Report of the analysis

I think you are using a dot (.) as a decimal symbol. If that is correct, everything is ok.

  • Significant genes
  • Goodness of fit
  • Estimation of the Q-Values
  • P-Values and adjusted P-Values "The statistics"

    Significant genes

    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.

  • gene0031
  • gene0045
  • gene0096
  • gene0127
  • gene0128
  • gene0812
  • gene0996
  • gene1144
  • gene1831
  • gene1873
  • gene1875

    Goodness of fit

    It is important to remember that our confidence in statistical inference procedures is related to the validity of the assumptions about them. A mechanically made inference may be misleading if some model assumption is grossly violated. An examination of the distribution of the data is important because it helps to detect any inconsistency between the data and the postulated model. If no abnormalities are exposed in this process, then we can consider the model adequate and proceed with the relevant inferences.

    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.

    Click to download the histogram as a postscript file

    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.

    Click to download the QQ-Plot as a postscript file

    Estimation of the Q-Values

    These are the Q-Value plots.
    Click to download the Q-Value plots as a postscript file

  • 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-Values and adjusted P-Values

    To import the data back to Excel, click into the textarea. Press [Ctrl+A] to mark everything. Press [Ctrl+C] to copy the data to your clipboard. Switch to Excel. Click on the position in your worksheet where the data should be inserted. Then press [Ctrl+V].

    Your data:

    The statistics:

    What the columns in the statistics mean:

    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).