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Pneumococcal pneumoniaPneumococcal pneumoniaPneumococcal pneumonia is a serious lung infection caused by the bacterium Streptococcus pneumoniae, also known as the pneumococcus. It is the most common form of bacterial pneumonia in adults and the leading cause of community-acquired pneumonia worldwide. This infection can lead to severe respiratory illness, especially in vulnerable populations. Symptoms of P. pneumonia often appear suddenly and may include chest pain, a cough that produces rusty or blood-streaked sputum, fever, chills, rapid or difficult breathing, and shortness of breath. In older adults, the presentation may differ, with confusion or low alertness being more prominent than the typical respiratory symptoms. Transmission occurs through respiratory droplets when an infected person coughs, sneezes, or talks. While small children often carry S. pneumoniae in their nose and throat without becoming ill, certain groups, especially elderly individuals with comorbidities, are at heightened risk for developing pneumococcal pneumonia. Diagnosis of P. pneumonia typically involves a physical examination, chest X-rays, and laboratory testing of blood or sputum samples to confirm the presence of S. pneumoniae. The drug of choice to treat P. pneumonia is benzylpenicillin or penicillin G. Early identification and treatment are crucial for improving outcomes, as untreated pneumococcal pneumonia can progress rapidly and lead to serious complications and death. Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses. The analyses are reported for three comparisons: 1) disease vs. all other diseases, 2) disease vs. diseases from the same class, and 3) disease vs. healthy samples. Disease vs All other
Disease vs Class
Disease vs Healthy
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
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The Project
The Human Protein Atlas
The Human Protein Atlas project is funded
