Release Notes Version 8 0

Version 8.0 of PLS_Toolbox and Solo was released in June, 2015.

For general product information, see PLS_Toolbox Product Page. For information on Solo, see Solo Product Page.

(back to Release Notes PLS Toolbox and Solo)

New Features in Solo and PLS_Toolbox

Multi-Block, Model and Data Fusion Tool

• Multiblock Tool - Interface to view, manipulate, and join data. Can be used for data and model fusion, or multi-block modeling.
  • Join multiple blocks of variables measured on the same samples (alignment based on labels, axis scales, or size).
  • Automatically align and join time-based blocks of data (based on time axis scale).
  • Optionally build models on one or more blocks and join outputs from those blocks (model fusion).
  • Choose and apply block-specific preprocessing before joining.
  • Save multiblock model to use to join new data, including application of defined preprocessing and models.
  • After building model from joined data, Analysis automatically splits loadings into component block segments for ease of interpretation.

Analysis and Models

• MLSCA - Multi-level simultaneous component analysis method added.
• Shortcuts to Data Fusion methods Multiblock Tool and Hierarchical Model Builder
• Re-designed Analysis and Preprocessing menus for ease-of-use and consistency.
• ANN now supports custom cross-validation.
• PLSDA variance captured plot now available.
• Better handleing of full-rank PCA simca sub-models (where Q residuals are zero.)

Plotting

• Significantly faster selection "linking".
• Improve handling of zoom status in newer versions of Matlab.
• Better handling of font sizes on different screen sizes and platforms.
• Smarter plot style (e.g., scatter vs. bar) assumptions in "automatic" mode.
• Fix rearranging of controls issue with newer versions of Matlab.
• Added Compress
• Connect Classes button has drop-down menu to display connection types.
• Add logic to NORMALIZE score distance to maximum calibration value.
• Respect include field when calculating scores limit
• Show knn score distance limits on plotscores plots.

Importers

• omnicreadr - Reads OMNICix HDF5 image files.
• Improved importer behavior with mixed length data using matchvars during import.
• Updated libraries to use new labspec 6 components including 64 bit support.
• xlsreadr - Add support for joining sheets in rows using matchvars.
  • Add support for using the same graphically-selected parsing options on ALL sheets.

Preprocessing

• glog Generalized Log Transform added to preprocessing options.
• pqnorm Probabilistic Quotient Normalization added to preprocessing options.
• glsw Clarified how ELS/EMM and EPO options are related
• Add support for handling missing data in both normaliz and mscorr (median only).

Other Interfaces

• Model Optimizer - Better handling of numeric data in comparison table, additional statistics, and improved handling of include field.
  • Add better support for model groupings in PLSDA and SVMDA within model optimizer.
• Better help integration with newer version of Matlab.
• Hierarchical Model Builder - Add vertical scrolling.

New Command-line Features and Functions

Misc New Functions

• eemoutlier - New function for automatically removing outliers in fluorescence PARAFAC models.
• mlsca Multi-level Simultaneous Component Analysis.
• multiblock Create or apply a multiblock model for joining data.

Command-Line Changes

• matchvars - Add logic to speed up some cases of joins.
• mdcheck - Allow use of KNN as data replacement method (replace missing data with data from sample(s) which are closest).
• jmlimit - Better handle degenerate cases when multiple confidence levels are requested (return VECTOR of zeros instead of single zero).
• cov_cv - Changed from SVDS to SVD.
• histaxes - Fix for when NaN's are present in data.
• als - Sort components by variance captured (if no constraints otherwise defining order).
• confusionmatrix - Report additional quantities for each class: count, classification error, precision and F1 score..
  • Standardize terminology: TP = count, TPR = proportion (rate) for confusion matrix quantities, and labels shown.
• comparemodels - Report the mean (class count weighted) of Classification Error, Precision, F1 Score for classification models.