ORCID Profile
0000-0002-9510-8326
Current Organisation
Walter and Eliza Hall Institute of Medical Research
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Publisher: Springer Science and Business Media LLC
Date: 02-10-2023
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 02-2017
Publisher: Cold Spring Harbor Laboratory
Date: 04-04-2023
DOI: 10.1101/2023.04.02.535231
Abstract: A major challenge in the analysis of RNA-seq data at the transcript-level is accounting for the variability introduced during quantification of RNA sequencing reads. This variability is due to the high levels of sequence similarity among transcripts annotated to the same genomic locus and the mapping ambiguity resulting from the assignment of sequence reads to such transcripts. The quantification uncertainty associated with transcript-level estimated counts is intractable to measure analytically but represents an extra source of variation that seriously compromises differential transcript expression (DTE) analyses if standard statistical methods developed for gene-level analyses are used. Bootstrap counts, as provided by popular RNA-seq quantification tools, allow one to estimate the quantification uncertainty and account for such an effect in DTE analyses. We present catchSalmon and catchKallisto , two functions included in the R/Bioconductor package edgeR , that estimate the transcript-level quantification uncertainty, here termed mapping ambiguity overdispersion, using bootstrap counts. We discuss how the mapping ambiguity overdispersion can be effectively removed from the data in transcript-level analyses via count scaling, an approach that reduces the size of the estimated counts obtained from quantification tools to effective count sizes that reflect their true precision. The presented count scaling approach allows users to perform efficient DTE analyses within the efficient edgeR framework. A comprehensive simulation study and a DTE analysis of human lung adenocarcinoma cell lines are presented to illustrate the benefits of accounting for the mapping ambiguity overdispersion in transcript-level RNA-seq data analyses.
Publisher: Springer Science and Business Media LLC
Date: 02-08-2016
DOI: 10.1038/SREP30749
Abstract: Despite the extraordinary success of HIV-1 antiretroviral therapy in prolonging life, infected in iduals face lifelong therapy because of a reservoir of latently-infected cells that harbor replication competent virus. Recently, compounds have been identified that can reverse HIV-1 latency in vivo . These latency- reversing agents (LRAs) could make latently-infected cells vulnerable to clearance by immune cells, including cytolytic CD8+ T cells. We investigated the effects of two leading LRA classes on CD8+ T cell phenotype and function: the histone deacetylase inhibitors (HDACis) and protein kinase C modulators (PKCms). We observed that relative to HDACis, the PKCms induced much stronger T cell activation coupled with non-specific cytokine production and T cell proliferation. When examining antigen-specific CD8+ T cell function, all the LRAs except the HDACi Vorinostat reduced, but did not abolish, one or more measurements of CD8+ T cell function. Importantly, the extent and timing of these effects differed between LRAs. Panobinostat had detrimental effects within 10 hours of drug treatment, whereas the effects of the other LRAs were observed between 48 hours and 5 days. These observations suggest that scheduling of LRA and CD8+ T cell immunotherapy regimens may be critical for optimal clearance of the HIV-1 reservoir.
Publisher: Cold Spring Harbor Laboratory
Date: 22-07-2022
DOI: 10.1101/2022.07.22.501076
Abstract: The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (“sequins”). S les were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture s les to allow performance assessment in the absence of true positives or true negatives. Our results show that, StringTie2 and bambu outperformed other tools from the 6 isoform detection tools tested, DESeq2, edgeR and limma-voom were best amongst the 5 differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the 5 tools compared, which suggests further methods development is needed for this application.
Publisher: Cold Spring Harbor Laboratory
Date: 03-12-2019
DOI: 10.1101/864124
Abstract: Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively-parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the ersity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, mixNBHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.
Publisher: Informa UK Limited
Date: 04-03-2020
Publisher: Oxford University Press (OUP)
Date: 28-01-2021
DOI: 10.1093/AJE/KWAB008
Abstract: Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex s ling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based s ling design, a parametric and a res ling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subs le of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 24-09-2021
DOI: 10.1126/SCIIMMUNOL.ABF7268
Abstract: PRC2 is essential for the self-renewal of tissue-resident macrophages but dispensable for dendritic cell development and function.
