- Analysis title
- geneXplain GmbH
- com.genexplain.analyses (geneXplain analyses)
Estimate differential expression using Linear Models for MicroArrays (LIMMA)
- Input table - Table with normalized measurement values
- Input log-base - Logarithmic base of input data
- 1. Condition / group name - Name for first condition / group
- 1. Columns - Columns assigned to first condition / group
- 2. Condition / group name - Name for second condition / group
- 2. Columns - Columns assigned to second condition / group
- 3. Condition / group name - Name for third condition / group
- 3. Columns - Columns assigned to third condition / group
- 4. Condition / group name - Name for fourth condition / group
- 4. Columns - Columns assigned to fourth condition / group
- 5. Condition / group name - Name for fifth condition / group
- 5. Columns - Columns assigned to fifth condition / group
- Output folder - Folder to store output tables
Please not that the first two groups (named Treatment and Control by default) are not optional and unnamed groups are not considered.
The output contains the columns described below. Columns highlighted in bold are shown in the default view. The other columns can be included on demand via the Columns tab of the lower right panel (available with opened output table).
- Fold change (log)
- Fold change (Lower confidence interval)
- Fold change (Upper confidence interval)
- Average log2-expression for the probe over all arrays
- Moderated T-statistic
- P-value Differential expression
- Adjusted P-value (Benjamini-Hochberg)
- Log-odds that the gene / probe shows differential expression
Limma estimates differential expression between specified conditions / groups.
This tool provides an interface for the popular and comprehensive Limma package. The platform tool computes differential expression between up to five conditions / groups. The groups consist of columns of a data table that contains normalized measurement values, e.g. from a normalized microarry experiment. All possible contrasts between groups are considered and their output is stored in a common folder. Conditions are compared in the specified order from first to fifth. E.g. given conditions named A, B and C, the output will contain the contrasts AvsB, AvsC and BvsC.
It is necessary to provide a unique name for each group. Also, at least two data columns are required per group.
Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, 2005.