Analysis

Description

Scikit-learn (sklearn) is a python library used for machine learning. sklearn contains simple and efficient tools for data mining and data analysis. Modules and functions include those for classification, regression, clustering, dimensionality reduction, model selection and data preprocessing. Many people have contributed to sklearn (list of authors)

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Description

FoCuS-point is stand-alone software for TCSPC correlation and analysis. FoCuS-point utilizes advanced time-correlated single-photon counting (TCSPC) correlation algorithms along with time-gated filtering and innovative data visualization. The software has been designed to be highly user-friendly and is tailored to handle batches of data with tools designed to process files in bulk. FoCuS-point also includes advanced diffusion curve fitting algorithms which allow the parameters of the correlation functions and thus the kinetics of diffusion to be established quickly and efficiently.

Description

FoCuS-scan is software for processing and analysis of large-scale scanning fluorescence correlation spectroscopy (FCS) data. FoCuS-scan can correlate data acquired on conventional turn-key confocal systems and in the form of xt image carpets.

Description

This notebook uses the rOMERO-gateway and EBImage to process an Image associated to the paper 'Timing of gene expression in a cell-fate decision system'.

The Image "Pos22" is taken from the dataset idr0040-aymoz-singlecell/experimentA/YDA306_AGA1y_PRM1r_Mating. It is a timelapse Image with 42 timepoints separated by 5 minutes. This Image is used to fit a model for the growth of the yeast cells. The notebook does not replicate any of the analysis of the above mentioned paper.

Its purpose is mainly to demonstrate the use of Jupyter, rOMERO-gateway and EBimage.

 

What it does:

  • For each time point of one movie:
    • Read the image for this time point  from the IDR
    • Threshold the images and count the cells using EBimage functions
  • Fit an exponential model to the count of cells against time to get a coefficient of grow (exponential factor)

 

 

 

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