Miniproject for Oxford Interdisciplinary Biosciences DTP data management and statistics course. Example data from 2-photon calcium imaging of mouse somatosensory cortex.
- Download git bash for windows https://gitforwindows.org/
- make a code directory (e.g. Documents/code)
- use the git bashj to clone the d2p type: 'git clone https://github.com/sarmstg/d2p.git' into git bash
- download anaconda https://www.anaconda.com/distribution/
- run jupyter notebook from anaconda navigator
- Create a new notebook in which you can work
- Load the .npy data from the repo data folder
- Make some plots of the fluoresence data on different trial types and outcomes, do you want to average at all?
- Is there more neural activity on go and nogo trials? How about hit or miss trials? (Does this depend on how you average??
- Push your new notebook back to the repo in the jupyter folder
- The session1.npy file is a 3d array of shape [n_cells x n_trials x time]
- The trial_info.npy dictionary has values that tell you about the trial (each array should have the length n_trials)
- Can the trial outcome (hit vs miss) be predicted from activty preceeding stimulation? This would involve analysing if there is a difference in the first 5 frames [0,1,2,3,4] of hit and miss trials.
- The first pass analysis would be to take a mean across the first 5 frames, so you have a matrix of shape [n_cells x n_trials]. This matrix can be passed to several differnet methods of classification e.g. a Support Vector Machine or a logitic regression (you will need to split your data into test and training). If this doesn't work, we can come up with an alternative to averaging across frames.