How can you manage laboratory research projects with large amounts of data and complex statistical analyses?

The planning document is referred to as Statistical Analysis Plan. It will be good to have all assumptions check, methods to be implemented based on assumptions and sub-group analysis documented upfront. This will prevent fishing for positivity. Remember, if you beat the data enough, it will show significance in someway. But that is NEVER the right method. Plan upfront and just follow as per the protocol.

Research question and objectives provide study direction and specific, measurable goals, guiding design and method selection. Key points include literature review, ethical considerations, and relevance to avoid unnecessary data.

I am not that good at grasping lab data, so whenever I work with a laboratorian, I have to start by curating the data output report from the machine or instrument. It's often in a *.csv and in a weird format (for a data scientist!). I have to get an example report, then interview a lab tech or other subject matter expert with a lot of patience, and take a lot of notes. I often take screen shots and put them in PowerPoint, then curate them with text and arrows. Later, these images show up in manuals so I can have programmers better than me do the heavy lifting of transferring the data from the output in some organized way. If you want to learn more about my curation techniques, take my course on LinkedIn Learning: https://buff.ly/3310BI4

You should take these decisions before data collection, not after, in order to avoid "fitting by investigator". Ideally, also make public your planned data analysis method in advance.

Tailor data tools to format and size. Common ones: spreadsheets, databases, stats software, scripting, cloud platforms. Consider interoperability, security, and scalability.

Create a clear and logical folder structure for your data.Maintain a comprehensive README file explaining the project, data sources, and file structure.Use version control systems (e.g., Git) to track changes in your code and analysis scripts.Break down your analysis into modular, reusable code chunks.Develop a systematic approach to handle missing data and outliers.Automate repetitive tasks and analyses to increase efficiency.Collaborate using platforms like GitHub or GitLab to enable team members to contribute and track changes.

Effective management of laboratory research data involves organizing and documenting data and code meticulously. Utilize consistent naming conventions, standardized file formats, and clear folder structures. Employ comments, metadata, and readme files to explain content, purpose, and provenance. This meticulous organization enhances research transparency, reproducibility, and shareability.

Consider p-value adjustments for multiple comparison. If you are performing a large amount of inferential hypothesis testing on variables, then something ought to turn significant. So, always for multiple variable comparison, adjust the p-value for multiple comparison like Bonferroni correction. In literal terms, adjustment will increase the difficulty that variable will turn significance by chance (i.e), it will reduce the p-value. For e.g. if the initial p-value set for significance is 0.05, after adjustment, it will become low and say for example p-value may turn out to be 0.03. So, only if the variable assessment shows significance at 0.03, we will consider the variable as significant.

In managing laboratory research data and analysis, it's crucial to apply relevant statistical methods. Select techniques aligned with your data type, distribution, and assumptions, ensuring validity and robustness. Before employing inferential or predictive methods, conduct exploratory data analysis, address outliers, clean data, and impute missing values. When reporting results, include measures of uncertainty, significance, and effect size. This ensures the appropriateness of your statistical approach and enhances the reliability of your findings.

In concluding laboratory research data management and analysis, effective communication of results is paramount. Utilize clear and concise language, supported by tables, graphs, and charts to logically present findings. Emphasize main discoveries, acknowledge limitations, and discuss implications in connection with your research question and objectives. Properly cite data sources, methods, and tools, and consider sharing data and code for transparency. Through transparent communication, you not only demonstrate research quality and integrity but also contribute to advancing knowledge in your field.

The use of tables or graphs, for example, to show a trend should be the response to the question and the objective set at the initial stage of the analysis. A comprehensive chart that doesn't address what was intended to be known or investigated does not fulfill the required function. I also believe it is important for some people to demonstrate how they arrived at the final result and to compare it with what is expected or was expected to happen.