scircm 1.0.3

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sci-RegulatoryClusteringModel

Python version

This is all for the python version of our package, if you are interested in the R version please go here: https://github.com/ArianeMora/SiRCleR

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

Install

Optionally create a new conda env.

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

pip install scircm

Note on Mac M1

Unfortunately, the Mac M1 has some issues with tensorflow, so we have not been able to get our package to work on a M1 mac yet. If you get it working please let us know your solution as others may benefit!

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 scircm 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('reticulate')

Create a new environment and install scircm.

virtualenv_create(
      envname = "ml",
      python = NULL,
      packages = "scircm",
      system_site_packages = getOption("reticulate.virtualenv.system_site_packages",
                                       default = FALSE)
    )

Then run the following script!

library(tidyverse) # install these if you don't have them
library(dplyr)
library(reticulate)

# If things fail here it's because you need to the steps above
use_condaenv("ml", required = TRUE) # OR use_virtualenv("ml", required = TRUE)  # depending on how you installed it!
scircm <<- import("scircm")    # Make global

prot_file <- 'path to the output from protein differential abundence file'
rna_file <- 'path to the output from differential expression analysis file'
meth_file <- 'path to the output from methylation DCpG analysis file'

rcm = scircm$SciRCM(meth_file, rna_file, prot_file, 
             "logFC_rna", "padj_rna", "CpG_Beta_diff", "padj_meth", "logFC_protein", "padj_protein",
             "ensembl_gene_id", sep=',',
             rna_padj_cutoff=0.05, 
             prot_padj_cutoff=0.05, 
             meth_padj_cutoff=0.05,
             rna_logfc_cutoff=1.0, 
             prot_logfc_cutoff=0.5, 
             meth_diff_cutoff=0.1, 
             output_dir='',
             non_coding_genes=['None'],
             output_filename='RCM_Output.csv',
             bg_type = '(P&M)|(P&R)|(M&R)'
         )
rcm$run()
df <- rcm$get_df()

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.