Left Censored Missing Value Classification

left-censored-missing-value-classification

Summary

Identify and flag left-censored missing values in metabolomics data by distinguishing them from other missingness mechanisms (MCAR/MAR) based on detection limits and instrumental thresholds. This classification is essential for selecting appropriate imputation strategies, as left-censored missingness violates MCAR assumptions and requires truncated distributional methods.

When to use

Apply this skill when you have a metabolomics dataset (LC/MS or GC/MS) with missing values and need to determine which are below the limit of detection (LOD) or limit of quantification (LOQ). Left-censored classification is necessary when the missingness is informative—i.e., when a metabolite feature failed to be quantified or detected due to instrumental sensitivity constraints rather than random absence or biological non-detection.

When NOT to use

• Do not apply this skill to datasets where missingness is already confirmed to be MCAR/MAR (e.g., random sample drop-out, incomplete data collection) without instrumental thresholds—use non-truncated imputation instead.
• Do not use this skill when the data has already undergone value imputation or thresholding—classification must occur on raw or post-retrieval, pre-imputation data.
• Do not apply to datasets where the detection limit is unknown or undocumented, as the distinction between left-censored and other missingness cannot be reliably made.

Inputs

• metabolomics data matrix (rows=samples, columns=metabolite features) with NA or zero encoding for missing values
• instrumental metadata: LOD/LOQ thresholds per feature or per assay batch
• optional: manual retrieval flags or post-retrieval annotation for untargeted data

Outputs

• classified missing-value indicator matrix (left-censored vs. other missingness)
• detection limit boundary per feature (minimum non-missing value or quantile)
• missingness mechanism summary (left-censored proportion, MNAR evidence)

How to apply

Load your metabolomics data matrix (rows=samples, columns=metabolite features) with missing values encoded as NA or zero. Compare the distribution of non-missing values in each feature against the instrumental detection threshold (LOD/LOQ) for that feature or assay batch. Flag any missing values that fall structurally below the minimum non-missing value as left-censored; this indicates the metabolite was present but undetectable. If manual retrieval has been performed (as in untargeted GC/MS workflows), exclude manually recovered values from the left-censored pool. Document the detection limit boundary for each feature, as this upper bound (hi='min' or quantile) will constrain the imputation model. Missingness in untargeted data with overlap between non-missing and retrieved values may indicate MCAR/MAR instead, requiring non-truncated methods.

Related tools

• GSimp (provides missing value classification and left-censored imputation wrapper functions) — https://github.com/WandeRum/GSimp
• imputeLCMD (R package) (provides QRILC and impute.QRILC functions for quantile regression imputation of left-censored data)
• Trunc_KNN (kNN-TN algorithm for truncation-aware k-nearest neighbors imputation of left-censored data)

Examples

source('GSimp.R'); NA_pos <- which(is.na(untargeted_data), arr.ind=TRUE); data_raw_log <- untargeted_data %>% log(); data_raw_log_qrilc <- impute.QRILC(data_raw_log) %>% extract2(1); hi_bound <- sapply(data_raw_log_qrilc, function(x) min(x, na.rm=TRUE)); left_censored_flag <- data.frame(row=NA_pos[,1], col=NA_pos[,2], is_left_censored=(data_raw_log_qrilc[NA_pos] < hi_bound[NA_pos[,2]]))

Evaluation signals

• Verify that the proportion of left-censored values aligns with expected instrumental sensitivity and sample/compound complexity (e.g., untargeted GC/MS ~0.8% post-retrieval; targeted LC/MS with LOQ/LOD failures ~2.1%).
• Check that the detected upper bounds (minimum non-missing or quantiles per feature) are consistent with reported instrumental LOD/LOQ and do not fall above feature-specific maxima.
• Confirm that left-censored classification is mutually exclusive from MCAR/MAR (i.e., overlapping distributions in untargeted data after retrieval should reclassify to MCAR, not left-censored).
• Validate that the classified left-censored positions match the positions later used in the Gibbs sampler truncation bounds (lo=-Inf, hi='min') without drift or re-encoding.
• Cross-check classified missing positions against instrumental log files or batch metadata to confirm missingness co-occurs with expected LOD/LOQ exceedances, not with sample preparation failures.

Limitations

• Classification depends critically on accurate LOD/LOQ metadata; undocumented or batch-variable thresholds will lead to misclassification.
• Untargeted metabolomics workflows with manual retrieval may blur the boundary between left-censored and MCAR/MAR; post-retrieval overlap in distributions requires re-evaluation of missingness mechanism.
• Classification is feature-specific: a single missing value may be left-censored in one context (strict LOQ) and MCAR in another (loose LOQ); threshold selection (e.g., minimum vs. 10% quantile) alters classification outcome.
• Left-censored classification does not account for right-censored missingness (high-intensity saturation or overflow), which requires opposite bounds (lo='max', hi=Inf) and is not addressed by GSimp's core design.
• If detection limits are estimated post-hoc from the non-missing distribution (e.g., 'min' value), circular reasoning can inflate left-censored counts; independent instrumental calibration is preferable.

Evidence

• [other] Identify and flag left-censored missing values (detection limit threshold) using GSimp's missing value classification.: "Identify and flag left-censored missing values (detection limit threshold) using GSimp's missing value classification."
• [other] GSimp implements a Gibbs sampler based approach for left-censored missing value imputation, with core functions provided in GSimp.R that enable application to metabolomics datasets.: "GSimp implements a Gibbs sampler based approach for left-censored missing value imputation, with core functions provided in GSimp.R that enable application to metabolomics datasets."
• [readme] The targeted LC/MS dataset contains 40 samples and 41 variables with 88 missing elements are failed to be quantified due to LOQ/LOD.: "The targeted LC/MS dataset contains 40 samples and 41 variables with 88 missing elements are failed to be quantified due to LOQ/LOD."
• [readme] we found overlaps between non-missing values and retrieved missing values. Thus, we assumed that the majority of missingness in untargeted GC/MS-based metabolomics data are MCAR/MAR.: "we found overlaps between non-missing values and retrieved missing values. Thus, we assumed that the majority of missingness in untargeted GC/MS-based metabolomics data are MCAR/MAR."
• [readme] lo=-Inf, hi='min' are default setting for left-censored missing values where the upper bound is set to the minimum value of non-missing part: "lo=-Inf, hi='min' are default setting for left-censored missing values where the upper bound is set to the minimum value of non-missing part"