Biology subjects

Louis, T. A.

Publications and source records attributed to Louis, T. A..

5 recordsLinked to original sources

bioRxiv2022

A Bayesian Hierarchical Model for Signal Extraction from Protein Microarrays

SO_SCPLOWUMMARYC_SCPLOWProtein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to the high technical variability of these assays and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Using within-array ranks of protein levels for analysis can mitigate the impact of between-sample variation on downstream analysis. Although ranks are sensitive to pre-processing steps, ranking methods that accommodate uncertainty provide robust and loss-function optimal ranks. Such ranking methods require Bayesian modeling that produces full posterior distributions for parameters of interest. Bayesian models that produce such outputs have been developed for other assays, for example DNA microarrays, but those modeling assumptions are not appropriate for protein microarrays. We develop and evaluate a Bayesian model to extract a full posterior distribution of normalized fluorescent signals and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microrarrays from different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

bioinformatics↗

bioRxiv2022

A Random Forest Classifier Uses Antibody Responses to Plasmodium Antigens to Reveal Candidate Biomarkers of the Intensity and Timing of Past Exposure to Plasmodium falciparum

1Important goals of malaria surveillance efforts include accurately quantifying the burden of malaria over time, which can be useful to target and evaluate interventions. The majority of malaria surveillance methods capture active or recent infections which poses several challenges to achieving malaria surveillance goals. In high transmission settings, asymptomatic infections are common and therefore accurate measurement of malaria burden often demands active surveillance; in low transmission regions where infections are rare accurate surveillance requires sampling a large subset of the population; and in any context monitoring malaria burden over time necessitates serial sampling. Antibody responses to Plasmodium falciparum parasites persist after infection and therefore measuring antibodies has the potential to overcome several of the current difficulties associated with malaria surveillance. However, identifying which antibody responses are markers of the timing and intensity of past exposure to P. falciparum is challenging, particularly among adults who tend to be re-exposed multiple times over the course of their lifetime and therefore have similarly high antibody responses to many P. falciparum antigens. A previous analysis of 479 serum samples from individuals in three regions in southern Africa with different historical levels of P. falciparum malaria transmission (high, intermediate, and low) revealed regional differences in antibody responses to P. falciparum antigens among children under 5 years of age. Using a novel bioinformatic pipeline optimized for protein microarrays that minimizes between-sample technical variation, we used antibody responses to P. falciparum and P. vivax antigens as predictors in random forest models to classify adult samples into these three regions of differing historical malaria transmission with high accuracy. Many of the antigens that were most important for classification in these models do not overlap with previously published results and are therefore novel candidate markers for the timing and intensity of past exposure to P. falciparum. Measuring antibody responses to these antigens could potentially lead to improved malaria serosurveillance that captures the timing and intensity of past exposure.

immunology↗

bioRxiv2021

A Pre-Processing Pipeline to Quantify, Visualize andReduce Technical Variation in Protein Microarray

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis. 1 Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.

bioinformatics↗

bioRxiv2021

Accurate assignment of disease liability to genetic variants using only population data

PurposeThe growing size of public variant repositories prompted us to test the accuracy of predicting pathogenicity of DNA variants using population data alone. MethodsUnder the a priori assumption that the ratio of the prevalence of variants in healthy and affected populations form two distinct distributions (pathogenic and benign), we used a Bayesian method to assign probability of a variant belonging to either distribution. ResultsThe approach, termed BayPR, accurately parsed 300 of 313 expertly curated cystic fibrosis transmembrane conductance regulator (CFTR) variants: 284 of 296 pathogenic/likely pathogenic (P/LP) variants in one distribution and 16 of 17 benign/likely benign (B/LB) variants in another. BayPR produced an area under the receiver operating curve (AUC) of 0.99 for 103 functionally-confirmed missense CFTR variants, equal to or exceeding ten commonly used algorithms (AUC range: 0.54 to 0.99). Application of BayPR to expertly curated variants in eight genes associated with seven Mendelian conditions assigned [≥]80% disease-causing probability to 1,350 of 1,374 (98.3%) P/LP variants and [≤]20% to 22 of 23 (95.7%) B/LB variants. ConclusionAgnostic to variant type or functional effect, BayPR provides probabilities of pathogenicity for DNA variants responsible for Mendelian disorders using only variant counts in affected and unaffected population samples.

genetics↗

bioRxiv2017

Bayesian estimation of MSM population size in Côte d’Ivoire

Cote dIvoire has one of the largest HIV epidemics in West Africa with around half million people living with HIV. Key populations like gay men and other men who have sex with men (MSM) are often disproportionately burdened with HIV due to specific acquisition and transmission risks. Quantifying the MSM population sizes at subnational level is critical to improving the HIV prevention interventions. While survey-based direct estimates of MSM numbers are available at a few urban centers in C{circumflex}ote dIvoire, no data on MSM population size exists at other areas without any community infrastructure to facilitate sufficient access to the MSM community. We use this limited data in a Bayesian regression setup to produce first empirically calculated estimates of the numbers of MSM in all areas of C{circumflex}ote dIvoire prioritized in the HIV response. Our hierarchical model imputes missing covariates using geospatial information and allows for proper uncertainty quantification leading to meaningful confidence bounds for the predicted MSM population size estimates. The intended impact of this process is to increase uptake and use of high quality, comprehensive epidemiologic and interventional data in program planning. These estimates will help design future surveys and support the planning of the scale and content of HIV prevention and treatment programs for MSM in C{circumflex}ote dIvoire.

epidemiology↗