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As a key feature, multiple graphs are generated for inspecting data integrity during the data acquisition process, helping instrument operators to identify problems early.
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To ensure the proper execution of the algorithms, the ROIs generated were visually inspected for accuracy.
In addition, a normal quantile-quantile plot of residuals of each variable was generated and visually inspected to test for normality.
To exclude the possibility of spurious signals caused by poor genotype calling, we also generated and visually inspected allele intensity plots for top associated SNPs.
Each image generated was visually inspected.
The generated reads were inspected and poor quality reads/bases were removed.
The generated sequence reads were inspected and trimmed manually for quality using Sequencher 4.10.1.
Finally, once ChIP-seq data for the antibody is generated, binding profiles should be inspected for recognized patterns, such as for well-characterized modifications like H3K4me3 at transcription start sites; if binding is as previously established, this can also be taken as further corroboration that the antibody is specific and behaves as expected.
The homoplasy matrices generated in PAUP were visually inspected to locate parts of trees where there was phylogenetic conflict and to identify the haplotypes responsible for this conflict.
In order to set proper cutoff levels for the real data, we took the same approach by manually inspecting the generated results.
A systematic algorithm of generating optimal inspection planning based on CAD-directed model is proposed for LGMR to inspect free-form surfaces.
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