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Abstract:
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Single-cell RNA-sequencing (scRNA-seq) provides a means to assess transcriptomic variations among individual cells rather than over a whole sample, giving an advantage over bulk sequencing methods that fail to detect subgroups and rare cell types. However, restrictions such as amplification bias, technical noise, and dropout events often limit the power of scRNA-seq results. Normalization methods correct observed gene counts to account for these restrictions and more accurately represent the true biological signal of interest. Eliminating technical noise and amplification error often involves the use of a set of "spike-in genes" injected into the cell in known quantities. By statistically modeling the difference between observed gene counts and known gene counts, the resulting model can then apply to all other genes present in the cell, adjusting observed gene counts accordingly. We propose a novel scRNA-seq normalization method that normalizes both within and between a data set's groups while also using dropout imputation to adjust for missing values. We compare this method with existing spike-in approaches, using real data sets to support our results.
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