A script designed to facilitate routine tables procedures for the analysis of tables obtained from fluorescent images
The aim of this script is obtaining figure and statitical pivot tables based on input data. Aditionaly, it provides a great opportunity to improve pandas knowledge.
Input data
There are 3 set of data (data, data2, data3).
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data contain tables connected with glutamine synthase and glutamate transporter characteristics in control group ('Контроль') and experimental group ('ЭС'): Volume, Surface Area, XYZ mass, Feret diameter. For our analysis we work only with Volume and Surface Area. Further, the dataset contain table neuro_count.xlsx with number of neurons in different hippocampal zones in observed groups and rec_count.xlsx data from electrophysiology analysis such as amplitude and decay time.
Electrophysiology data are attended only in this dataset. -
data2 has a similar to 'data' tables except for lack of electrophysiology data and the proteins of interest . Cx43 and s100b were used here.
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data3 contains the same set of tables where protein of interest is GFAP.
Result
There are four scenarios for available data analysis:
- 'GLT1_GS_graph_stat'. It grabs files named 'GS'/'GLT' from folder 'data', create figures and save statistical data into excel-file
- 'Cell_count_table_analysis'. It upload file 'neuro_count.xlsx' from folder 'data', then create figures and save statistical data into excel-file
- 'recordings calculate'. It grabs file named 'rec_count.xlsx' from folder 'data', create figures and save statistical data into excel-file
- '4_Cx43_s100b_graph_stat' and '5_GFAP_GLT1_graph_stat' scenarios work with 'data2' and 'data3' respectively
Dependencies
Python 3.8.8
glob2 0.7
matplotlib 3.5.0
numpy 1.21.2
pandas 1.3.5
seaborn 0.11.2
scipy 1.7.3