imputing-proteomics-data

Imputing Proteomics Data

Impute missing values in protein intensity matrices for downstream analysis requiring complete data.

When to Impute

Impute when:

• Downstream analysis requires complete data (e.g. PCA, COMBAT batch correction)
• Missingness rate is moderate (<30-50% per feature)

Do NOT impute when:

• Downstream methods handle missing data natively (mixed-effects models, limma)
• Feature missingness is high - instead remove highly missing features with low data support.

Key missingness patterns

• MNAR (Missing Not At Random): Low-abundance proteins below detection limit. Shows correlation between intensity and missingness.
• MAR (Missing At Random): Ion suppression, peptide competition. More common in DDA.
• MCAR (Missing Completely At Random): Stochastic dropouts. Random pattern.

Imputation Workflow

Copy this checklist and track progress:

Analysis step progress:
- [ ] **Prepare data**
- [ ] **Assess** missingness patterns
- [ ] **Select** method
- [ ] **Apply imputation**
- [ ] **Validate** imputation quality
      If quality criteria not met: Restart at step `assess`

Workflow

Prepare data

Remove features with high missingness before imputation due to little data support. For typical datasets, features with <50% completeness should be removed. Assess missingness within biological groups, not globally. For example, a cell-type marker "missing" in 90% of cells but present in 100% of that cell type is informative.

Ensure that data is log-transformed data if not already as most methods assume log-scale.

Assess missingness patterns

Before imputation, assess missingness patterns.

Calculate missingness rates per feature.

Visualize intensity vs missingness. If missingness is higher for features with low intensity, this corresponds to a MNAR patterns. If the missingness is roughly independent, this corresponds to an MCAR or MAR pattern.

Method Selection

Rationale: Prefer methods that consider the global data structure over methods that only consider local structure over methods that provide a single point estimate for all samples.