sircle 0.0.5

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sci-RegulatoryClusteringModel Mac M1 version

If you want to read more about how SiRCle works, please check out our paper: https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1

Note this is the version which doesn't contain the patient stratification using the VAE since tensorflow doesn't work on the M1 chip.

We will be looking into how we can make this work in the future.

Install

Optionally create a new conda env.

conda create --name scircle python=3.8
conda activate scircle

pip install sircle

Note on Windows

We have tested our code on Windows (10) and Mac (pro) computers, I'm not sure how it would go on a Windows 7 machine so if you have issues post an issue.

Note on libraries/dependenices

If you find that things don't install please let us know! We've done our best to make it reproducible but different environments may mess things up so we're happy to help you debug, just post an issue on the github.

Note we expect python 3.8 so if things don't work first time, check you're running python 3.8 and then try again :)

Run

See the examples folder for a proper tutorial with data included that you can run!

*If you have any troubles running the tutorial on a windows machine, please let us know and we can help to sort out any issues.

Quick version

from sircle import SciRCM
# FORMAT must be csv :) 
prot_file = f'path to the output from protein differential abundence file'
rna_file = f'path to the output from differential expression analysis file'
meth_file = f'path to the output from methylation DCpG analysis file'

# Note we assume your methylation CpGs map to a single gene, if they don't see the section below.
# logFC_rna = column name in your RNA file that has your RNA logFC (same for the protein and CpG)
# padj_rna = column name in your RNA file that has your padj value (same for protein and CpG)
# NOTE: these need to be unique from one another since we merge the datasets, if they aren't, you need
# to update your csv files.
# Lastly: ensembl_gene_id this is the gene ID column, All must use the same identifier, and this must be
# labelled the same in each file, if it isn't, update your column names before running.

rcm = SciRCM(meth_file, rna_file, prot_file, 
             "logFC_rna", "padj_rna", "CpG_Beta_diff", "padj_meth", "logFC_protein", "padj_protein",
             "ensembl_gene_id", sep=',',
             rna_padj_cutoff=0.05, 
             prot_padj_cutoff=0.05, 
             meth_padj_cutoff=0.05,
             rna_logfc_cutoff=1.0, 
             prot_logfc_cutoff=0.5, 
             meth_diff_cutoff=0.1, 
             output_dir='',
             non_coding_genes=['None'],
             output_filename='RCM_Output.csv',
             bg_type = '(P&M)|(P&R)|(M&R)'
         )
rcm.run()
df = rcm.get_df()
# That DF now has your rcm clustering results, how easy was that :D

Making your CpGs map to a single gene version

from scircm import filter_methylation_data_by_genes
meth_df = pd.read_csv(f'path to the output from methylation DCpG analysis file')
# Note: you need to pass it: 
# 1) the gene ID column, here it is 'ensembl_gene_id'
# 2) the padj column: here it is 'padj_meth'
# 3) the logFC or test statistic column: here it is 'CpG_Beta_diff'
filtered_meth_df = filter_methylation_data_by_genes(meth_df, 'ensembl_gene_id', 'padj_meth', 'CpG_Beta_diff')

Now you can run the first version :)

R version

First install Rtools if you haven't done this yet. There are different versions (windows: https://cran.r-project.org/bin/windows/Rtools/, macOS: https://cran.r-project.org/bin/macosx/tools/)

If you don't have conda, you'll need to do the below, first make sure you have reticulate installed.

#install.packages('BiocManager')
#BiocManager::install('basilisk')
library(basilisk)
# Set this to be the path to the example data we downloaded
data_dir <- '../data_example/'

protFile <- paste0(data_dir, 'prot_DE_Stage IV_sircle.csv')
rnaFile <- paste0(data_dir, 'rna_DE_Stage IV_sircle_renamed-cols.csv')
methFile <- paste0(data_dir, 'filtered_cpg_DE_Stage IV_sircle.csv')
# Note if you use gene names here you would need to change this to be the column that has the gene ID in it
geneId <- 'ensembl_gene_id'

sircleFileName <- paste0(data_dir, "SircleR-RCM.csv")

# Use basilisk to create an environment we can use
bas_scircm <- BasiliskEnvironment(envname="simple_sircle",
                                   pkgname="sircle",
                                   packages=c("numpy==1.20"),
                                  pip=c("sircle")
)

#logFC_rna = column name in your RNA file that has your RNA logFC (same for the protein and CpG)
#padj_rna = column name in your RNA file that has your padj value (same for protein and CpG)
#NOTE: these need to be unique from one another since we merge the datasets, if they aren't, you need
#to update your csv files.
#Lastly: ensembl_gene_id this is the gene ID column, All must use the same identifier, and this must be
#labelled the same in each file, if it isn't, update your column names before running.
res <- basiliskRun(env=bas_scircm, fun=function(args) {
    rcm <- sircleRCM(rnaFile, methFile, protFile, geneId,  "logFC_rna", "padj_rna", "CpG_Beta_diff", "padj_meth", "logFC_protein", "padj_protein",
                 outputFileName = sircleFileName, 
                 envName="simple_sircle")
    # Do something with pandas
    return(rcm)
})


# Plot the sircle function
sirclePlot(sircleFileName, regLabels="Regulation_Grouping_2") 

# Note you need to have the entrez gene ID added to your csv file
# Run ORA on the groups
sircleORAHuman(sircleFileName, "entrezgene_id", "Regulation_Grouping_2")

Regulatory clustering model

The general table of how we define regulatory clusters.

