installation requirenments.txt or yaml file

[bioarpit1]

hi

I am trying to use this tool but installation is real hurdler can you please provide the yaml file i tried with all the dependencies but getting this error import-im6.q16: unable to open X server ' @ error/import.c/ImportImageCommand/346. import-im6.q16: unable to open X server' @ error/import.c/ImportImageCommand/346. ./deconPeaker2.py: line 5: os.environ[R_HOME]: command not found ./deconPeaker2.py: line 6: os.environ[PATH]: command not found import-im6.q16: unable to open X server ' @ error/import.c/ImportImageCommand/346. ./deconPeaker2.py: line 24: from: command not found ./deconPeaker2.py: line 29: from: command not found ./deconPeaker2.py: line 30: from: command not found ./deconPeaker2.py: line 31: from: command not found ./deconPeaker2.py: line 32: from: command not found ./deconPeaker2.py: line 37: syntax error near unexpected token(' ./deconPeaker2.py: line 37: `LOGS = log_infos() # logging informative'

Here is the deconpeaker2.py with R path as original deconpeaker.py was not able to access R without explicit R path

import os import sys

Set R environment variables

os.environ['R_HOME'] = '/nfs/amishra/mambaforge/envs/deconpeaker' os.environ['PATH'] = '/nfs/amishra/mambaforge/envs/deconpeaker/bin:' + os.environ['PATH']

Continue with the rest of the script

...

!/usr/bin/env python

title : deconPeaker.py

description : Identification and deconvolution of cell types based on chromatin accessibility.

author : Huamei Li

date : 29/05/2018

type : main script

version : 3.8

-----------------------------------------------------

load python modules

import numexpr from time import time

-----------------------------------------------------

load own python modules

from modules.deconv_mixed import from modules.simulate import from modules.peaks import * from modules.parse_opts import parse_opts

-----------------------------------------------------

global setting

LOGS = log_infos() # logging informative

numexpr.set_num_threads(numexpr.detect_number_of_cores())

-----------------------------------------------------

def preprocess(): ''' Create chromatin accessibility profile for pure samples, this step is the basis for subsequent specific cell type identification and mixture deconvolution. :return: 0

'''
LOGS.info('Loading peaks......')
merged_files = mp_read_peaks(ARGS.infos.PEAK.values.tolist(), kargs=ARGS)

LOGS.info('Chosing a list of non-overlapping, maximally significant peaks')
nonovp_peakfil = remove_redundant_peakfile(
        merged_files              , 
        ARGS.infos.CELL.unique()  , 
        ARGS.prefix               , 
        ARGS.outdir
    )

if ARGS.offset:
    LOGS.info('Filtering out the peaks nearby the TSS (+/-{} bps)'.format(ARGS.offset))
nonovp_peakfil = remove_peaks_nearbytss(nonovp_peakfil, ARGS.hg_genome, offset=ARGS.offset)

peaknum = get_line_number(nonovp_peakfil)
LOGS.info('Counting the number of fragments from each sample falling into each of {} peaks'.format(peaknum))
multi_get_reads(
        ARGS.infos.BAM.values.tolist(), 
        nonovp_peakfil                , 
        ARGS                          , 
        prefix = ARGS.prefix + '_reference_count_matrix',
        outdir = ARGS.outdir          ,
        bg = False
    )
phenotype = os.path.join(ARGS.outdir, ARGS.prefix + '_phenotype_classes.xls')
LOGS.info('Writing phenootype class into {}'.format(phenotype))
write_phenotype_file(ARGS.infos, phenotype)
LOGS.info('Preprocess finished')
return 0

def findctsps(): ''' find cell type specific peaks for pure samples :return: 0

'''
profile    = load_profile(ARGS.profile, ARGS.lib_strategy)
phenotypes = load_phenotypes(ARGS.phenotype)
LOGS.info('Loaded {} peaks and {} samples'.format(profile.shape[0], profile.shape[1] - 3))

ARGS.prefix = os.path.basename(ARGS.profile).rsplit('.', 1)[0]
LOGS.info('Normalizing pure profile by {} method to remove batch effects'.format(ARGS.norm))
profile_norm = normalize_profile(
        profile              , 
        ARGS.norm            , 
        profile.columns[3 : ],
        False                
    )

profile_norm = filter_weakpeaks(profile_norm)
curcnts, diffcnts = profile_norm.shape[0], profile.shape[0] - profile_norm.shape[0]
LOGS.info('Filtering out {} peaks and {} peaks have been remained'.format(diffcnts, curcnts))
del profile

LOGS.info('Identifying cell specific peaks accross pure cell profile')
markerpeaks = cellspecificpeaks(profile_norm, phenotypes, ARGS)

LOGS.info('Final number of cell specific peaks is {}'.format(markerpeaks.shape[0]))
return 0

def deconvolution(): ''' Based on pure cell type information, a deconvolution strategy was used to calculate the proportion of possible cell types in the mixed sample.

'''
if ARGS.format == 'BAM':
    LOGS.info('Counting the number of reads/fragments in each peak in the bam files by featureCounts')
    ARGS.mixture = multi_get_reads(
        ARGS.infos.BAM.values.tolist() ,
        ARGS.pure                      ,
        ARGS                           ,
        prefix = 'mixed_sample_profile',
        outdir = ARGS.outdir           ,
        bg = False
    )
mixsamples, sigprofile = load_profile([ARGS.mixture, ARGS.pure], ARGS.lib_strategy)
LOGS.info('Deconvolving......')
results = deconvcells(
        mixsamples       , 
        sigprofile       , 
        ARGS.lib_strategy,
        ARGS.pvalue      ,
        ARGS.method      ,
        ARGS.norm        ,
        ARGS.outdir      
    )
LOGS.info('Showing deconPeaker results: ')
print(results)
return 0

def simulate(): ''' simulate mixture cell sampels by sampling reads of pure cells

'''
LOGS.info('Simulating......')
ARGS.mixture = random_proportions(
        ARGS.pure_infos, 
        ARGS.mixture   , 
        ARGS.rep_counts, 
        ARGS.prefix    , 
        ARGS.outdir
    )
multi_simulate_bams(ARGS) # simulate data with specified proportions
LOGS.info('Okay!')
return 0

def run(): ''' deconPeaker main funcion and contains four sections, pureprofile, identifycells, deconvolution and simulation, respectivly :return: stat [int]

'''
global ARGS
ARGS, start  = parse_opts(), time()
funcs, modes = [preprocess, findctsps, deconvolution, simulate], \
               ['preprocess', 'findctsps', 'deconvolution', 'simulation']
ARGS.tmpdir = tmpdir = __import__('tempfile').mkdtemp('_deconPeaker')
try:    
    stat = funcs[modes.index(ARGS.sub_parser)]()
finally:
    __import__('shutil').rmtree(tmpdir) if os.path.exists(tmpdir) else 0
LOGS.info('Elapsed time is {} seconds'.format(time() - start))
return stat

if name == 'main': sys.exit(run())

original deconpeaker.py is giving this error

(deconpeaker) Singularity> ./deconPeaker.py Unable to determine R library path: [Errno 2] No such file or directory: '/opt/R/4.2.3/lib/R/bin/Rscript' cannot find system Renviron Fatal error: unable to open the base package

[lihuamei]

Hi Arpitm,

Apologies for my delay. I've added setup.py to update the DeconPeaker installation strategy. Please give it a try. Thanks for your feedback. If you encounter any other problems, please don't hesitate to contact me.

[lihuamei]

git clone git@github.com:lihuamei/DeconPeaker.git cd DeconPeaker && pip install . && cd ..