chromosight.cli package


chromosight.cli.chromosight module

Pattern exploration and detection

Explore and detect patterns (loops, borders, centromeres, etc.) in Hi-C contact maps with pattern matching.

chromosight detect [–kernel-config=FILE] [–pattern=loops]
[–pearson=auto] [–win-size=auto] [–iterations=auto] [–win-fmt={json,npy}] [–norm={auto,raw,force}] [–subsample=no] [–inter] [–tsvd] [–smooth-trend] [–n-mads=5] [–min-dist=0] [–max-dist=auto] [–no-plotting] [–min-separation=auto] [–dump=DIR] [–threads=1] [–perc-zero=auto] [–perc-undetected=auto] <contact_map> <prefix>
chromosight generate-config [–preset loops] [–click contact_map]
[–norm={auto,raw,norm}] [–win-size=auto] [–n-mads=5] [–chroms=CHROMS] [–inter] [–threads=1] <prefix>
chromosight quantify [–inter] [–pattern=loops] [–subsample=no]
[–win-fmt=json] [–kernel-config=FILE] [–norm={auto,raw,norm}] [–threads=1] [–n-mads=5] [–win-size=auto] [–perc-undetected=auto] [–perc-zero=auto] [–no-plotting] [–tsvd] <bed2d> <contact_map> <prefix>

chromosight list-kernels [–long] [–mat] [–name=kernel_name] chromosight test

performs pattern detection on a Hi-C contact map via template matching
Generate pre-filled config files to use for detect and quantify. A config consists of a JSON file describing parameters for the analysis and path pointing to kernel matrices files. Those matrices files are tsv files with numeric values as kernel to use for convolution.
Given a list of pairs of positions and a contact map, computes the correlation coefficients between those positions and the kernel of the selected pattern.
Prints information about available kernels.
Download example data and run loop detection on it.
Arguments for detect:
contact_map The Hi-C contact map to detect patterns on, in
bedgraph2d or cool format.
prefix Common path prefix used to generate output files.
Extensions will be added for each file.
Arguments for quantify:
bed2d Tab-separated text files with columns chrom1, start1
end1, chrom2, start2, end2. Each line correspond to a pair of positions (i.e. a position in the matrix).
contact_map Path to the contact map, in bedgraph2d or
cool format.
prefix Common path prefix used to generate output files.
Extensions will be added for each file.
Arguments for generate-config:
prefix Path prefix for config files. If prefix is a/b,
files a/b.json and a/b.1.txt will be generated. If a given pattern has N kernel matrices, N txt files are created they will be named a/b.[1-N].txt.
-e, --preset=loops
 Generate a preset config for the given pattern. Preset configs available are “loops” and “borders”. [default: loops]
-c, --click=contact_map
 Show input contact map and uses double clicks from user to build the kernel. Warning: memory-heavy, reserve for small genomes or subsetted matrices.
-C, --chroms=CHROMS
 Comma-separated list of chromosome names. When used with –click, this will show each chromosome’s one-by-one sequentially instead of the whole genome. This is useful to reduce memory usage.
Arguments for list-kernels:
 Only show information related to a particular kernel.[default: all]
--long Show default parameters in addition to kernel names.
--mat Prints an ascii representation of the kernel matrix.
Basic options:
-h, --help Display this help message.
--version Display the program’s current version.
--verbose Displays the logo.
-n, –norm={auto,raw,force} Normalization / balancing behaviour. auto: weights
present in the cool file are used. raw: raw contact values are used. force: recompute weights and overwrite existing values. raw[default: auto]
-I, --inter Enable to consider interchromosomal contacts. Warning: Experimental feature with high memory consumption, only use with small matrices.
-m, --min-dist=auto
 Minimum distance from the diagonal (in base pairs). at which detection should operate. [default: auto]
-M, --max-dist=auto
 Maximum distance from the diagonal (in base pairs) for detection. [default: auto]
-P, --pattern=loops
 Which pattern to detect. This will use preset configurations for the given pattern. Possible values are: loops, loops_small, borders, hairpins and centromeres. [default: loops]
-p, --pearson=auto
 Pearson correlation threshold when detecting patterns in the contact map. Lower values leads to potentially more detections, but more false positives. [default: auto]
-s, --subsample=INT
 If greater than 1, subsample INT contacts from the matrix. If between 0 and 1, subsample a proportion of contacts instead. Useful when comparing matrices with different coverages. [default: no]

-t, –threads=1 Number of CPUs to use in parallel. [default: 1] -u, –perc-undetected=auto Maximum percentage of non-detectable pixels (nan) in

windows allowed to report patterns. [default: auto]
-z, --perc-zero=auto
 Maximum percentage of empty (0) pixels in windows allowed to report patterns. [default: auto]
Advanced options:
-d, --dump=DIR Directory where to save matrix dumps during processing and detection. Each dump is saved as a compressed npz of a sparse matrix and can be loaded using scipy.sparse.load_npz.
-i, --iterations=auto
 How many iterations to perform after the first template-based pass. [default: 1]
-k, --kernel-config=FILE
 Optionally give a path to a custom JSON kernel config path. Use this to override pattern if you do not want to use one of the preset patterns.
--no-plotting Disable generation of pileup plots.
-N, –n-mads=5 Maximum number of median absolute deviations below
the median of the bin sums distribution allowed to consider detectable bins. [default: 5]
-S, --min-separation=auto
 Minimum distance required between patterns, in basepairs. If two patterns are closer than this distance in both axes, the one with the lowest score is discarded. [default: auto]
-T, --smooth-trend
 Use isotonic regression when detrending to reduce noise at long ranges. Do not enable this for circular genomes.
-V, --tsvd Enable kernel factorisation via truncated svd. Accelerates detection, at the cost of slight inaccuracies. Singular matrices are truncated to retain 99.9% of the information in the kernel.
-w, –win-fmt={json,npy} File format used to store individual windows
around each pattern. Window order matches patterns inside the associated text file. Possible formats are json and npy. [default: json]
-W, --win-size=auto
 Window size (width), in pixels, to use for the kernel when computing correlations. The pattern kernel will be resized to match this size. Linear linear interpolation is used to fill between pixels. If not specified, the default kernel size will be used instead. [default: auto]

Capture the stderr of the test run.

chromosight.cli.chromosight.logo_version(logo, ver)[source]

chromosight.cli.score module

Module contents