Abstract
In gene expression analysis, sample differences and experimental operation differences are common, but sometimes, these differences will cause serious errors to the results or even make the results meaningless. Finding suitable internal reference genes efficiently to eliminate errors is a challenge. Aside from the need for high efficiency, there is no package for screening endogenous genes available in Python. Here, we introduce ERgene, a Python library for screening endogenous reference genes. It has extremely high computational efficiency and simple operation steps. The principle is based on the inverse process of the internal reference method, and the robust matrix block operation makes the selection of internal reference genes faster than any other method.
Highlights
In gene expression analysis, sample differences and experimental operation differences are common, but sometimes, these differences will cause serious errors to the results or even make the results meaningless
Gene expression analysis has become increasingly important in many areas of biological research
In order to solve these problems, a new approach is proposed by analyzing the principle of the internal reference method
Summary
Sample differences and experimental operation differences are common, but sometimes, these differences will cause serious errors to the results or even make the results meaningless. Aside from the need for high efficiency, there is no package for screening endogenous genes available in Python. We introduce ERgene, a Python library for screening endogenous reference genes. It has extremely high computational efficiency and simple operation steps. The commonly used measurement methods include m icroarray, RT-PCR2 and massively parallel sequencing. The commonly used measurement methods include m icroarray, RT-PCR2 and massively parallel sequencing3 These measurements require normalization to reduce the differences between samples. There are currently no available package for screening endogenous reference genes in Python. Using the computational power of the Pandas library in Python, we build a Python library to meet the requirements of normalization and internal reference gene screening
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