Estimating cell type-specific differential expression using deconvolution

Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer

BACKGROUND

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

METHODS

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

RESULTS

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

CONCLUSIONS

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

A systematic evaluation of cell-type-specific differential methylation analysis in bulk tissue

We conducted a systematic assessment of computational models-CellDMC, TCA, HIRE, TOAST, and CeDAR-for detecting cell-type-specific differential methylation CpGs in bulk methylation data profiled using the Illumina DNA Methylation BeadArrays. This assessment was performed through simulations and case studies involving two epigenome-wide association studies (EWAS) on rheumatoid arthritis and major depressive disorder. Our evaluation provided insights into the strengths and limitations of each model. The results revealed that the models varied in performance across different metrics, sample sizes, and computational efficiency. Additionally, we proposed integrating the results from these models using the minimum p-value ($minpv$) and average p-value ($avepv$) approaches. Our findings demonstrated that these aggregation methods significantly improved performance in identifying cell-type-specific differential methylation CpGs.

Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data

There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. In this article, we introduce a flexible statistical deconvolution framework that allows a general and subject-specific covariance of bulk gene expressions. Under this framework, we propose a decorrelated constrained least squares method called DECALS that estimates cell-type proportions as well as the sampling distribution of the estimates. Simulation studies demonstrate that DECALS can accurately quantify the uncertainties in the estimated proportions whereas other methods fail. Applying DECALS to analyze bulk gene expression data of post mortem brain samples from the ROSMAP and GTEx projects, we show that taking into account the uncertainties in the estimated cell-type proportions can lead to more accurate identifications of cell-type-specific differentially expressed genes and transcripts between different subject groups, such as between Alzheimer's disease patients and controls and between males and females.

Deciphering the shift from benign to active relapsing-remitting multiple sclerosis: Insights into T regulatory cell dysfunction and apoptosis regulation

BACKGROUND

Relapsing-remitting multiple sclerosis (RRMS), a common demyelinating disease among young adults, follows a benign course in 10-15% of cases, where patients experience minimal neurological disability for a decade following disease onset. However, there is potential for these benign cases to transition into a clinically active, relapsing state.

OBJECTIVE

To elucidate the biological mechanisms underlying the transition from benign to active RRMS using gene expression analysis.

METHODS

We employed complementary-DNA microarrays to examine peripheral-blood gene expression patterns in patients with benign MS, defined as having a disease duration exceeding 10 years and an Expanded Disability Status Scale (EDSS) score of ≤3.0. We compared the gene expression pattern between patients who switched to active disease (Switching BMS) with those who maintained a benign state (Permanent-BMS) during an additional 5-year follow-up.

RESULTS

We identified two primary mechanisms linked to the transition from benign MS to clinically active disease. The first involves the suppression of regulatory T cell activity, and the second pertains to the dysfunction of nuclear receptor 4 A family-dependent apoptosis. These mechanisms collectively contribute to an augmented autoimmune response and increased disease activity.

CONCLUSIONS

The intricate gene regulatory networks that operate in switching-BMS are related to suppression of immune tolerance and aberrant apoptosis. These findings may lead to new therapeutic targets to prevent the escalation to active disease.

A comprehensive assessment of cell type-specific differential expression methods in bulk data

Accounting for cell type compositions has been very successful at analyzing high-throughput data from heterogeneous tissues. Differential gene expression analysis at cell type level is becoming increasingly popular, yielding biomarker discovery in a finer granularity within a particular cell type. Although several computational methods have been developed to identify cell type-specific differentially expressed genes (csDEG) from RNA-seq data, a systematic evaluation is yet to be performed. Here, we thoroughly benchmark six recently published methods: CellDMC, CARseq, TOAST, LRCDE, CeDAR and TCA, together with two classical methods, csSAM and DESeq2, for a comprehensive comparison. We aim to systematically evaluate the performance of popular csDEG detection methods and provide guidance to researchers. In simulation studies, we benchmark available methods under various scenarios of baseline expression levels, sample sizes, cell type compositions, expression level alterations, technical noises and biological dispersions. Real data analyses of three large datasets on inflammatory bowel disease, lung cancer and autism provide evaluation in both the gene level and the pathway level. We find that csDEG calling is strongly affected by effect size, baseline expression level and cell type compositions. Results imply that csDEG discovery is a challenging task itself, with room to improvements on handling low signal-to-noise ratio and low expression genes.