Contributed paper presentation, delivered for Session 41, Next Generation Sequencing, at the Eastern North American Region Spring Meeting 2024, in Baltimore, MD, USA. Abstract: Estimating relative fractions of cell composition from bulk transcriptomics data is useful for differential expression analyses and for cell composition comparisons across conditions. While many deconvolution algorithms are published each year, not enough attention is given to cell size scale factor normalizations and resources are lacking to enable these normalizations in deconvolution across tissues and conditions. Normalizations on cell size help accurately quantify cell type proportions rather than total RNA content. To address this, we use the lute R/Bioconductor package for cell size scale factor normalization. Using a multi-omics dataset including single-nucleus RNA-seq, multi-condition bulk RNA-seq, and fluorescent label imaging in human dorsolateral prefrontal cortex, cell type proportion accuracy improved in systematic simulations and integrative analyses using size normalization. lute can be used to perform normalization in other regions of human prefrontal cortex to better determine relative roles of cell type-specific expression and their subsequent dysregulation in debilitating brain disorders. We propose to speak at ENAR 2024 about this work.