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The microarray experiment and data analysis methodology have been previously described in detail (Satoh et al. [2010]).
Array design is often considered the most important process in any microarray experiment and can be the deciding factor in the success of a study.
We have provided a detailed description of the experimental design, microarray experiment and normalization and analysis of data which have been deposited into NCBI Gene Expression Omnibus (GEO): GSE75240.
Here, we performed simulations to build a simple statistical table of general guidelines adapted specifically for identifying differentially expressed genes using replications of a microarray experiment, and we present an accompanying nonparametric method, replicated studentized-deviate detection (RSD).
The details of the microarray experiment and data analysis were described previously [8].
Our assessment can be easily implemented into the design of any microarray experiment and can be performed with minimal training.
Most of the genes showed the same expression pattern in both the microarray experiment and the qRT-PCR analysis.
Subjects were consecutively selected first for the microarray experiment and then the qPCR experiment based on tissues availability.
Ostrin et al. conducted a microarray experiment and identified 188 potential TGs of Ey whose expression is independent of the function of the retinal differentiation TF atonal.
The latter gene was interrogated by three probes in the microarray experiment and similar signal intensities were observed in the three adipose tissues.
Pre-microarray validation was performed from the same samples used for the microarray experiment, and biological triplicates were examined for the injection control experiment reported in Figure S1.
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