Constructing a rat model of stress cardiomyopathy and screening for diagnostic markers using machine learning algorithms
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA925909 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23166270_1.fastq.gz | Fastqsanger.gz | |||
| SRR23166270_2.fastq.gz | Fastqsanger.gz | |||
| SRR23166271_1.fastq.gz | Fastqsanger.gz | |||
| SRR23166271_2.fastq.gz | Fastqsanger.gz | |||
| SRR23166272_1.fastq.gz | Fastqsanger.gz |
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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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Project description:BackgroundThe mechanisms of the occurrence and progression of dilated cardiomyopathy are still unclear and further exploration is needed. The upgrading of programming languages and the improvement of biological databases have created conditions for us to explore the structural and functional information of biological molecules at the nucleic acid and protein levels, screen key pathogenic genes, and elucidate pathogenic mechanisms. This study aimed to screen key pathogenic genes using machine learning algorithms and explore the correlation between key genes and immune microenvironment through transcriptome sequencing data sets of myocardial samples from patients with dilated cardiomyopathy, providing new ideas for elucidating the pathogenesis of the disease.MethodsThe transcriptome sequencing data sets of heart tissue from patients with dilated cardiomyopathy were downloaded from the Gene Expression Omnibus (GEO) database (GSE29819 and GSE21610). Differentially expressed genes (DEGs) were screened between pathological and normal tissues. The key genes were screened using least absolute shrinkage and selection operator (LASSO) regression analysis and random forest tree algorithms. The diagnostic efficiency of the key genes for the disease was evaluated using the receiver operating characteristic (ROC) curve.ResultsCompared with the normal heart tissue (control group) samples, there were 213 DEGs in the heart tissue samples of patients with dilated cardiomyopathy (treat group), including 101 upregulated and 102 downregulated genes. CCL5 and CTGF were highly expressed in the treat group compared to the control group. The ROC curve showed that the areas under the curve (AUCs) of CCL5 and CTGF were 0.821 and 0.902, respectively (P<0.05). In the treat group samples, CCL5 was positively correlated with the infiltration content of most immune cell subtypes.ConclusionsCCL5 and CTGF are key disease-causing genes in dilated cardiomyopathy and have good diagnostic efficiency for the disease. CCL5 and CTGF may be related to immune cell enrichment and myocardial fibrosis, respectively.
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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.
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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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Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.
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