Rank-based Regression Research Articles

HYENA detects oncogenes activated by distal enhancers in cancer.

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm 'HYENA' to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

Brain-Derived Neurotrophic Factor rs6265 polymorphism is associated with severe cancer-related fatigue and neuropathic pain in female cancer survivors

PurposeThis study examined the relationships between a single-nucleotide polymorphism (SNP) of brain-derived neurotrophic factor (BDNF) rs6265 and psychoneurological (PN) symptoms in female cancer survivors.MethodsThis secondary analysis examined 393 study participants. In addition to demographic variables, self-reported PN symptom scores (anxiety, bodily pain, depression, fatigue, neuropathic pain, and sleep disturbance) were collected using the Patient-Reported Outcomes Measurement Information System and 36-Item Short-Form Health Survey. Buccal swab samples were collected to obtain genotypes for BDNF rs6265 (Val/Val, Val/Met, or Met/Met). The PN symptom scores were compared across genotypes, and the relationships were examined using a regression model. We also explored correlations between different symptoms within each genotype.ResultsParticipants with the Met/Met genotype reported significantly worse cancer-related fatigue and neuropathic pain, which was confirmed by rank-based regression analysis. In addition, cancer-related fatigue was correlated with other PN symptoms, particularly depression. These correlations were stronger in study participants with the Met/Met genotype than those with other genotypes.ConclusionOur study suggests that female cancer survivors with the Met/Met genotype of BDNF rs6265 are likely to experience worse cancer-related fatigue and neuropathic pain and that cancer-related fatigue is a good predictor of co-occurring PN symptoms in this population.Implications for Cancer SurvivorsOur findings advance the scientific community's understanding of cancer-related PN symptoms experienced by female cancer survivors, especially the unique role of BDNF rs6265 polymorphism in these symptoms. Our findings offer valuable insights for clinical practice that the symptom experience among female cancer survivors may vary based on BDNF genotypes.

Understanding the Relationship Between Decreases in Social Security Benefits and Intergenerational Inequalities in Mental Health.

It is well-established that mental health follows similar patterns across generations. However, little is known how structural factors, such as those related to social security reforms, may impact this relationship. Our aim was to quantify the strength of association in mental health between parents and their adolescent children, and to explore how much of this correlation is explained by decreases in benefits. We used data from U.K. Household Longitudinal Study (2009-2019) from which we matched youth data to their parents, and split the sample into single- and dual-parent households. To estimate the intergenerational correlations, we estimated a series of unit- and rank-based regression models of standardized and time-averaged mental health measures for adolescents and their parents. Our findings suggest that there are statistically significant intergenerational associations in mental health between parents and children for both single- and dual-parent households, with the relationship being stronger for single-mother households. Benefit losses explain a small proportion of this association, for both single-mother and dual-parent households. Nevertheless, they are negatively associated with the mental health of adolescents in dual-parent households-independently of both adolescent and parental characteristics. Such negative effects should be considered when designing and evaluating future social security benefit policies.

Abstract 3136: HYENA detects non-coding genes activated by distal enhancers in cancer

