| gcor | R Documentation |
Calculates correlation between track expressions over iterator bins inside the supplied genomic scope. Expressions are processed in pairs: (expr1, expr2), (expr3, expr4), etc. Only bins where both expressions are not NaN are used.
gcor(
expr1 = NULL,
expr2 = NULL,
...,
intervals = NULL,
iterator = NULL,
band = NULL,
method = c("pearson", "spearman", "spearman.exact"),
details = FALSE,
names = NULL
)
expr1 |
first track expression |
expr2 |
second track expression |
... |
additional track expressions, supplied as pairs (expr3, expr4, ...) |
intervals |
genomic scope for which the function is applied |
iterator |
track expression iterator. If 'NULL' iterator is determined implicitly based on track expression. |
band |
track expression band. If 'NULL' no band is used. |
method |
correlation method to use. One of 'pearson' (default), 'spearman' (approximate, memory-efficient), or 'spearman.exact' (exact, requires O(n) memory where n is number of non-NaN pairs). |
details |
if 'TRUE' returns summary statistics for each pair, otherwise returns correlations only. For Pearson, includes n, n.na, mean1, mean2, sd1, sd2, cov, cor. For Spearman methods, includes n, n.na, cor. |
names |
optional names for the pairs. If supplied, length must match the number of pairs. |
If 'details' is 'FALSE', a numeric vector of correlations. If 'details' is 'TRUE', a data frame with summary statistics for each pair.
gextract, gscreen, gsummary
gdb.init_examples()
gcor("dense_track", "sparse_track", intervals = gintervals(1, 0, 10000), iterator = 1000)
# Spearman correlation (approximate, memory-efficient)
gcor("dense_track", "sparse_track",
intervals = gintervals(1, 0, 10000),
iterator = 1000, method = "spearman"
)
# Exact Spearman correlation
gcor("dense_track", "sparse_track",
intervals = gintervals(1, 0, 10000),
iterator = 1000, method = "spearman.exact"
)
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