| digeR {digeR} | R Documentation |
Start the Graphical User Interface for digeR.
digeR supports spots correlation analysis, score plot, classification, feature selection and power analysis.
digeR()
digeR GUI options:
| File | Read in data and image, quit | |
| Open | upload the txt file | |
| Upload_gel_image | upload the JPG image as a reference for spots correlation analysis | |
| Quit | dispose the GUI | |
| ——————– | ||
| Correlation | Spots correlation analysis | |
| Dataset | select the group to look at | |
| Spot List | select the spot to look at | |
| Selected feature | upload the feature list from feature selection | |
| Load features | upload the feature list from an saved R workspace | |
| Pearson, Kendall, Spearman | type of correlation coefficient to be calculated: "pearson" (default), "kendall", or "spearman" | |
| Show the correlation | plot the spots with required correlation | |
| Correlation Coefficiency | change the coefficiency threshold | |
| Show spot ID | plot spots with ID | |
| Show number | Show ID for those spots with required correlation | |
| ——————– | ||
| Score Plot | PCA and PLSR score plot | |
| Plot Type | select either PCA or PLSR score plot | |
| Top N component | plot score plot with top N components | |
| Pair-wise | plot selected 2 components | |
| Component 1 and 2 | two components in the pairwise plot | |
| Group | set the color for the two groups | |
| With label | plot the sample ID | |
| Scaling | scale the data before plotting | |
| Plot | plot the score plot | |
| ——————– | ||
| Classification | Classification | |
| Methods | select the method for the classification | |
| Scaling | scale the data before classification | |
| Arguments | ||
| Method | way for estimate the covariance matrix. | |
| "moment" | standard estimators of the mean and variance | |
| "mle" | MLEs, | |
| "mve" | to use cov.mve | |
| "t" | robust estimates based on a t distribution | |
| nComp | number of component for fitting PCR or PLSR | |
| N-fold CV | number of fold in the cross validation | |
| nboot | number of bootstrap in the classification | |
| Selected feature | upload the feature list from feature selection | |
| Load features | upload the feature list from an saved R workspace | |
| leave-one-out cv | classification with leave-one-out cross validation | |
| N-fold cv | classification with n-fold cross validation | |
| Bootstrap | classification with bootstrap | |
| Run classification | press button to do the classification | |
| Save | save the prediction results into an R workspace | |
| Legened | where the legend will be put | |
| ROC curve | generate ROC plot | |
| Prediction result | store the classification results in the selected items | |
| ——————– | ||
| Feature Selection | Select important features | |
| Method | select feature selection method | |
| Scaling | scale the data before feature selection | |
| Arguments | ||
| Method | same as Method in Classification | |
| Ncomp | same as ncomp in Classification | |
| Top | select the top n variables from the feature selection | |
| Ntree | number of trees to grow in randomForest | |
| Mtry | Number of variables randomly sampled as candidates at each split. Default sqrt(number of variables) | |
| Mfinal | the number of iterations for which boosting is run or the number of trees to use | |
| Run feature selection | press to start feature selection | |
| Select featuers | store the selected features in the selected items | |
| Save features | save the features into an R workspace | |
| ——————– | ||
| Power | Power analysis | |
| Single Spots | univariate power analysis | |
| Gel | multivariate power analysis for experiment design | |
| Significant level | set the significant level | |
| Power | set the power level to be achieved | |
| Sample size per group | sample size for achieving certain significant level and power in each group | |
| Spot Number | set the spots to calculated | |
| Calculate | calculate the one being left blank (either power, sample size or significant level) |
digeR is built upon gWidgets package. Make sure gWidgets package is properly installed.
Yue Fan yue.fan@ucd.ie,Thomas Brendan Murphy, R. William G. Watson