Pitfalls include increased false positive rates (due to multiple comparisons), sample preparation artifacts, sample heterogeneity (like mixed genetic backgrounds), highly correlated samples, unaccounted for multi-level experimental designs, and poor experimental design. One notable pitfall is viewing results in Microsoft Excel.[97] Although convenient, Excel automatically converts some gene names (SEPT1, DEC1, MARCH2) into dates or floating point numbers. Choice of tools and benchmarking: There are numerous efforts that compare the results of these tools, with DESeq2 tending to moderately outperform other methods.[98][99][100][101][102][103][104] As with other methods, benchmarking consists of comparing tool outputs to each other and known gold standards.