The following genes have had a p-value of less than 0.01 (1-confidence level you chose) and a fold change of at least 2. These are probably the genes you are looking for. However,don't stop here, read the rest of the report, import the statistics to Excel and examine the result there more closely.
This is a histogram of the logarithms of your data. It should look like a normal distribution. If this is the case the test will be reliable. If it does not look like a normal distribution you should be sceptical. The red curve is an estimation of the normal distribution that fits your data best.
This is a QQ-Plot of your data. If your data is perfectly normally distributed it will lie on the red line. Genes that are not equally expressed will not follow the same distribution than the rest of the genes. Therefore, some outliers at both ends of the QQ-Plot are to be expected.
|FDR = 0.05(The FDR is the false discovery rate you will have if you consider all genes with a p-value less than 1-0.99 (the confidence you required) significant.|
|Pi0 = 0.62 Pi0 is the an estimate of the percentage of genes where the null hypothesis is true. That is, the percentage of genes that are considered expressed the same.|
|P_VALUE||P value - The least probability of error for which this gene can still be called significant. For details see: Wie der P-Wert in Microarrayexperimenten berechnet wird|
|SI||Indicates whether this gene is significant based on the confidence entered by the user. If the lower bound of the confidence interval is greater than 1, the value is "M"(ore) if the upper bound of the confidence interval is less than 1, the value is "L"(ower). If the confidence interval covers 1, the field is left empty.|
|AVG||The average of the values for the gene|
|AVG_est||The ratios of the intensities follow a log-normal distribution. This is an estimate of the maximum of the distribution. It is 2^mean(log (ratios)) or the geometric mean of the ratios.|
|I_L||The lower bound of the confidence interval based on the desired confidence entered by the user|
|I_R||The upper bound of the confidence interval based on the desired confidence entered by the user. The true ratio will be in the interval [I_L..I_R] with the confidence you specified (0.99)|
|Q_VALUE||The q-value for a particular feature is the minimum false discovery rate that can be attained when calling all features up through that one on the list significant.|
|rawp||The p-value again|
|Bonferroni||Bonferroni single-step adjusted p-values for strong control of the FWER.|
|Holm||Holm (1979) step-down adjusted p-values for strong control of the FWER.|
|Hochberg||Hochberg (1988) step-up adjusted p-values for strong control of the FWER (for raw (unadjusted) p-values satisfying the Simes inequality).|
|SidakSS||Sidak single-step adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).|
|SidakSD||Sidak step-down adjusted p-values for strong control of the FWER (for positive orthant dependent test statistics).|
|BH||adjusted p-values for the Benjamini & Hochberg (1995) step-up FDR controlling procedure (independent and positive regression dependent test statistics).|
|BY||adjusted p-values for the Benjamini & Yekutieli (2001) step-up FDR controlling procedure (general dependency structures).|