tissue_specificity: Tissue Specificity (TS): produces a list of samples with...

In langmead-lab/snapr: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts

Tissue Specificity (TS): produces a list of samples with their tissues marked which either contain queried junctions (1) or not (0); can be used as input to significance testing methods such as Kruskal-Wallis to look for tissue enrichment (currently only works for the GTEx compilation).

Description

Lists the number of samples labeled with a specific tissue type. Samples are filtered for ones which have junctions across all the user-specified groups. That is, if a sample only appears in the results of some of the groups (from their basic queries) it will be assigned a 0, otherwise if it is in all of the groups' results it will be assigned a 1. This is similar to the SSC high level query type, but doesn't sum the coverage.

Usage

tissue_specificity(..., group_names = NULL)

Arguments

...	One or more QueryBuilder objects
group_names	Optional vector of strings representing the group names

Details

The samples are then grouped by their tissue type (e.g. Brain). This is useful for determining if there's an enrichment for a specific tissue in the set of junctions queried. Results from this can be fed to a statistical test, such as the Kruskal-wallis non-parametric rank test. This query is limited to GTEx only, due to the fact that GTEx is one of the few compilations that has consistent and complete tissue metadata.

Value

A DataFrame of all samples in the compilation with either a 0 or 1 indicating their occurrence and shared status (if > 1 group passed in). Occurrence here is if the sample has at least one result with > 0 coverage, and further, if > 1 group is passed in, then if it occurs in the results of all groups. Also includes the sample tissue type and sample_id.

Examples

in1 <- QueryBuilder(compilation = "gtex", regions = "chr4:20763023-20763023")
in1 <- set_coordinate_modifier(in1, Coordinates$EndIsExactOrWithin)
in1 <- set_row_filters(in1, strand == "-")

in2 <- QueryBuilder(compilation = "gtex", regions = "chr4:20763098-20763098")
in2 <- set_coordinate_modifier(in2, Coordinates$StartIsExactOrWithin)
in2 <- set_row_filters(in2, strand == "-")

tissue_specificity(list(in1, in2))

langmead-lab/snapr documentation built on May 4, 2022, 7:41 p.m.