ranbo4s

A Python package for mining key transcription factors from transcriptome data.

Installation

pip install numpy pandas statsmodels scipy seaborn matplotlib ranbo4s

Usage

from ranbo4s import clinks, predict

if __name__ == '__main__':
	# download TF-target set, only need to run once!!!
	clinks.download_data()

	# list available TF-target set
	sets_names = clinks.get_sets(species='Homo sapiens')
	print(sets_names)

	# list avaiable TFs
	tfs_codes = clinks.get_tfs(set_name='human_hocomoco_CLink_rci4_1w_0.8')
	print(tfs_codes)

	# get targets
	targets = clinks.get_targets(set_name='human_hocomoco_CLink_rci4_1w_0.8', tf='NFKB1_HUMAN.H11MO.1.B')
	print(targets)
	
	# predict one DEG-list
	result = predict.rbo(set_names=['human_jaspar_CLink_rci4_1w_0.8', 'human_hocomoco_CLink_rci4_1w_0.8'], list_file='./ranbo4s_data/input_deg-list.txt', thread_n=16, bs_n=500)

	# save result to text file
	result.to_csv('output.txt', sep='\t', index=False, encoding='utf-8')
	
	# plot OLC matrix
	predict.plot_olc(set_names=['human_jaspar_CLink_rci4_1w_0.8', 'human_hocomoco_CLink_rci4_1w_0.8'], list_file='./ranbo4s_data/input_deg-list.txt', tf='MA0844.2_XBP1', win=30)