Group Project 4: Heatmap - biotec.tu-dresden.de · Group Project 4: Heatmap. Titel der...

CRTD – Programming for Bioinformatics, Prof. Michael Schroder, Predoc Melissa Adsame & Negin Malekian

20. December 2018

Anne Henka, Beatrice Krause, Yasmin Schulz, Lea Seibert

Group Project 4: Heatmap

Titel der PräsentationStruktureinheit der TU Dresden / Name Vorname des VortragendenOrt oder Anlass des Vortrags // 13.01.2018

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Description

A heatmap is a special diagram which visualizes values in matrix as colors. It is often used for biological data analysis, especially in cases where large-scale pairwise comparisons are made (e.g. chemical similarity of compounds in a database)

Input A: file with 5-mer DNA sequences.

Output: A heatmap visualizing the pairwise sequence identity of the 5-mers.


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Problem

Python script performs an all-vs-all sequence identity calculation and visualizes the results in a heatmap (using seaborn)

• Needed packages: numpy, pandas, seaborn, matplotlib


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Approach

1. Read in the file and store the 5-mers.

2. Iterate through all pairs of 5-mers

3. Calculate the sequence identity and store it (e.g. in a dictionary)

4. Transform the dictionary into the correct format for the heatmap function

5. Call the heatmap functions with your data and save the plot


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Solution - Step 1: Read in the file and store the 5-mers

Version I Version II


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Solution - Step 2: Iterate through all pairs of 5-mers

• creating a list with Needleman-Wunsch-

algorithm and figure out the sequence identity


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Solution - Step 2: Iterate through all pairs of 5-mersVersion I Version II

Almost same code, but…


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Solution - Step 3: calculate the sequence identityVersion I

Version II

Your storage location


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Solution - Step 4: Transform the dictionary into the correct format for the heatmap function

Version I

Version II


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Solution - Step 4: Transform the dictionary into the correct format for the heatmap function

• colour: cmap(“...”)• heading: plt.title(“...”)• axis title:

plt.xlabel(“...”)/plt.ylabel(“...”)• empty space: linewidths = …

[number]• showing values: annot = True


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Solution - Step 5: Call the heatmap functionsVersion I Version II


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Our problems in the last week

correct code

mistakes:

• Crating a list with needleman -> slide 6

• Variation in scoring scheme -> slide 7

• Transforming data into dataframe with the help of dict() -> slide 9

• Calling heat map -> slide 11

=> The answer was not far away!


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Do you have any questions?


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Resources

• https://stackoverflow.com/questions/37790429/seaborn-heatmap-using-pandas-dataframe

• https://pythonspot.com/generate-heatmap-in-matplotlib/

• https://seaborn.pydata.org/generated/seaborn.heatmap.html

https://stackoverflow.com/questions/37790429/seaborn-heatmap-using-pandas-dataframe



https://pythonspot.com/generate-heatmap-in-matplotlib/

Group Project 4: Heatmap - biotec.tu-dresden.de · Group Project 4: Heatmap. Titel der...

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