Single-cell RNA sequencing (scRNA-seq) enables high-resolution exploration of cellular diversity and gene regulation, yet analyzing such data remains challenging due to technical and methodological limitations. Existing task-specific deep generative models like Variational Auto-Encoder (VAE) and its variants struggle to incorporate external biological knowledge, while transformer-based foundational large Language Models (LLMs or large LaMs) face limitations in computational cost and applicability to tabular gene expression data. Here, we introduce sciLaMA (single-cell interpretable Language Model Adapter), a novel representation learning framework that bridges these gaps by integrating static gene embeddings from multimodal LaMs with scRNA-seq tabular data through a paired-VAE architecture. Our approach generates context-aware representations for both cells and genes and outperforms state-of-the-art methods in key single-cell downstream tasks, including batch effect correction, cell clustering, and cell-state-specific gene marker and module identification, while maintaining computational efficiency. sciLaMA offers a computationally efficient, unified framework for comprehensive single-cell data analysis and biologically interpretable gene module discovery.

BACKGROUND: Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. RESULTS: To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. CONCLUSIONS: The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects. Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry, even after batch correction was applied. We introduce PERT, a new probabilistic expression deconvolution method that detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution. We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions (uncultured mono-nucleated and lineage-depleted cells, and culture-derived lineage-depleted cells). Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry. Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions. We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity.

MOTIVATION: Lung cancer is often discovered long after its onset, making identifying genes important in its initiation and progression a challenge. By the time the tumors are discovered, we only observe the final sum of changes of the few genes that initiated cancer and thousands of genes that they have influenced. Gene interactions and heterogeneity of samples make it difficult to identify genes consistent between different cohorts. Using gene and gene-product interaction networks, we propose a principled approach to identify a small subset of genes whose network neighbors exhibit consistently high expression change (in cancerous tissue versus normal) regardless of their own expression. We hypothesize that these genes can shed light on the larger scale perturbations in the overall landscape of expression levels. RESULTS: We benchmark our method on simulated data, and show that we can recover a true gene list in noisy measurement data. We then apply our method to four non-small cell lung cancer and two pancreatic cancer cohorts, finding several genes that are consistent within all cohorts of the same cancer type. CONCLUSION: Our model is flexible, robust and identifies gene sets that are more consistent across cohorts than several other approaches. Additionally, our method can be applied on a per-patient basis not requiring large cohorts of patients to find genes of influence. Our approach is generally applicable to gene expression studies where the goal is to identify a small set of influential genes that may in turn explain the much larger set of genome-wide expression changes.

Background Cancer gene expression profiling is an indispensable tool for identifying drivers of tumor progression, identifying subtypes, and predicting clinical outcome. An outstanding challenge faced by cancer gene expression studies is the limited concordance between studies [1], driven in part by lack of statistical power [2]. Part of this lack of statistical power is due to the fact that tumor samples from some solid cancers contain between 30%-70% healthy tissue [3]. This healthy tissue contaminates tumor expression profiles and variable amounts of healthy tissue leads to increased variability between tumor expression profiles. Physical purification of these tumor samples before profiling is often not feasible.

MOTIVATION: One of the most deadly cancer diagnoses is the carcinoma of unknown primary origin. Without the knowledge of the site of origin, treatment regimens are limited in their specificity and result in high mortality rates. Though supervised classification methods have been developed to predict the site of origin based on gene expression data, they require large numbers of previously classified tumors for training, in part because they do not account for sample heterogeneity, which limits their application to well-studied cancers. RESULTS: We present ISOLATE, a new statistical method that simultaneously predicts the primary site of origin of cancers and addresses sample heterogeneity, while taking advantage of new high-throughput sequencing technology that promises to bring higher accuracy and reproducibility to gene expression profiling experiments. ISOLATE makes predictions de novo, without having seen any training expression profiles of cancers with identified origin. Compared with previous methods, ISOLATE is able to predict the primary site of origin, de-convolve and remove the effect of sample heterogeneity and identify differentially expressed genes with higher accuracy, across both synthetic and clinical datasets. Methods such as ISOLATE are invaluable tools for clinicians faced with carcinomas of unknown primary origin. AVAILABILITY: ISOLATE is available for download at: http://morrislab.med.utoronto.ca/software CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest in species such as human and mouse. We present a statistical model that makes a number of significant extensions to previous models to enable characterization of changes in expression among highly complex organisms. We demonstrate the efficacy of our method on a microarray dataset containing diverse tissues from multiple vertebrate species. We anticipate that the model will be invaluable in the study of gene expression patterns in other diverse organisms as well, such as worms and insects.

BACKGROUND: We present Pegasys--a flexible, modular and customizable software system that facilitates the execution and data integration from heterogeneous biological sequence analysis tools. RESULTS: The Pegasys system includes numerous tools for pair-wise and multiple sequence alignment, ab initio gene prediction, RNA gene detection, masking repetitive sequences in genomic DNA as well as filters for database formatting and processing raw output from various analysis tools. We introduce a novel data structure for creating workflows of sequence analyses and a unified data model to store its results. The software allows users to dynamically create analysis workflows at run-time by manipulating a graphical user interface. All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation. The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools, or GAME XML for import into the Apollo genome editor. The modularity of the design allows for new tools to be added to the system with little programmer overhead. The database application programming interface allows programmatic access to the data stored in the backend through SQL queries. CONCLUSIONS: The Pegasys system enables biologists and bioinformaticians to create and manage sequence analysis workflows. The software is released under the Open Source GNU General Public License. All source code and documentation is available for download at http://bioinformatics.ubc.ca/pegasys/.