AlexsLemonade / AlexsLemonade/refinebio
Perform Principal Component Analysis on Agilent Two Color Dataset
- Dominant language
- Python
- Stars
- 135
- Forks
- 21
- PR merge metrics
- No merged PRs in 30d
Description
### Context
We have [the data](https://s3.amazonaws.com/crunch-outputs/random_twocolor.tar.gz)!
Now we need to know: is the data good? Specifically - Can we clearly separate channel one from channel two? Even better - can we automatically classify an experiment as being a reference or loop experiment?
### Problem or idea
### Solution or next step
Rich relearns how to use Pandas, scikit-learn and Jupyter.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by downloading the referenced random_twocolor dataset and reviewing the requested analysis with Pandas, scikit-learn, and Jupyter. Define the analysis output needed to determine whether the two channels separate and whether reference and loop experiments can be classified; the issue names no files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter, pandas, python, scikit-learn
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100