Practice | Mermaid-Markdown

Objective:

To practice creating a simple flowchart for each of my research method

References:

https://github.blog/2022-02-14-include-diagrams-markdown-files-mermaid/ https://tuts.alexmercedcoder.dev/2022/2/why-all-developers-should-master-markdown/ https://mermaid-js.github.io/mermaid/#/n00b-syntaxReference

Note:

Read syntax and Beginner's Guide

graph TD;
Raw_Data-->Backup_Data;
Raw_Data-->Process_Data;
Process_Data-->Clean_Data;
Clean_Data-->R;
R-->Statistical_Analyses;
Statistical_Analyses-->Data_visualizations;

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Physicochemical & Physiological Data:

flowchart LR
id1[(Working Directory)] --> |Environmental Metadata| B(read.csv)
B --> |Data Cleaning &</br>Subsetting| C(tidyverse)
C --> |Get Summary of </br>Inferential Stats| D(summarySE)
C--> |Do a Quick Plot| E(qplot)
C --> F(glm)
F-. Normality Test .->G(shapiro.test)
F--> |Homoscedasticity| H(bptest)
subgraph Interactions
direction TB
F--> I(TukeyHSD)
end

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CCA Data:

flowchart LR
id1[(Working Directory)] --> |CCA Metadata| B(read.delim)
B --> |Data Cleaning,</br>Subsetting, &</br>Transposition| C(tidyverse)
C --> |Create CCA Model| D(cca)
D -. Global Test .->G(anova.cca)
C--> |Get summary of CCA Model| E(summary)
C --> |Apply VIF less than 10|F(ordistep & vif.cca)
F-. Global Test .->G(anova.cca)
subgraph Plot
direction BT
D-->|Create simple plot|H(autoplot)
D--> |Modify plot</br>fortify ccamodel</br>take scores, site factor, and species data| I(ggvegan</br>ggrepel</br>ggplot2)
end

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Predictive Function Data:

flowchart LR
id1[(Working Directory)] --> |KO Metadata| B(read.csv)
B --> |Data Cleaning,</br>Subsetting otu_table,tax_table,sample_data, &</br>String Factor to Character| C(tidyverse)
C --> |convert to data matrix| D(as.matrix)
D--> |create large phyloseq object| E(phyloseq)
E --> |convert phyloseq to deseq|F(phyloseq_to_deseq2)
F-. Investigate test result .->G(results)
subgraph Statistics
direction BT
G-->|Create padj and log2Foldchange threshold|H(new vector)
H--> |Add vector to results| I(new results)
I--> |Subset, Filter results, and</br>write as new dataframe| J(Dataframe for contrast and plotting)
%%Thresholds applied here are 0.05 for padj.cutoff and 0.58 lfc.cutoff
end

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graph TD;
A{Dataframe for contrast and plotting} ---B(Ctenidia vs Mantle) 
A ---C(Ctenidia vs Water)
A ---D(Mantle vs Water)
B-. "contrast <- results(dds, contrast = c(level, factor1, factor2)" .-> E(results & summary)
C-. " " .->F(results & summary)
D-. " " .->G(results & summary)
E-. "Visualize the results</br> using DEGreport and tidyquant" .->H(Volcano Plot)

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