What are the best practices for omics data quality control and preprocessing?

2 Perform data cleaning

The next step is to perform data cleaning, which involves removing or correcting any errors, outliers, or artifacts that may affect the downstream analysis. For example, you may need to trim or filter low-quality reads, remove adapters or contaminants, correct for batch effects or confounding factors, or impute missing values. You can use tools and software such as Trimmomatic, Cutadapt, Picard, ComBat, and MICE to perform data cleaning. You should also document and report the steps and parameters used for data cleaning, and compare the quality metrics before and after cleaning.

3 Apply data normalization

The third step is to apply data normalization, which aims to reduce the unwanted variation and bias that may arise from technical or biological factors, such as sample preparation, sequencing depth, or gene expression levels. Data normalization can help to improve the comparability and interpretability of the data across different samples, conditions, or experiments. You can use various methods and software to perform data normalization, such as TMM, RPKM, CPM, DESeq2, edgeR, and limma. You should also evaluate the performance and suitability of different normalization methods for your data and research question.

4 Conduct data transformation

The fourth step is to conduct data transformation, which involves changing the scale or distribution of the data to meet the assumptions or requirements of the downstream analysis methods. Data transformation can help to enhance the signal-to-noise ratio, reduce the skewness or heteroscedasticity, or adjust for non-linear relationships. You can use various methods and software to perform data transformation, such as log, square root, rank, or Box-Cox transformation, or variance-stabilizing transformation. You should also check the distribution and variance of the data before and after transformation, and justify your choice of transformation method.

5 Perform data integration

The fifth step is to perform data integration, which involves combining or comparing different types of omics data, such as gene expression, protein abundance, or metabolite concentration. Data integration can help to reveal the interactions and pathways that underlie the biological processes and phenotypes of interest. You can use various methods and software to perform data integration, such as correlation, co-expression, network, or multivariate analysis, or integrative omics platforms such as iOmicsPASS, mixOmics, or DIABLO. You should also consider the challenges and limitations of data integration, such as data heterogeneity, scalability, or interpretability.

6 Choose data visualization

The sixth step is to choose data visualization, which involves presenting and exploring the data in graphical or interactive formats. Data visualization can help to communicate and summarize the main findings, patterns, and trends of the data analysis, as well as to identify any potential problems or outliers. You can use various tools and software to create data visualization, such as ggplot2, plotly, shiny, or dash, or omics-specific visualization tools such as clusterProfiler, gplots, or ComplexHeatmap. You should also follow the principles and best practices of data visualization, such as clarity, accuracy, consistency, and aesthetics.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?