Abstract Genome instability is a hallmark of cancer, resulting in the accumulation of mutation. Among different types of mutations, structural variants (SVs) widely spread in cancer genomes and can be important cancer drivers, demonstrated in multiple cancer types. SVs can induce oncogene activation by redirecting enhancers to promoter regions or disrupting chromatin topology. These events are called “enhancer hijacking” and known to upregulate oncogenes in multiple cancers. Enhancers are among the best understood cis-regulatory elements and are essential regulators for precise gene expression in normal tissue. Since SV associated gene deregulation has been shown in multiple cancer types, it is a feasible way to infer putative oncogenes by detecting enhancer hijacking events. Taking advantage of next generation sequencing, bioinformatic tools were developed to study pan-histology protein-coding gene deregulation caused by SVs. However, algorithms that can unbiasedly detect enhancer hijacking in both coding and non-coding genes are yet to be developed. Here we present a computational algorithm “HYENA” to identify putative oncogenes activated by enhancer hijacking using whole-genome sequencing and RNA sequencing data. It deploys rank-based regression model to test whether an SV breakpoint located close to a gene significantly increases the gene expression. Permutation tests were used to generate a false discovery rate for each gene. By benchmarking with existing tools serving similar purposes, we confirmed that HYENA has superior capabilities to infer enhancer hijacking events. We detected 192 oncogene candidates in 1,148 tumor samples from 25 cancer types. While confirming the known cases of enhancer hijacking, we also identified numerous novel oncogene candidates, of which a substantial proportion was non-coding genes. Specifically, we found that a long non-coding RNA TOB1-AS1 was activated by rearranged enhancers in 10% of pancreatic cancer samples. Furthermore, experimental validation with pancreatic cancer cell lines and immunodeficient mice indicated that high expression of TOB1-AS1 is capable to promote cancer cell invasion and tumor metastasis, suggesting it is a novel cancer driver. As the existing treatments for pancreatic cancer are largely inefficacious and patients still have poor prognosis, new targets are urgently needed to improve pancreatic cancer treatments. Although the molecular mechanisms underlying TOB1-AS1 promoted metastasis remain to be unveiled, this non-coding gene can be a promising target in the patients carrying this enhancer hijacking event. In summary, our results demonstrated that HYENA is a sensitive and reliable tool to infer enhancer hijacking activated oncogenes and spot new potential targets to treat cancer. Citation Format: Anqi Yu, Ali E. Yesilkanal, Ashish Thakur, Fan Wang, Xiaoyang Wu, Alexander Muir, Xin He, Francois Spitz, Lixing Yang. HYENA detects non-coding genes activated by distal enhancers in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3136.

Serum levels of IL-6 are associated with cognitive impairment in the salus in apulia population-based study

Growing evidence suggests that inflammation contributes to brain aging and neurodegeneration. This study investigates the relationship between global cognitive as well executive function and the inflammatory markers IL-6, CRP, and TNF-α in a population-based study of older adults. A population-based sample, of older people in Southern Italy, was enrolled. We measured serum levels of IL-6, CRP, and TNF-α. We also administered two neuropsychological tests: Mini-Mental State Examination and Frontal Assessment Battery. Rank-based regression models were performed to investigate the relationship between inflammatory markers and cognitive functions, including major demographic and clinical confounders for adjustment. The sample consisted of 1929 subjects aged between 65 and 95 years. Multivariate linear regression analysis revealed that higher serum levels of IL-6 were associated with lower MMSE and FAB scores even after adjustment for demographic data and cardiovascular risk factors. No significant associations were found between cognitive functioning and serum levels of CRP and TNF-α. Our results suggest that higher levels of IL-6 were associated with cognitive impairment in an older adult population of Southern Italy.

Transcranial direct current stimulation leads to faster acquisition of motor skills, but effects are not maintained at retention.

Practice is required to improve one’s shooting technique in basketball or to play a musical instrument well. Learning these motor skills may be further enhanced by transcranial direct current stimulation (tDCS). We aimed to investigate whether tDCS leads to faster attainment of a motor skill, and to confirm prior work showing it improves skill acquisition and retention performance. Fifty-two participants were tested; half received tDCS with the anode on primary motor cortex and cathode on the contralateral forehead while concurrently practicing a sequential visuomotor isometric pinch force task on Day 1, while the other half received sham tDCS during practice. On Day 2, retention of the skill was tested. Results from a Kaplan-Meier survival analysis showed that participants in the anodal group attained a pre-defined target level of skill faster than participants in the sham group (χ2 = 9.117, p = 0.003). Results from a nonparametric rank-based regression analysis showed that the rate of improvement was greater in the anodal versus sham group during skill acquisition (F(1,249) = 5.90, p = 0.016), but there was no main effect of group or time. There was no main effect of group or time, or group by time interaction when comparing performance at the end of acquisition to retention. These findings suggest anodal tDCS improves performance more quickly during skill acquisition but does not have additional benefits on motor learning after a period of rest.

The Use of a Production Function to Evaluate Caribbean Ocean Health Conflicts and Economic Development: Policy Implications.

Central American and Caribbean (CAC) countries enjoy diverse marine environments. The oceans that enclose these coastlines contribute significantly to their economic growth. Industrial expansion and tourism place pressure on the marine ecosystems causing a decline in ocean health. To evaluate the cause of ocean health changes we investigated the factors influencing CAC ocean health using a health production function. Using Rank-Based Regression and the set of extractive, cultural and human well-being and services goals measuring Ocean Health Index (OHI), data from the World Bank, and the Human Development Index we developed a production function for CAC countries ocean health. Results show that all regional OHI scores, but Tourism and Recreation, the main income earning industry for most of the CAC countries, are less than the global score with four of the goals less than 40. The production function shows that all the goals, but the biodiversity sub-goal species, positively influencing OHI. Two climatic related variables, Nitrous Oxide and Carbon Dioxide negatively influenced OHI. The results are important to policy makers as they decide on the need to make greater effort towards improving sustainable contribution of CAC ocean resources to the blue economy.

