The paper introduces Census, an algorithm that converts relative RNA-Seq expression levels into relative transcript counts without the need for experimental spike-in controls. This approach significantly improves the accuracy of differential gene expression analysis compared to normalized read counts, enabling new statistical tests for identifying developmentally regulated genes. The authors demonstrate the power of Census through reanalysis of single-cell studies in various developmental and disease contexts. Census counts can be analyzed with widely used regression techniques to reveal changes in cell fate-dependent gene expression, splicing patterns, and allelic imbalances, demonstrating its robustness in single-cell analysis at multiple layers of gene regulation. The paper also introduces BEAM, a regression model for detecting genes that change following fate decisions in development, and shows that Census enables more accurate and reliable single-cell differential expression analyses.The paper introduces Census, an algorithm that converts relative RNA-Seq expression levels into relative transcript counts without the need for experimental spike-in controls. This approach significantly improves the accuracy of differential gene expression analysis compared to normalized read counts, enabling new statistical tests for identifying developmentally regulated genes. The authors demonstrate the power of Census through reanalysis of single-cell studies in various developmental and disease contexts. Census counts can be analyzed with widely used regression techniques to reveal changes in cell fate-dependent gene expression, splicing patterns, and allelic imbalances, demonstrating its robustness in single-cell analysis at multiple layers of gene regulation. The paper also introduces BEAM, a regression model for detecting genes that change following fate decisions in development, and shows that Census enables more accurate and reliable single-cell differential expression analyses.