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Showing content from https://github.com/fionarhuang/treeclimbR_article below:

GitHub - fionarhuang/treeclimbR_article

This repository provides toy examples to understand treeclimbR and codes to reproduce figures in treeclimbR article. The used R version was 3.6.1 (2019-07-05)

Data container: TreeSummarizedExperiment
Algorithm: treeclimbR
Data visualization: TreeHeatmap.


# TreeSummarizedExperiment: version (1.3.1)
remotes::install_github(“fionarhuang/TreeSummarizedExperiment@dfa2097c61f900c3b2989a4f045f864b992fc8c5")
# treeclimbR: version (0.1.1)
remotes::install_github("fionarhuang/treeclimbR@treeclimbR_article")
BiocManager::install("fionarhuang/TreeHeatmap_old")

Below are results of one toy dataset.

To capture signal patterns on the tree, treeclimbR firstly propose multiple candidates that are generated by tuning a parameter t:

Heatmap shows counts of entities (rows) in samples (columns) split by groups. Branches that include entities with signals are colored in orange.

Nodes detected by from treeclimbR are compared to those detected by BH under FDR 0.05.

Reproduce figures in the manuscript of treeclimbR

install snakemake & conda
Clone this repository and set directory to simulation_microbe/
Modify the configuration file (config.yaml) to specify paths of input and output files.
- Rbin: the path to the system R
- condaR: the path to conda R
  - run create an environment: conda env create -f envs/lefse.yml
  - run conda activate lefse
  - run which R to get path to conda R
Specify the R library paths in the .Renviron. If there is less than 3 library paths, then set R_LIBS_3 = "" to remove the third library path.
dry run the pipeline using snakemake --use-conda -npr
run the pipeline using snakemake --use-conda --cores n (n is the number of cores to be used)
Once the pipeline is run successfully, all figures could be generated using all_figure.R under the folder simulation_microbe/summary/.

2. Non-parametric synthetical microbial data (see here) 3. AML-sim and BCR-XL-sim (in folder cytof)

The semi-simulated data should be downloaded in the cytof/data/ folder from HDCytoData using Download.R before running the pipeline

AML-sim (DA folder)
- install snakemake
- Set directory to DA/
- Specify paths to input and output files in the configuration file (config.yaml)
- Specify R library paths in the .Renviron. If there is less than 3 library paths, then set R_LIBS_3 = "" to remove the third library path.
- dry run the pipeline using snakemake -npr
- run the pipeline using snakemake --cores n (n is the number of cores to be used)
BCR-XL-sim (DS folder). Similarly to run AML-sim

Figues 3 is generated using all_figure.R

4. Infant gut microbial data (in folder microbe) 5. Mouse miRNA data (in folder miRNA) 6. Mouse cortex scRNAseq data(in folder LPS)

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