Examining Systemic Risk using Google PageRank Algorithm: An Application to Indian Non-Bank Financial Companies (NBFCs) Crisis

In the recent financial crises, attention has shifted towards "too-central-to-fail" to recognize the sources of systemic risk. The NBFC Crisis of 2018-19 adversely affected other financial institutions and the real economy of India. The NBFCs crisis highlighted the role of smaller institutions in perpetuating and amplifying the crisis. Thus, the present study models the interconnection of NBFCs with the rest of financial institutions using a complex Granger-causality network based on returns data. The PageRank algorithm identifies the central and important nodes and ranks financial institutions in pre-crisis and crisis periods. The financial institutions are also ranked based on the maximum percentage loss suffered during the crises. Using non-parametric rank-based regression, the PageRank ranking of financial institutions in the pre-crises period (explanatory variable) is regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable) along with Leverage and Size as control variables. We found that PageRank from pre-crisis can significantly identify most financial institutions that suffered loss during NBFCs crises even in the presence of control variables.

Another look at regression analysis using ranked set samples with application to an osteoporosis study.

Statistical learning with ranked set samples has shown promising results in estimating various population parameters. Despite the vast literature on rank-based statistical learning methodologies, very little effort has been devoted to studying regression analysis with such samples. A pressing issue is how to incorporate the rank information of ranked set samples into the analysis. We propose two methodologies based on a weighted least squares approach and multilevel modeling to better incorporate the rank information of such samples into the estimation and prediction processes of regression-type models. Our approaches reveal significant improvements in both estimation and prediction problems over already existing methods in the literature and the corresponding ones with simple random samples. We study the robustness of our methods with respect to the misspecification of the distribution of the error terms. Also, we show that rank-based regression models can effectively predict simple random test data by assigning ranks to them a posteriori using judgment poststratification. Theoretical results are augmented with simulations and an osteoporosis study based on a real data set from the Bone Mineral Density (BMD) program of Manitoba to estimate the BMD level of patients using easy to obtaincovariates.

Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy

In the era of immunotherapy, there lacks of a reliable genomic predictor to identify optimal patient populations in combined radiotherapy and immunotherapy (CRI). The purpose of this study is to investigate whether genomic scores defining radiosensitivity are associated with immune response. Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, radiosensitivity index (RSI) was calculated. A total of 12832 primary tumours across 11 major cancer types were analysed for the association with DNA repair, cellular stemness, macrophage polarisation, and immune subtypes. Additional 585 metastatic tissues were extracted from MET500. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Proteomic differential analysis was used to identify significant proteins according to RSI categories. Gene Set Variance Analysis (GSVA) was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. RSI was significantly associated with homologous DNA repair, cancer stemness and immune-related molecular features. Lower RSI was associated with higher fraction of M1 macrophage. Differential proteomic analysis identified significantly higher TAP2 expression in RSI-Low colorectal tumours. In the TCGA cohort, dominant interferon-γ (IFN-γ) response was characterised by low RSI and predicted better response to programmed cell death 1 (PD-1) blockade. In conclusion, in addition to radiation response, our study identified RSI to be associated with various immune-related features and predicted response to PD-1 blockade, thus, highlighting its potential as a candidate biomarker for CRI.

Connection Between Tumor Radiosensitivity and Response to Immunotherapy in 11 Major Epithelial Cancer Types

Background: Radiotherapy has gained increasing attention as an effective cytotoxic agent to improve therapeutic response to immunotherapy. However, synergic effect is seldom seen clinically and reliable biomarkers for optimal patient selection remains to be identified. Radiosensitivity index (RSI) is a well-known measure of tumor’s response to radiotherapy, which is derived from genomic data and system biology. Its association with the immune landscape in cancer is unclear. Methods: Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, RSI was calculated. A total of 12,832 primary tumors across 11 major cancer types were analyzed. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Gene set variance analysis was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. Findings: We found that RSI was significantly associated with homologous DNA repair, cancer stemness and various cancer-specific molecular features. Tumors with low RSI were associated with significantly higher portion of M1 macrophage, relative to M2 macrophage, and higher IFNG expression. Additionally, dominant interferon-γ (IFN-γ) response was characterized by low RSI and potentially predicted better response to programmed cell death 1 (PD-1) blockade. Interpretation: RSI is strongly associated with immune response and efficacy to PD-1 blockade. IFN-γ dominant status could be estimated by RSI, providing an alternative selection strategy to identify suitable patients for combined radiotherapy and immunotherapy. Funding Statement: This study was supported by study project of Tri-Service General Hospital (TSGH-D109063). Declaration of Interests: The authors declare that they have no competing interests.

