||Searches given paths for all lines containing regex matches.|
||Return a table containing the vertices in a near maximal independent set of an undirected graph whose edges are given by a two-column table.|
||Rename duplicate column keys.|
grep(regex, path, max_count=100)¶
Searches given paths for all lines containing regex matches.
Print all lines containing the string
Print all lines containing digits in file1.txt and file2.txt:
>>> hl.grep('\d', ['data/file1.txt','data/file2.txt'])
grep()mimics the basic functionality of Unix
grepin parallel, printing results to the screen. This command is provided as a convenience to those in the statistical genetics community who often search enormous text files like VCFs. Hail uses Java regular expression patterns. The RegExr sandbox may be helpful.
- regex (
str) – The regular expression to match.
- path (
str) – The files to search.
- max_count (
int) – The maximum number of matches to return
- regex (
maximal_independent_set(i, j, keep=True, tie_breaker=None) → hail.table.Table¶
Return a table containing the vertices in a near maximal independent set of an undirected graph whose edges are given by a two-column table.
Run PC-relate and compute pairs of closely related individuals:
>>> pc_rel = hl.pc_relate(dataset.GT, 0.001, k=2, statistics='kin') >>> pairs = pc_rel.filter(pc_rel['kin'] > 0.125)
Starting from the above pairs, prune individuals from a dataset until no close relationships remain:
>>> related_samples_to_remove = hl.maximal_independent_set(pairs.i, pairs.j, False) >>> result = dataset.filter_cols( ... hl.is_defined(related_samples_to_remove[dataset.col_key]), keep=False)
Starting from the above pairs, prune individuals from a dataset until no close relationships remain, preferring to keep cases over controls:
>>> samples = dataset.cols() >>> pairs_with_case = pairs.key_by( ... i=hl.struct(id=pairs.i, is_case=samples[pairs.i].is_case), ... j=hl.struct(id=pairs.j, is_case=samples[pairs.j].is_case)) >>> def tie_breaker(l, r): ... return hl.cond(l.is_case & ~r.is_case, -1, ... hl.cond(~l.is_case & r.is_case, 1, 0)) >>> related_samples_to_remove = hl.maximal_independent_set( ... pairs_with_case.i, pairs_with_case.j, False, tie_breaker) >>> result = dataset.filter_cols(hl.is_defined( ... related_samples_to_remove.key_by( ... s = related_samples_to_remove.node.id.s)[dataset.col_key]), keep=False)
The vertex set of the graph is implicitly all the values realized by i and j on the rows of this table. Each row of the table corresponds to an undirected edge between the vertices given by evaluating i and j on that row. An undirected edge may appear multiple times in the table and will not affect the output. Vertices with self-edges are removed as they are not independent of themselves.
The expressions for i and j must have the same type.
The value of keep determines whether the vertices returned are those in the maximal independent set, or those in the complement of this set. This is useful if you need to filter a table without removing vertices that don’t appear in the graph at all.
This method implements a greedy algorithm which iteratively removes a vertex of highest degree until the graph contains no edges. The greedy algorithm always returns an independent set, but the set may not always be perfectly maximal.
tie_breaker is a Python function taking two arguments—say l and r—each of which is an
Expressionof the same type as i and j. tie_breaker returns a
NumericExpression, which defines an ordering on nodes. A pair of nodes can be ordered in one of three ways, and tie_breaker must encode the relationship as follows:
l < rthen
tie_breakerevaluates to some negative integer
l == rthen
tie_breakerevaluates to 0
l > rthen
tie_breakerevaluates to some positive integer
For example, the usual ordering on the integers is defined by:
l - r.
The tie_breaker function must satisfy the following property:
tie_breaker(l, r) == -tie_breaker(r, l).
When multiple nodes have the same degree, this algorithm will order the nodes according to
tie_breakerand remove the largest node.
Table– Table with the set of independent vertices. The table schema is one row field node which has the same type as input expressions i and j.
rename_duplicates(dataset, name='unique_id') → hail.matrixtable.MatrixTable¶
Rename duplicate column keys.
Requires the column key to be one field of type
>>> renamed = hl.rename_duplicates(dataset).cols() >>> duplicate_samples = (renamed.filter(renamed.s != renamed.unique_id) ... .select() ... .collect())
This method produces a new column field from the string column key by appending a unique suffix
_Nas necessary. For example, if the column key “NA12878” appears three times in the dataset, the first will produce “NA12878”, the second will produce “NA12878_1”, and the third will produce “NA12878_2”. The name of this new field is parameterized by name.
- dataset (
MatrixTable) – Dataset.
- name (
str) – Name of new field.
- dataset (