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API
This page tells the basic usage of all D2 scripts. Notworthy, Output parameter in all APIs wanted a output prefix, while Out_file wanted a complete file name, including the file extension.
D2.py D2 compute the DNA density and DisTP of a single 3dg file. This scripts will output a bed-like file (den_dtp file) storing density and DisTP, and also a serials of scatter plots for manaully checking.
python D2.py D2 [options] <3dg_file> <index_file> <output>
Options:
-d Bool If is debug model. default: 0.
-w Bool If write to log. default: 0.
D2.py D2s compute the DNA density and DisTP of multiple 3dg files, considering that one researcher always deals with multiple cells. To use it, please first put all related 3dg files in one directory and name each cell by its cell name. The script will find these files automatically. The output is simialr with D2, except a directory containing all the computed den_dtp files for further analysis.
python D2.py D2 [options] <3dg_dir> <index_file> <output>
Options:
-d Bool If is debug model. default: 0.
-w Bool If write to log. default: 0.
D2.py sta gives the density and DisTP ranges, and a scatter plot as below. These ranges marked the boundaries to eliminate the outliers. 5% of genomib bins would be eliminated. D2 sta takes a directory of all related den_dtp files as input. It outputs the ranges to terminal, and also a scatter plot delineating the distributions.
python D2.py sta <den_dtp_dir> <index_file> <out_file>
D2.py map puts the bins on density-DisTP matrix, and stores the probability of genomic bins appearing at matrix bins (states).
D2 map takes the same input as ave. It outputs a hist formatted file storing the probability of genomic bins appearing at states. This probability of a genomic bin showed how many cells whose given genomic bins appeared at the given physical state. Noteworthy, the default ranges here are choosed based on 16 diploid cell types. It should work well among human and mouse diploid cells.
python D2.py map [options] <den_dtp_dir> <index_file> <out_file>
Options:
-n Int Bin number. default: 15
-ei Float Density min. default: 1
-ea Float Density max. default: 3.2
-ti Float DisTP min. default: 1.21
-ta Float DisTP max. default: 16
D2.py ave computes the mean and standard deviation (SD) of density and DisTP. The mean could represent the average states of cells from one cell type. The SD show the stachosity. If -f is assigned, a scatter plot of mean and SD will be outputted to figure out.
python D2.py ave [options] <hist_file> <out_file>
Options:
-f STR Figure output. default: None.
D2.py marks indexes and concatenates the markers. Markers should be in type of bed file. Please put all the interested markers in the mark_dir directory. Make sure the markers genome is same as index_file. The out_file will be a concatenated files of all the inputted markers.
python D2.py marks <mark_dir> <index_file> <out_file>
D2.py enrich plotted the enrichments of markers individually. It will output every enrichment in figures into output.
python D2.py enrich [options] <hist_file> <mark_idx_file> <output>
Options:
-t Str Tissue name. default: empty
-v Float Vmax and Vmin. default: 1.5
D2.py hiera ranks the physical states by hierarchy cluster. It will output three figures, the hierarchy result, the ranks at density-DisTP matrix and a cluster at density-DisTP matrix of four clusters. Nonetheless, the rank here sometimes is flipped. Use -f to flip it back.
python D3.py hiera [options] <hist_file> <mark_idx_file> <output>
Options:
-v Float Vmax and Vmin. default: 2
-f Flip Either flip the ranking result. default: False