limma analysis | Standard limma analysis steps | Apply topconfects | Looking at the result | edgeR analysis | Standard edgeR analysis | DESeq2 analysis | Comparing results

If you want to find top confident differentially expressed genes | If you have a collection of effect sizes with standard errors | If you can calculate p-values for a collection of interval hypotheses | Visualizing results

Read files, extract experimental design from sample names | Create weitrix object | Calibration | Testing | Top confident effects | Testing with limma | Testing multiple contrasts | Examine individual genes | Exploratory analysis: overdispersed genes | Exploratory analysis: components of variation | Gene loadings for C1: gradual lengthing over time | Gene loadings for C2: cell-cycle associated changes | Gene loadings for C3: longer tails in set1 mutant | Discussion

Shift score definition | Load files | Exploratory analysis | Components of variation | Calibration | Using the calibrated weitrix with weitrix_confects | Gene loadings for C1 | Gene loadings for C2 | Gene loadings for C3 | Gene loadings for C4 | Genes with high variability | Examine individual genes | Alternative calibration method

Load the data | Calibrate | Components of variation | Appendix: data download and extraction

Further examples

Initial processing | Conversion to weitrix | Calibration | Advanced calibration | Similar to limma voom | Exploration | Find genes with excess variation | Find components of variation | Examining components | Without varimax rotation, components may be harder to interpret | col can potentially be used as a design matrix

Concepts | What is a weitrix? | Rows and columns | Weights | Calibration | Examples | Linear models and components of variation | Dispersion | Testing with topconfects or limma | Practical details | Big datasets | Parallelism fine tuning | BiocParallel problems | OpenBLAS