This is an implementation of the LDA classifier proposed in "Predicting cell populations in single cell mass cytometry data", an automated method to annotate the CyTOF dataset.
Implementation description
- function Model = CyTOF_LDAtrain(SamplesFolder,mode,LabelsFolder,RelevantMarkers,arcsinh_transform)
CyTOF_LDAtrain function can be used to train a Linear Discriminant Analysis (LDA) classifier, on the labeled CyTOF samples. The trained classifier model can be then used to automatically annotate new CyTOF samples.
For full description, check CyTOF_LDAtrain
- function Predictions = CyTOF_LDApredict(TrainedModel,DataFolder,mode,RejectionThreshold)
CyTOF_LDApredict function can be used to produce automatic cell type annotations for new samples, based on the trained LDA classifier using CyTOF_LDAtrain function.
For full description, check CyTOF_LDApredict
Implementation is available in R and Matlab
The 'Examples' folder contains R notebooks showing how to use the implementation, using CyTOF and Flow Cytometry datasets.
Experiments code description
In the following six folders, AML, BMMC, PANORAMA, MultiCenter, HMIS-1 and HMIS-2, we provide Matlab scripts to reproduce the results shown in the pre-print, including the LDA and the Nearest Median classifiers performance, and comparisons with ACDC and DeepCyTOF. Also, we provide a full documentation in pdf format.
Further, the k-NN classifier implementation is available in the HMIS-2 folder, including the editing and the feature selection functions.
The DeepCyTOF_on_HMIS folder contains python scripts needed to apply DeepCyTOF on our HMIS-1 and HMIS-2 datasets.
All datasets can be downloaded from Flow Repository (http://flowrepository.org/id/FR-FCM-ZYTT)