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