GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

<div><p>Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: <a href="https://github.com/WandeRum/GSimp" target="_blank">https://github.com/WandeRum/GSimp</a>.</p></div

Sequentially parameters updating in GSimp.

<p>The first 500 iterations out of a total of 2000 (100×20) iterations using GSimp where <i>ŷ</i>, <i>ỹ</i> and <i>σ</i> represent fitted value, sample value and standard deviation correspondingly.</p

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

Comparisons of imputed values and original values on one variable.

<p>Scatter plots of imputed values (X-axis) and original values (Y-axis) on one example missing variable while non-missing elements represented as blue dots and missing elements as red dots based on four imputation methods: HM (upper left), QRILC (upper right), kNN-TN (lower left), and GSimp (lower right). Rug plots show the distributions of imputed values and original values.</p

Evaluations of different imputation methods using labeled approaches.

<p>Pearson's correlation between log-transformed p-values of student’s t-tests on FFA dataset (upper left) and BA dataset (upper right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross). PLS-Procrustes sum of squared errors on FFA dataset (lower left) and BA dataset (lower right) along with different numbers of missing variables based on four imputation methods: HM (red circle), QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross).</p

Evaluations of different imputation methods using TPR for various <i>p</i>-cutoffs on simulation dataset.

<p><i>TPR</i> along with different numbers of missing variables based on three imputation methods: QRILC (green triangle), GSimp (blue square), and kNN-TN (purple cross) among different p-cutoff = 0.05 (left panel), and 0.01 (right panel).</p

Global and Targeted Metabolomics Evidence of the Protective Effect of Chinese Patent Medicine <i>Jinkui Shenqi</i> Pill on Adrenal Insufficiency after Acute Glucocorticoid Withdrawal in Rats

Glucocorticoids are commonly used in anti-inflammatory and immunomodulatory therapies, but glucocorticoid withdrawal can result in life-threatening risk of adrenal insufficiency. Chinese patented pharmaceutical product <i>Jinkui Shenqi</i> pill (JKSQ) has potent efficacy on clinical adrenal insufficiency resulting from glucocorticoid withdrawal. However, the underlying molecular mechanism remains unclear. We used an animal model to study JKSQ-induced metabolic changes under adrenal insufficiency and healthy conditions. Sprague–Dawley rats were treated with hydrocortisone for 7 days with or without 15 days of JKSQ pretreatment. Sera were collected after 72 h hydrocortisone withdrawal and used for global and free fatty acids (FFAs)-targeted metabolomics analyses using gas chromatography/time-of-flight mass spectrometry and ultraperformance liquid chromatography/quadruple time-of-flight mass spectrometry. Rats without hydrocortisone treatment were used as controls. JKSQ pretreatment normalized the significant changes of 13 serum metabolites in hydrocortisone-withdrawal rats, involving carbohydrates, lipids, and amino acids. The most prominent effect of JKSQ was on the changes of FFAs and some [product FFA]/[precursor FFA] ratios, which represent estimated desaturase and elongase activities. The opposite metabolic responses of JKSQ in adrenal insufficiency rats and normal rats highlighted the “<i>Bian Zheng Lun Zhi</i>” (treatment based on ZHENG differentiation) guideline of TCM and suggested that altered fatty acid metabolism was associated with adrenal insufficiency after glucocorticoid withdrawal and the protective effects of JKSQ