CMEE Coursework Groupwork Practicals - Python Week 2

• Authors: Laiyin Zhou, Yaxin Liu, Sebastian Dohne, and Yangfeng Wang
• Languages: Python, R, Latex

Groupwork Practicals

This repository contains these main groupwork practicals:

1. Align DNA Sequences
2. Missing Oaks Problem
3. Visuaising regression groupwork
4. KeyWestAnnualMeanTemperature.R
5. TAutoCorrLatexCode.tex

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align_seqs_better.py

• Description:
  A Python script that reads two DNA sequences from FASTA files, aligns them using a custom scoring function, and identifies the best-scoring alignments. The results are displayed in the console and saved in a text file.

• Features:

  • Reads DNA sequences from FASTA files.
  • Calculates alignment scores.
  • Displays best alignments.
  • Saves best alignments in a binary file.

• Modules Used:

  • ipdb: Debugging tool.
  • os: Handles file path manipulation and directory creation.
  • sys: Accesses command-line arguments and system parameters.
  • csv: Reads from and writes to CSV files.

• Installation:

  1. Clone the repository:

     git clone <repository-url>

  2. Navigate to the project directory:

     cd /path/to/your/project

  3. Install dependencies if required (optional):

     pip install -r requirements.txt

• Usage:

  • Specify FASTA files via command line, or use defaults from ../data/.
  • Command-line Usage:

    python3 align_seqs_better.py <fasta_file_1> <fasta_file_2>

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Missing_oaks_problem.py

• Description:
  This Python script identifies rows where the genus is 'Quercus' (oak) in a CSV dataset and saves these rows to a new output file. It reads from an input CSV, checks each row for the genus, and writes matching rows to an output CSV without the header.

• Script Information:

  • Script Name: Missing_oaks_problem.py
  • Authors: Yaxin Liu (yaxin.liu24@imperial.ac.uk), Laiyin Zhou (l.zhou24@imperial.ac.uk), Sebastian Dohne (sebastian.dohne24@imperial.ac.uk), Yanfeng Wang (yanfeng.wang24@imperial.ac.uk)
  • Version: 0.0.1

• Functions and Workflow:

  • Read Input: Reads the input CSV file TestOaksData.csv from the ../data directory.
  • Filter for Oaks: Processes each row, checking if the genus is 'Quercus'. Matching rows are added to a list called oaks.
  • Write Output: Writes each oak entry to an output file oaks.csv located in the ../results directory.

• Arguments:
  No command-line arguments are required.

• Dependencies:
  Requires Python 3 and uses standard libraries:

  • csv: To read and write CSV files.
  • os: To ensure output directories exist.

• Usage:

  • Run the script using:

    python3 missing_oaks.py

  • The script will:
    • Read and filter oak entries from ../data/TestOaksData.csv.
    • Write the filtered entries to ../results/oaks.csv.

• Additional Notes:

  • The script will create the ../results directory if it does not already exist.
  • Ensure that TestOaksData.csv is available in the specified input directory (../data) before running the script.
  • This script omits headers in the output file by design, only including oak entries.

PP_Regress_loc.R

• Description:
  This script generates multiple plots containing linear regressions of predator and prey masses grouped by predator lifestage and feeding type by location from the EcolArchives-E089-51-D1 dataset. It also calculates regression coefficients for each group and saves them to a CSV file.

  • Functions:

    • The script does not define reusable functions but performs the following tasks:
      • Creates a combined dataset with log-transformed predator and prey masses.
      • Identifies groups with insufficient data for regression.
      • Generates regression plots faceted by feeding interaction type.
      • Saves regression results to a CSV file.

  • Arguments:
    This script does not require any arguments when running.

• Dependencies:

  • ggplot2: For data visualization.
  • dplyr: For data manipulation.
  • broom: For organizing regression results.
  • purrr: For functional programming operations.

• Usage:

  • To run the script, use the following command in a terminal:

    Rscript PP_Regress_loc.R

  • The script will:
    • Load the dataset and preprocess it.
    • Generate a PDF file with regression plots (test_Visualising_regression_analysis.pdf).
    • Save a CSV file containing regression coefficients (PP_Regress_bylocation_Results.csv).

KeyWestAnnualMeanTemperature.R

• Description:
  This script evaluates the correlation between consecutive years' annual mean temperatures in Key West using a permutation test. It computes the observed correlation coefficient, generates a null distribution, and calculates an approximate p-value. The results are visualized in a histogram saved as a PDF.

• Dependencies:

  • base (core R functions like cor and sample)
  • graphics (for plotting)

• Usage:

  • Run the script with:

  Rscript KeyWestAnnualMeanTemperature.R

  • The script will:
    • Load the dataset KeyWestAnnualMeanTemperature.RData.
    • Compute the observed correlation coefficient.
    • Perform a permutation test with 10,000 iterations.
    • Save a histogram of correlation coefficients to ../results/Coefficients.pdf.

TAutoCorrLatexCode.tex

• Description: This LaTeX document generates a report analyzing the correlation between annual mean temperatures in Key West.

• Dependencies:

  • LaTeX distribution: Ensure you have a LaTeX compiler such as pdflatex, xelatex, or lualatex installed.
  • Packages:
    • 'geometry':for setting page margins
    • 'setspace':for controlling line spacing
    • 'graphicx':for inserting images
    • 'amsmath' :for mathematical expressions

• Usage: To compile the LaTeX file and generate a PDF

  • Input:
    • The script references the histogram plot Coefficients.pdf located in the ../results directory. Ensure this file exists (run "KeyWestAnnualMeanTemperature.R" first) before compiling the LaTeX document.
  • Output:
    • If you want the output PDF to be saved in a specific directory (e.g., ../results), compile with the following command in terminal:

  pdflatex -output-directory=../results TAutoCorrLatexCode.tex