Despite their potential, deconvolution algorithms face several challenges:
Data Quality: The accuracy of deconvolution depends on the quality of input data. Poor-quality data can lead to incorrect conclusions. Complexity: Tumors can have a highly complex cellular composition, making it difficult to accurately deconvolute the signals. Limited Reference Profiles: Deconvolution requires reference gene expression profiles for different cell types, which may not always be available or accurate. Computational Cost: Advanced deconvolution algorithms can be computationally intensive, requiring significant resources.
