An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms
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ABSTRACT:
PROVIDER: PRJNA514255 | ENA |
REPOSITORIES: ENA
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Project description:We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs.Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (ClinicalTrials.gov NCT02670408).
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2018-02-09 | PXD007587 | Pride
Project description:Constructing a rat model of stress cardiomyopathy and screening for diagnostic markers using machine learning algorithms
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Project description:MOLECULAR PROFILING IMPROVES DIAGNOSES OF REJECTION AND INFECTION IN TRANSPLANTED ORGANS. The monitoring of transplanted hearts is currently based on histological evaluation of endomyocardial biopsies, a method that is fairly insensitive and that does not always accurately discriminate between rejection and infection in the heart. Accurate diagnosis of rejection and infection is absolutely crucial, however, as the respective treatments are completely different. Using microarrays we analyzed gene expression in 76 cardiac biopsies from 40 heart recipients undergoing rejection, no rejection, or T. cruzi infection. We found a set of 98 genes whose expression patterns were typical of acute rejection, and 87 genes that discriminated between rejection and T. cruzi infection. These sets revealed acute rejection episodes up to two weeks earlier, and trypanosome infection up to two months earlier than did histological evaluation. When applied to raw data from other institutions, the two sets of predictive genes were also able to accurately pinpoint acute rejection of lung and kidney transplants, as well as bacterial infections in kidneys. In addition to their usefulness as diagnostic tools, the data suggest that there are similarities in the biology of the processes involved in rejection of different grafts and also in the tissue responses to pathogens as diverse as bacteria and protozoa. pre-rejection: GSM47949, GSM48048, GSM49625 non-rejection: GSM47957, GSM48045, GSM48049, GSM48057, GSM48072, GSM48089, GSM48092, GSM49624, GSM49654, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49748, GSM49750, GSM49751, GSM49752, GSM49754, GSM49756, GSM49757, GSM49758, GSM49762, GSM49765, GSM49766, GSM49767, GSM49769, GSM49771, GSM49773, GSM49774, GSM49781, GSM49782, GSM49783, GSM49784 acute rejection: GSM48040, GSM48043, GSM48046, GSM48047, GSM48050, GSM48053, GSM48056, GSM49655, GSM49747, GSM49749, GSM49755, GSM49759, GSM49761, GSM49763, GSM49764, GSM49780 pre-Chagas disease reactivation: GSM48089, GSM49756, GSM49757, GSM49758, GSM49767, GSM49769, GSM49771 Chagas disease reactivation: GSM49753, GSM49760, GSM49768, GSM49772, GSM49775, GSM49776, GSM49777, GSM49778 Toxoplasma myocarditis: GSM48058 first subset ("summer"): GSM47949, GSM47957, GSM48040, GSM48043, GSM48045, GSM48046, GSM48047, GSM48048, GSM48049, GSM48050, GSM48053, GSM48056, GSM48057, GSM48058, GSM48072, GSM48089, GSM48092, GSM49624, GSM49625, GSM49654, GSM49655, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49747, GSM49748, GSM49749, GSM49750, GSM49751, GSM49752 second subset ("winter"): GSM49753, GSM49754, GSM49755, GSM49756, GSM49757, GSM49758, GSM49759, GSM49760, GSM49761, GSM49762, GSM49763, GSM49764, GSM49765, GSM49766, GSM49767, GSM49768, GSM49769, GSM49770, GSM49771, GSM49772, GSM49773, GSM49774, GSM49775, GSM49776, GSM49777, GSM49778, GSM49780, GSM49781, GSM49782, GSM49783, GSM49784 Keywords = cardiac transplant Keywords = acute rejection Keywords = infection Keywords: repeat sample
2005-10-24 | GSE2596 | GEO
Project description:Pattern discovery algorithms are methods for discovering recurrent, non-random motifs widely used in the analysis of biological sequences. Many algorithms exist but few comparisons have been made amongst them. We systematically profile eight representative methods at multiple parameter settings across 174 diverse experimental datasets, including ten novel ChIP-on-chip datasets. We executed 16,777 pattern discovery analyses to assess prediction accuracy, CPU usage and memory consumption. For 144 datasets we developed a gold-standard using machine-learning algorithms; cross-validation was used for the remaining datasets. Performance was highly disparate, with median accuracy ranging from 32% to 96%. Importantly we were unable to replicate previously reported algorithm-rankings, emphasizing the need to use many and diverse experimental datasets. We found deterministic algorithms like Projection and Oligo/Dyad had the highest prediction accuracy. Computational efficiency was not linearly related to dataset size and becomes critical: some algorithms are intractably slow on large datasets. This work provides the first combined assessment of the CPU, memory, and prediction accuracies of pattern discovery algorithms on real experimental datasets.
2009-11-24 | GSE15370 | GEO
Project description:Background:Epilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy. Methods:By analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model. Results: Epilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3.The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup. Conclusion: We believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway,JAK-STAT signaling pathway), and finally localized their key cellular subgroups(T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup,and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.
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Project description:Integrating diagnostic approaches in bacterial infant meningitis caused by a non-K1 Escherichia coli: a case report
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Project description:MOLECULAR PROFILING IMPROVES DIAGNOSES OF REJECTION AND INFECTION IN TRANSPLANTED ORGANS. The monitoring of transplanted hearts is currently based on histological evaluation of endomyocardial biopsies, a method that is fairly insensitive and that does not always accurately discriminate between rejection and infection in the heart. Accurate diagnosis of rejection and infection is absolutely crucial, however, as the respective treatments are completely different. Using microarrays we analyzed gene expression in 76 cardiac biopsies from 40 heart recipients undergoing rejection, no rejection, or T. cruzi infection. We found a set of 98 genes whose expression patterns were typical of acute rejection, and 87 genes that discriminated between rejection and T. cruzi infection. These sets revealed acute rejection episodes up to two weeks earlier, and trypanosome infection up to two months earlier than did histological evaluation. When applied to raw data from other institutions, the two sets of predictive genes were also able to accurately pinpoint acute rejection of lung and kidney transplants, as well as bacterial infections in kidneys. In addition to their usefulness as diagnostic tools, the data suggest that there are similarities in the biology of the processes involved in rejection of different grafts and also in the tissue responses to pathogens as diverse as bacteria and protozoa. Keywords: disease state analysis
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Project description:Pattern discovery algorithms are methods for discovering recurrent, non-random motifs widely used in the analysis of biological sequences. Many algorithms exist but few comparisons have been made amongst them. We systematically profile eight representative methods at multiple parameter settings across 174 diverse experimental datasets, including ten novel ChIP-on-chip datasets. We executed 16,777 pattern discovery analyses to assess prediction accuracy, CPU usage and memory consumption. For 144 datasets we developed a gold-standard using machine-learning algorithms; cross-validation was used for the remaining datasets. Performance was highly disparate, with median accuracy ranging from 32% to 96%. Importantly we were unable to replicate previously reported algorithm-rankings, emphasizing the need to use many and diverse experimental datasets. We found deterministic algorithms like Projection and Oligo/Dyad had the highest prediction accuracy. Computational efficiency was not linearly related to dataset size and becomes critical: some algorithms are intractably slow on large datasets. This work provides the first combined assessment of the CPU, memory, and prediction accuracies of pattern discovery algorithms on real experimental datasets. HL60-Mnt-ChIP: ChIP-Chip with 10 biological replicates HL60-Trrap-ChIP: ChIP-Chip with 13 biological replicates
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