An Assessment of Physical Education Teacher Candidates’ Dispositions

The study assessed physical education teacher candidates’ (TCs) dispositions toward teaching. Participants included 72 physical education TCs (80.56% males and 19.44% females). TCs completed the Missouri Educator Profile (MEP) once, as part of the requirements for admission to the teacher education program at their institution. The MEP is a 10-point Likert scale with six subscales: Achievement (AT), Social Influence (SI), Interpersonal Effectiveness (IP), Self-Adjustment (SA), Conscientiousness (CN), and Practical Intelligence (PI). The predictor variables were TCs’ sex, PE major status, cumulative GPA (GPA), number of hours attempted (HRA), number of hours passed (HRP) at the time of taking the MEP, transfer student status (TSS), and first semester of enrollment (FSE). Descriptive data indicated that the SA subscale (45.83%) had the highest percentage of TCs with high scores, followed by SI (33.33%). Conversely, the lowest percentage of TCs with high scores was in PI (5.56%). Nonparametric rank-based regression analyses showed that sex and PE major status were significant predictors of SI, IP, and PI. Female TCs had lower scores than their male counterparts on these subscales. TCs who maintained their status as PE majors after taking the MEP had lower scores than those who changed majors. Furthermore, HRA and HRP significantly predicted the IP and PI subscales. TCs with higher number of hours attempted had higher scores on the IP and PI subscales. Conversely, the higher the number of hours passed, the lower the score on the IP and PI subscales. All the predictor variables were not significant for subscales AT, SA, and CN. Finally, transfer student status was not a significant predictor of any of the dispositions. Physical Education Teacher Education programs need to teach and assess TCs’ dispositions on a regular basis. Dispositional assessment data could serve as valuable source of feedback for teacher education program improvement.

An Updated Algorithm for Moderate Censoring in Time-to-Event Data Using Rank-based Regression

Aim: To propose an updated algorithm with an extra step added to the Newton-type algorithm used in robust rank based non-parametric regression for minimizing the dispersion function associated with Wilcoxon scores in order to account for the effect of covariates.
 Methodology: The proposed accelerated failure time approach is aimed at incorporating right random censoring in survival data sets for low to moderate levels of censoring. The existing Newton algorithm is modified to account for the effect of one or more covariates. This is done by first applying Mantel scores to residuals obtained from a regression model, and second by minimizing the dispersion function of these scored residuals. Diagnostic check of the model fit is performed by observing the distribution of the residuals and suitable Bent scores are considered in the case of skewed residuals. To demonstrate the efficacy of this method, a simulation study is conducted to compare the power of this method under three different scenarios: non-proportional hazard, proportional and constant hazard, and proportional but non-constant hazard.
 Results: In most situations, this method yielded reasonable estimates of power for detecting an association of the covariate with the response as compared to popular parametric and semi-parametric approaches. The estimates of the regression coefficient obtained from this method were evaluated and were found to have low bias, low mean square error, and adequate coverage. In a real-life example pertaining to pancreatic cancer study, the proposed method performed admirably well and provided a more realistic interpretation about the effect of covariates (age and Karnofsky score) compared to a standard parametric (lognormal) model.
 Conclusion: In situations where there is no clear best parametric fit for time-to-event data with moderate level of censoring, the proposed method provides a robust alternative to obtain regression coefficients (both adjusted and unadjusted) with a performance comparable to that of a proportional hazards model.

General local rank estimation for single-index varying coefficient models

In this paper, we consider rank-based estimation of the index coefficient and the functional regression coefficients for the single-index varying coefficient regression model. The consistency and asymptotic normality of the proposed estimators are established under mild assumptions. An extensive Monte-Carlo simulation study demonstrates the robustness and efficiency of the proposed estimators compared to the least squares estimators. An application in ecology is provided and shows that the rank-based regression procedure effectively provides more accurate estimates compared to its least squares counterpart.

