Sample interview questions: How do you approach data imputation or missing value estimation in single-cell transcriptomics data analysis?
Sample answer:
Approaching Data Imputation in Single-Cell Transcriptomics Data Analysis
Assess missingness patterns:
* Determine the extent and distribution of missing values.
* Identify potential biases or technical artifacts that may have caused the missingness.
Select appropriate imputation method:
* K-nearest neighbors (KNN): Impute missing values based on the values of similar cells in the expression space.
* Matrix factorization: Decompose the expression matrix into a product of matrices that can be used to estimate missing values.
* Random forest: Predict missing values using a machine learning model trained on observed data.
Evaluate imputation quality:
* Use metrics such as the root mean squared error (RMSE) or the mean absolute error (MAE) to assess the accuracy of imputed values.
* Perform sens… Read full answer
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