We are a computational group (“dry lab”). We develop methods to study human immune response as a biological system by integrating transcriptome, cellular, and epigenome. We perform systems biology analysis within and across diseases using data from patient populations across the world that represent the real-world biological and technical heterogeneity.
A typical biological experiment is a controlled experiment that explicitly limits biological and technical heterogeneity by using the samples from the same tissue and from patients that are treated similarly, and profiled using the same technology. These controlled experiments have improved our understanding of biology. However, a controlled experiment does not represent the real-world heterogeneity. Therefore, its results are nearly impossible to translate into clinical practice immediately.
The Khatri Lab has developed a novel framework for integrating relatively “Small Data” – datasets with a few tens of samples – into “Big Data” – a few hundreds to thousands of samples – in a consistent manner that is representative of the biological and technical heterogeneity observed in the real-world patient population. We have shown that compared to single-cohort analysis, following general guidelines of our framework, implemented in an R package MetaIntegrator, significantly improves reproducibility by integrating data from multiple independent cohorts, even when controlling for sample size. We have repeatedly demonstrated that utility of our framework in a broad spectrum of diseases including organ transplant rejection, infectious diseases (sepsis, bacterial infections, viral infections, tuberculosis, dengue), autoimmune diseases (systemic sclerosis, IBD), cancers (lung cancer, KRAS-associated cancers), pan-organ fibrosis, and vaccination for identifying signatures that are diagnostic, prognostic, therapeutic, and mechanistic.
Using heterogeneous data can also improve new methods development. We have created immunoStates, a basis matrix, for estimating percentages of 20 immune cell types using transcriptome data. We have demonstrated that immunoStates is not affected by biological and technological differences in blood transcriptome data, irrespective of the method used. We have shown that immunoStates can be used to perform in silico cellular phenotyping across multiple independent cohorts to analyze changes in immune profiles that identify hitherto unknown biology and biomarkers in tubercluosis, pan-organ fibrosis, and influenza infections.
In collaboration with the Utz lab, we have developed a technology for measuring epigenetic profiles at a single-cell level, called Epigenetic profiling using Cytometry Time of Flight (EpiTOF). EpiTOF allows measuring 8 classes of histone modifications and 4 histone variants along with various lineage markers. Using EpiTOF we have demonstrated profound effect of aging on epigenome while accounting for genetics. Download our data from the Cell paper here.
Biomedical research is facing a ‘reproducibility crisis.’ Contributing to this problem is the fact that most studies are conducted on small, homogeneous samples which do not reflect the natural heterogeneity of a given disease. To address this, the Khatri lab has developed the multi-cohort analysis platform, called MetaIntegrator, and used it to analyze over 50,000 human samples with approximately 1.5 billion data points from 103 diseases. We’ve made these results available to the public at metasignature.stanford.edu, with the goal of promoting data-driven hypothesis generation.
The Khatri lab is also interested in studying the systemic challenges to reproducible research. For example, we have carried out analyses highlighting how gene annotation can impede biomedical research and how interpretation of the same experiment changes over time. The most differentially expressed genes (determined by our comprehensive analysis above) are often poorly annotated and studied.
We integrate large datasets that span every level of molecular characterization, from epigenetic data to patient records. We integrate “science of medicine” (molecular data) with “practice of medicine” (electronic health records (EHR) and claims data) to draw connections and conclusions about factors that impact patient health. For example, we used cellular deconvolution of publicly-available transcriptome data to predict increased monocytes as a prognostic marker of poor outcomes in patients with a fibrotic disease. We then used EHRs from different hospitals and claims data to validate our prediction.
We believe computational researchers have an integral role to play in translating basic science research into clinical practice. We collaborate with clinicians to prospectively validate our computational discoveries in independent cohorts, and with basic science researchers to understand mechanisms underlying results of our analysis.
We are applying techniques from deep learning to enhance drug discovery and learn about the relationships between diseases by integrating multiple types of data (from EHRs to high-throughput sequencing).
“P-hacking” and animal models that are not representative of human biology have been associated with “reproducibility crisis” in biomedical science. However, we believe there is another very important factor contributing to this “reproducibility crisis” – data used for biomedical discovery are not representative of the real-world patient population. Specifically, these data lack biological and technical heterogeneity observed in the real-world patient population. In the Khatri lab, we focus on developing methods and performing analyses such that both biological and technical heterogeneity is represented in the data in order to identify robust biological signal.
Our ongoing research is also highlighting another aspect of reproducibility – the street lamp effect. We as researchers tend to focus on ideas (drugs, genes, diseases, etc) that are already well-studied. This leaves many potential breakthroughs in the dark. The Khatri lab uses data-driven approach to shed light on them. We have shown that there is substantial ‘research bias‘ in literature that results in different interpretation of the same biological experiment over time.