Publisher: Wiley
Date: 07-03-2023
DOI: 10.1002/SIM.9692
Abstract: Design‐based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey s ling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non‐response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design‐based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design‐based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing‐completely‐at‐random or missing‐at‐random missing mechanism and when the missingness model is correctly specified or over‐specified. We also illustrate the use of these methods using data from HCHS/SOL.
Publisher: Oxford University Press (OUP)
Date: 24-09-2020
DOI: 10.1093/AJH/HPAA152
Abstract: Hypertension has been implicated as a smoking-related risk factor for cardiovascular disease but the dose–response relationship is incompletely described. Hispanics, who often have relatively light smoking exposures, have been understudied in this regard. We used data from a 6-year follow-up study of US Hispanic adults aged 18–76 to address the dose–response linking cigarette use with incident hypertension, which was defined by measured blood pressure above 140/90 mm Hg or initiation of antihypertensive medications. Adjustment was performed for potential confounders and mediators, including urinary albumin-to-creatinine ratio which worsened over time among smokers. Current smoking was associated with incident hypertension, with a threshold effect above 5 cumulative pack-years of smoking (vs. never smokers, hazard ratio for hypertension [95% confidence interval] of 0.95 [0.67, 1.35] for 0–5 pack-years, 1.47 [1.05, 2.06] for 5–10 pack-years, 1.40 [1.00, 1.96] for 10–20 pack-years, and 1.34 [1.09, 1.66] for ≥20 pack-years, P = 0.037). In contrast to current smokers, former smokers did not appear to have increased risk of hypertension, even at the highest cumulative pack-years of past exposure. The results confirm that smoking constitutes a hypertension risk factor in Hispanic adults. A relatively modest cumulative dose of smoking, above 5 pack-years of exposure, raises risk of hypertension by over 30%. The increased hypertension risk was confined to current smokers, and did not increase further with higher pack-year levels. The lack of a smoking–hypertension association in former smokers underscores the value of smoking cessation.
Publisher: Wiley
Date: 03-05-2021
DOI: 10.1111/BIOM.13477
Abstract: Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP‐seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the ersity in ChIP‐seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 16-06-2020
Abstract: Among US Hispanics/Latinos, the largest ethnic minority population in the United States, hypertension incidence has not been thoroughly reported. The goal of this study was to describe the incidence of hypertension among US Hispanic/Latino men and women of erse Hispanic/Latino background. We studied 6171 participants of the Hispanic Community Health Study/Study of Latinos, a erse group of self‐identified Hispanics/Latinos from 4 US urban communities, aged 18 to 74 years, and free from hypertension in 2008 to 2011 and re‐examined in 2014 to 2017. Hypertension was defined as self‐reported use of anti‐hypertension medication, or measured systolic blood pressure ≥130 mm Hg, or diastolic blood pressure ≥80 mm Hg. Results were weighted given the complex survey design to reflect the target population. Among men, the 6‐year age‐adjusted probability of developing hypertension was 21.7% (95% CI , 19.5–24.1) and differed by Hispanic/Latino background. Specifically, the probability was significantly higher among men of Cuban (27.1% 95% CI , 20.2–35.2) and Dominican (28.1% 95% CI , 19.5–38.8) backgrounds compared with Mexican Americans (17.6% 95% CI : 14.5–21.2). Among women, the 6‐year age‐adjusted probability of developing hypertension was 19.7% (95% CI , 18.1–21.5) and also differed by Hispanic/Latino background. Specifically, the probability was significantly higher among women of Cuban (22.6% 95% CI , 18.3–27.5), Dominican (23.3% 95% CI , 18.0–29.5), and Puerto Rican (28.2% 95% CI , 22.7–34.4) backgrounds compared with Mexican Americans (16.0% 95% CI , 13.9–18.4). Hypertension incidence varies by Hispanic/Latino background, with highest incidence among those of Caribbean background.
Publisher: Wiley
Date: 17-10-2019
DOI: 10.1111/BIOM.13083
Publisher: Elsevier BV
Date: 11-2017
Location: United States of America
Location: Australia
No related grants have been discovered for Pedro L. Baldoni.