CDSeq

CDSeq performs complete deconvolution of bulk RNA-Seq data to estimate cell-type proportions and cell-type-specific gene expression profiles for analysis of tissue heterogeneity.

Key Features:

• Complete Deconvolution Capability: Simultaneously estimates cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq data without requiring external reference profiles.
• Efficiency and Performance: Benchmarked against seven established deconvolution methods using synthetic and real experimental datasets with known cell-type compositions and expression profiles, showing superior accuracy in disentangling cell-type contributions.
• Versatility Across Applications: Applicable to complex tissue samples including tumor microenvironments, enabling analysis of how different cell populations contribute to physiological states and disease.
• Implementation and Data-Dilution Option: Implemented for MATLAB and Octave and includes a data-dilution option to expedite algorithmic computations.

Scientific Applications:

• Tissue Heterogeneity Analysis: Deconvolving bulk RNA-Seq from heterogeneous tissues to characterize cell-type composition and expression profiles.
• Tumor Biology / Oncology: Analyzing tumor microenvironments to study cellular heterogeneity relevant to disease progression and treatment response.
• Cell-Type Contributions to Physiology and Disease: Investigating how cell-type-specific expression contributes to normal physiology and disease states, informing studies in personalized medicine.

Methodology:

Simultaneous estimation of cell-type proportions and expression profiles from bulk RNA-Seq data; implementation in MATLAB and Octave; inclusion of a data-dilution option; benchmarking against seven established deconvolution methods using synthetic and real datasets with known cell-type compositions and expression profiles.