Methylation	RNAseq	Proteomics	Regulation driver_1	Regulation driver_2	Regulation_Grouping1	Regulation_Grouping2	Regulation_Grouping3
Hypermethylation	DOWN	DOWN	Methylation increase (MDS)	None	MDS	MDS	MDS
Hypermethylation	UP	DOWN	mRNA increase (TPDE)	Protein decrease (TMDS)	TPDE+TMDS	TPDE+TMDS	TMDS
Hypermethylation	UP	UP	mRNA increase (TPDE)	None	TPDE	TPDE	TPDE
Hypermethylation	DOWN	UP	Methylation increase (MDS)	Protein increase (TMDE)	MDS+TMDE	TMDE	TMDE
Hypermethylation	No Change	UP	mRNA increase (TPDE)	Protein increase (TMDE)	TPDE+TMDE	TMDE	TMDE
Hypermethylation	No Change	DOWN	mRNA increase (TPDE)	Protein decrease (TMDS)	TPDE+TMDS	TMDS	TMDS
Hypermethylation	UP	No Change	mRNA increase (TPDE)	Protein decrease (TMDS)	TPDE+TMDS	TPDE+TMDS	TMDS
Hypermethylation	DOWN	No Change	Methylation increase (MDS)	Protein increase (TMDE)	MDS+TMDE	MDS+TMDE	TMDE
Hypermethylation	No Change	No Change	Methylation increase (ncRNA)	None	MDS-ncRNA	MDS_ncRNA	MDS_ncRNA
Hypomethylation	DOWN	DOWN	mRNA decrease (TPDS)	None	TPDS	TPDS	TPDS
Hypomethylation	UP	DOWN	Methylation decrease (MDE)	Protein decrease (TMDS)	MDE+TMDS	TMDS	TMDS
Hypomethylation	UP	UP	Methylation decrease (MDE)	None	MDE	MDE	MDE
Hypomethylation	DOWN	UP	mRNA decrease (TPDS)	Protein increase (TMDE)	TPDS+TMDE	TPDS+TMDE	TMDE
Hypomethylation	No Change	UP	mRNA decrease (TPDS)	Protein increase (TMDE)	TPDS+TMDE	TMDE	TMDE
Hypomethylation	No Change	DOWN	mRNA decrease (TPDS)	Protein decrease (TMDS)	TPDS+TMDS	TMDS	TMDS
Hypomethylation	UP	No Change	Methylation decrease (MDE)	Protein decrease (TMDS)	MDE+TMDS	MDE+TMDS	TMDS
Hypomethylation	DOWN	No Change	mRNA decrease (TPDS)	Protein increase (TMDE)	TPDS+TMDE	TPDS+TMDE	TMDE
Hypomethylation	No Change	No Change	Methylation decrease (ncRNA)	None	MDE+ncRNA	MDE_ncRNA	MDE_ncRNA
No Change	DOWN	UP	mRNA decrease (TPDS)	Protein increase (TMDE)	TPDS+TMDE	TPDS+TMDE	TMDE
No Change	UP	DOWN	mRNA increase (TPDE)	Protein decrease (TMDS)	TPDE+TMDS	TPDE+TMDS	TMDS
No Change	DOWN	DOWN	mRNA decrease (TPDS)	None	TPDS	TPDS	TPDS
No Change	UP	UP	mRNA increase (TPDE)	None	TPDE	TPDE	TPDE
No Change	No Change	UP	Protein increase (TMDE)	None	TMDE	TMDE	TMDE
No Change	No Change	DOWN	Protein decrease (TMDS)	None	TMDS	TMDS	TMDS
No Change	UP	No Change	mRNA increase (TPDE)	Protein decrease (TMDS)	TPDE+TMDS	TPDE+TMDS	TMDS
No Change	DOWN	No Change	mRNA decrease (TPDS)	Protein increase (TMDE)	TPDS+TMDE	TPDS+TMDE	TMDE
No Change	No Change	No Change	NoChange	NoChange	NoChange	NoChange	NoChange

Please post questions and issues related to sci-rcm on the Issues <https://github.com/ArianeMora/scircm/issues>_ section of the GitHub repository.