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Novel guidelines help select optimal deconvolution method


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Novel guidelines help select optimal deconvolution method
Biomedical scientists are increasingly using deconvolution methods, those used to computationally analyze the composition of complex mixtures of cells. One of their challenges is to select one method that is appropriate for their experimental conditions among nearly 50 available.
To help with method selection, researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital have extensively evaluated 11 deconvolution methods that are based on RNA-sequencing (RNA-seq) data analysis, determining each method’s individual strengths and weaknesses in a variety of scenarios. From these analyses, the researchers derived guidelines that scientists can use to determine the deconvolution method that optimally fits their needs. The study appears in the journal Genome Biology. ....

United States , Baylor College Of Medicine , Haijing Jin , Zhandong Liu , Chao Family Foundation , Dan Duncan Neurological Research Institute , Huffington Foundation , National Institute , Cancer Prevention Research Institute Of Texas , Baylor College , Texas Children , Bioinformatics Core , Dan Duncan Neurological Research , General Medical Sciences , Cancer Prevention Research Institute , Houston Endowment , ஒன்றுபட்டது மாநிலங்களில் , பேலர் கல்லூரி ஆஃப் மருந்து , குழப்பம் குடும்பம் அடித்தளம் , டான் டங்கன் நரம்பியல் ஆராய்ச்சி நிறுவனம் , ஹஃபிங்டன் அடித்தளம் , தேசிய நிறுவனம் , புற்றுநோய் ப்ரெவெந்ஶந் ஆராய்ச்சி நிறுவனம் ஆஃப் டெக்சாஸ் , பேலர் கல்லூரி , டெக்சாஸ் குழந்தைகள் , உயிர் தகவலியல் கோர் ,