Data processing python scripts used for the Rotten Ice project by Carie Frantz cariefrantz@weber.edu
| Script | Description | Data files used |
|---|---|---|
| AlphaDiversityPlots.py | Used to generate alpha diversity metric boxplots from rarefied sequencing sample metrics | alpha_diversity_metrics_16S.csv, alpha_diversity_metrics_18S.csv |
| BetaClustermaps.py | Used to generate sample clustermaps from sample distance matrices | 16S_dist_matrix.csv, 18S_dist_matrix.csv |
| ChemDataPCA.py | Used to generate PCA plots of physical and chemical habitat parameters | metadata.csv |
| ChemHeatmaps.py | Used to generate heatmaps of sample metadata (physical, chemical, and biological measurements) | metadata.csv |
| SpearmanGrid.py | Used to generate a heatmap grid of Spearman correlation coefficients for metadata vs. taxonomic group abundance | metadata.csv, 16S_cDNA_ASV-table.csv, 18S_cDNA_ASV-table.csv |
| WebTaxBarplotsSimple.py | Used to generate interactive side-by-side bokeh taxonomic bar plots (web-based) from algae taxonomy data. Results at different taxonomic levels are built in as tabs. | AlgaeIDs.csv |
| WebTaxBarplotsStacked.py | Used to generate interactive stacked bokeh taxonomic bar plots (web-based) from Illumina sequencing data. Plots for absolute and relative abundance for both DNA and cDNA are stacked for ease of comparison. | 16S_DNA_ASV-table.csv, 16S_cDNA_ASV-table.csv, 18S_DNA_ASV-table.csv, 18S_cDNA_ASV-table.csv |
| CommunityPCoA.py | Generates PCoA plots split out and coded by metadata from pcoa ordinate data. | ordination.txt for different datasets of interest |
| DistplotTMB.py | Generates box-and-whisker plots comparing community distances in different ice horizons during different months to assess vertical homogenization. | distance-matrix.tsv |
| DistplotDNAcDNA.py | Generates box-and-whisker plots comparing DNA vs. cDNA community distances between samples. | distance-matrix.tsv for the different datasets |
| MetadataPCA.py | Similar to ChemDataPCA, but generates PCA biplots comparing samples based on their metadata. Displays vectors showing how different metadata parameters influence the principal components. | metadata.tsv |
| formatASVtable.py | Compiles ASV and taxonomy tables exported from QIIME2 as an excel file with tabs for counts and normalized data that is sorted by sample and taxonomy. | asv-table.tsv (taxonomy-added asv table from BIOM export) and otu-table-L7.csv (from QIIME2 View barplot export) |
| TaxBarplotPretty.py | Creates stacked barplots for publication from annotated relative abundance tables. | ASV-table-formatted-annotated.csv for datasets of interest |
The following scripts are called by several (most) of the main scripts listed above. Download these to the same directory as the scripts above.
| Script | Description |
|---|---|
| RottenIceModules.py | Collection of scripts shared by multiple other scripts in this project |
| RottenIceVars.py | Collection of variables shared by multiple other scripts in this project |
The code for this project requires the following list of packages in order to run.
- tkinter
- progress
- numpy
- pandas
- math
- scipy
- sklearn
- seaborn
- matplotlib
- bokeh
To install using conda, execute the command:
conda install tkinter
conda install progress
...and so on
To install using pip, execute the command:
pip install tkinter
pip install progress
...and so on
Once python and the packages listed above are installed, to run a script from command line execute the command:
python ChemDataPCA.py
python WebTaxBarplotsSimple.py
...and so on