A spatial rank-based EWMA chart for monitoring linear profiles

ABSTRACTProfile monitoring has been recently considered as one of the most promising areas of research in statistical process monitoring (SPM). It is a technique for monitoring the stability of a functional relationship between a dependent variable and one or more independent variables over time. The monitoring of linear profiles is the most popular one because the relationship between the dependent variable and the independent variables is easy to describe by linearity, in addition to its flexibility and simplicity. Furthermore, almost all existing charting schemes for monitoring linear profiles assume that error terms are normally distributed. In some applications, however, the normality assumption of error terms is not justified. This makes the existing charting schemes not only inappropriate but also less efficient for monitoring linear profiles. In this article, based on the spatial rank-based regression, we propose a charting method for monitoring linear profiles where the error terms are not normally distributed. The charting scheme applies the exponentially weighted moving average (EWMA) to the spatial rank of the vector of the Wilcoxon-type rank-based estimators of regression coefficients and a transformed error variance estimator. Performance properties of the proposed charting scheme are evaluated and compared with an existing charting method based on multivariate sign in terms of the in-control (IC) and out-of-control (OC) average run length (ARL). Finally, a real example is used to demonstrate the applicability and implementation of the proposed charting scheme.

Robust continuous piecewise linear regression model with multiple change points

This paper considers a robust piecewise linear regression model with an unknown number of change points. Our estimation framework mainly contains two steps: First, we combine the linearization technique with rank-based estimators to estimate the regression coefficients and the location of thresholds simultaneously, given a large number of change points. The associated inferences for all the parameters are easily derived. Second, we use the LARS algorithm via generalized BIC to refine the candidate threshold estimates and obtain the ultimate estimators. The rank-based regression guarantees that our estimators are less sensitive to outliers and heavy-tailed data, and therefore achieves robustness. Simulation studies and an empirical example on BMI and age relationship illustrate the proposed method.

Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms.

This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach. The intent is to demonstrate how a nonparametric regression based on a rank-based estimator can be used to estimate a Cobb-Douglas production function using data on maize production from Ghana. The nonparametric results are compared to common parametric specification using the ordinary least squares regression. Results of the study indicate that the estimated coefficients of the CobbDouglas Model using the Least squares method and the rank-based regression analysis are similar. Findings indicated that in both estimation techniques, land and Equipment had a significant and positive influence on output whilst agrochemicals had a significantly negative effect on output. Additionally, seeds which also had a negative influence on output was found to be significant in the robust rank-based estimation, but insignificant in the ordinary least square estimation. Both the least squares and rank-based regression suggest that the farmers were operating at an increasing returns to scale. In effect this paper demonstrate the usefulness of the rank-based estimation in production analysis.
 JEL CODE: Q18, D24, Q12, C1 and C67

THE STAR CLUSTER MASS–GALACTOCENTRIC RADIUS RELATION: IMPLICATIONS FOR CLUSTER FORMATION

ABSTRACT Whether or not the initial star cluster mass function is established through a universal, galactocentric-distance-independent stochastic process, on the scales of individual galaxies, remains an unsolved problem. This debate has recently gained new impetus through the publication of a study that concluded that the maximum cluster mass in a given population is not solely determined by size-of-sample effects. Here, we revisit the evidence in favor and against stochastic cluster formation by examining the young ( ≲ ?> a few × 10 8 ?> year old) star cluster mass–galactocentric radius relation in M33, M51, M83, and the Large Magellanic Cloud. To eliminate size-of-sample effects, we first adopt radial bin sizes containing constant numbers of clusters, which we use to quantify the radial distribution of the first- to fifth-ranked most massive clusters using ordinary least-squares fitting. We supplement this analysis with an application of quantile regression, a binless approach to rank-based regression taking an absolute-value-distance penalty. Both methods yield, within the 1 &sgr; ?> to 3 &sgr; ?> uncertainties, near-zero slopes in the diagnostic plane, largely irrespective of the maximum age or minimum mass imposed on our sample selection, or of the radial bin size adopted. We conclude that, at least in our four well-studied sample galaxies, star cluster formation does not necessarily require an environment-dependent cluster formation scenario, which thus supports the notion of stochastic star cluster formation as the dominant star cluster-formation process within a given galaxy.

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability.

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.