I have a proteomics dataset with missing values. I tried some strategies, but the point is that there are sets completely with missing values.
The last strategy was to apply MissForest in python and it does not handle completely missing value columns.
Any ideas on how to deal with this?
Thanks in advance.
You have to get familiar with the dataset, with respect to the pattern/the quantity of the missing data.
If the amount of missing data is small, you can remove rows or columns with missing values.
If it is not, you can use different imputation techniques - replace missing values by "most likely" values based on your complete data:
- Mean/Median Imputation
- kNN Neighbors Imputation
- for more advanced imputation techniques, you have the "fancyimpute" library in Python or also MICE (Multiple Imputation by Chained Equations).
Dealing with missing data in proteomics is a crucial step to ensure the quality and reliability of the analysis. When choosing the most appropriate method, it's important to consider the nature of the missing data and the study's goals. Here are some strategies that can be used:
- Imputation: This involves replacing missing values with estimated ones. The goal is to minimize the bias introduced by imputation while preserving as much of the original data as possible.
- Removal of Incomplete Data: In cases where the missing data is extensive or imputation is not suitable, it may be necessary to remove peptides or proteins with insufficient samples for analysis.
- Thresholding: Setting a threshold for complete observations, such as the "80% rule", which keeps only those proteins identified in at least 80% of samples.
- Data Analysis Techniques: Some data analysis methods can handle missing values directly, such as certain types of clustering or machine learning algorithms that can work with incomplete data matrices.
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