accurate imputations by considering the temporal structure of the data.

4. What is the difference between time series forecasting and time series analysis?

Time Series Analysis focuses on understanding the historical patterns and components of time-dependent data, such as trends and seasonality. Time Series Forecasting, on the other hand, specifically aims to predict future values based on these historical patterns. While analysis is often the first step to identify key components, forecasting uses this understanding to make predictions about future observations.

5. What are common applications of Time Series Analysis?

Time Series Analysis is widely used across various fields. In biology, it helps in studying gene expression dynamics and ecological data. In finance, it's used for stock price prediction and risk assessment. In economics, it's crucial for analyzing economic indicators like GDP or inflation rates. Environmental science uses it for climate change studies, while in engineering, it helps in monitoring system performance over time.

6. How do you assess the accuracy of a Time Series model?

The accuracy of a Time Series model is typically evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These metrics quantify the difference between predicted and actual values. Cross-validation methods, like rolling-window validation, are also used to test the model's performance on unseen data, ensuring its reliability.

7. What is seasonality in Time Series Analysis?

Seasonality refers to regular, repeating patterns or cycles in a time series that occur at specific intervals, such as daily, monthly, or annually. For example, retail sales often exhibit seasonality with peaks during holiday seasons. Identifying and modeling seasonality is crucial in Time Series Analysis, as it allows for more accurate forecasting by accounting for these recurring fluctuations.

8. What software tools are commonly used for Time Series Analysis?

Several software tools are popular for Time Series Analysis. R and Python are widely used due to their extensive libraries like forecast, tsibble (R), and pandas, statsmodels (Python). MATLAB and SAS are also employed for their robust statistical and forecasting capabilities. Additionally, specialized software like Minitab and SPSS offers user-friendly interfaces for conducting Time Series Analysis.

References

1. Gao, Y.; et al. Time Series Transcriptome Analysis in Medicago truncatula Shoot and Root Tissue During Early Nodulation. Frontiers in Plant Science. 2022, 13, 861639.
2. Shen, B.; et al. Proteomic and Metabolomic Characterization of COVID-19 Patient Sera. Cell. 2020, 182(1), 59-72.