| The SECIMTools a set of python tools that are available both as standalone and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, linear discriminant analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net). |
hg clone https://toolshed.g2.bx.psu.edu/repos/malex/secimtools
| Name | Description | Version | Minimum Galaxy Version |
|---|---|---|---|
| - A standalone tool. | 21.6.3 | 16.01 | |
| to remove specified group types or sampleIDs | 21.6.3 | 16.01 | |
| algorithm to select features. | 21.6.3 | 16.01 | |
| to compare groups | 21.6.3 | 16.01 | |
| with the same unique identifiers into a single file. | 21.6.3 | 16.01 | |
| from the data. | 21.6.3 | 16.01 | |
| - Flag features based on a user-specified threshold. | 21.6.3 | 16.01 | |
| 21.6.3 | 16.01 | ||
| - Calculate LOD and flag features in non-blank samples below threshold. | 21.6.3 | 16.01 | |
| - Flag features with discrepancies in retention time. | 21.6.3 | 16.01 | |
| - Count the number of digits before the decimal place. | 21.6.3 | 16.01 | |
| with visual summaries. | 21.6.3 | 16.01 | |
| of missing values using selected algorithm. | 21.6.3 | 16.01 | |
| - Predict sample groups. | 21.6.3 | 16.01 | |
| - A standalone tool. | 21.6.3 | 16.01 | |
| 21.6.3 | 16.01 | ||
| - Based on groups. | 21.6.3 | 16.01 | |
| - Calculate means per group and plot a heatmap. | 21.6.3 | 16.01 | |
| of p-values. | 21.6.3 | 16.01 | |
| using the order samples were run. | 21.6.3 | 16.01 | |
| 21.6.3 | 16.01 | ||
| 21.6.3 | 16.01 | ||
| - Calculate the coefficient of variation and flag potential outliers. | 21.6.3 | 16.01 | |
| from the data using a flag file. | 21.6.3 | 16.01 | |
| for visual summaries of the components. | 21.6.3 | 16.01 | |
| based on m/z ratio and retention time. | 21.6.3 | 16.01 | |
| on features. | 21.6.3 | 16.01 | |
| 21.6.3 | 16.01 | ||
| 21.6.3 | 16.01 | ||
| for feature selection. | 21.6.3 | 16.01 | |
| within a flag file. | 21.6.3 | 16.01 | |
| . | 21.6.3 | 16.01 | |
| on features. | 21.6.3 | 16.01 | |
| - Create pairwise BA plots for outlier detection. | 21.6.3 | 16.01 | |
| for the specified mean. | 21.6.3 | 16.01 | |
| Generate a wide format file with ranked columns from an input wide file. | 21.6.3 | 16.01 | |
| of data. | 21.6.3 | 16.01 | |
| - Perform a multi-way ANOVA with covariates and fixed effects. | 21.6.3 | 16.01 | |
| across 2 files. | 21.6.3 | 16.01 | |
| on features (rows). | 21.6.3 | 16.01 | |