All scripts for the analysis of the paper:
Prediction of functional markers of mass cytometry data via deep learning (2019). Solis-Lemus, C., X. Ma, M. Hostetter II, S. Kundu, P. Qiu, D. Pimentel-Alarcon.
- For individual 1, we have collected 100,000 cells, and for each cell we have 50 features: 18 surface markers (which identify the type of cell) and 32 functional markers (which identify the function of the cell)
- We collect this information at baseline: matrix 100k by 50. Future: collect data at several experimental moments. So, if we have N experiments => N+1 matrices 100k by 50: B (baseline), E_1,...,E_N
- We want to use the baseline information to predict the funcional markers from surface markers (which do not change with experimental settings). That is, use B to predict E_i with a neural network
- The structure of the data is given by: each row is a cell. The meaning of the columns are as follows:
Surface markers:
- 191-DNA
- 193-DNA
- 115-CD45
- 139-CD45RA
- 142-CD19
- 144-CD11b
- 145-CD4
- 146-CD8
- 148-CD34
- 147-CD20
- 158-CD33
- 160-CD123
- 167-CD38
- 170-CD90
- 110_114-CD3
Functional markers:
- 141-pPLCgamma2
- 150-pSTAT5
- 152-Ki67
- 154-pSHP2
- 151-pERK1/2
- 153-pMAPKAPK2
- 156-pZAP70/Syk
- 159-pSTAT3
- 164-pSLP-76
- 165-pNFkB
- 166-IkBalpha
- 168-pH3
- 169-pP38
- 171-pBtk/Itk
- 172-pS6
- 174-pSrcFK
- 176-pCREB
- 175-pCrkL
See script folder. The file notebook-log.md has the detailed steps in the analyses.