aggr-class: AGGREGATES object class constructor
In RGMQL: GenoMetric Query Language for R/Bioconductor

Description Usage Arguments Details Value Examples

This class constructor is used to create instances of AGGREGATES object, to be used in GMQL functions that require aggregate on value.

SUM(value)

COUNT()

COUNTSAMP()

MIN(value)

MAX(value)

AVG(value)

MEDIAN(value)

STD(value)

BAG(value)

BAGD(value)

Q1(value)

Q2(value)

Q3(value)

value

string identifying name of metadata or region attribute

SUM: It prepares input parameter to be passed to the library function sum, performing all the type conversions needed
COUNT: It prepares input parameter to be passed to the library function count, performing all the type conversions needed
COUNTSAMP: It prepares input parameter to be passed to the library function countsamp, performing all the type conversions needed. It is used only with group_by functions
MIN: It prepares input parameter to be passed to the library function minimum, performing all the type conversions needed
MAX: It prepares input parameter to be passed to the library function maximum, performing all the type conversions needed
AVG: It prepares input parameter to be passed to the library function mean, performing all the type conversions needed
MEDIAN: It prepares input parameter to be passed to the library function median, performing all the type conversions needed
STD: It prepares input parameter to be passed to the library function standard deviation, performing all the type conversions needed
BAG: It prepares input parameter to be passed to the library function bag; this function creates comma-separated strings of attribute values, performing all the type conversions needed
BAGD: It prepares input parameter to be passed to the library function bagd; this function creates comma-separated strings of distinct attribute values, performing all the type conversions needed
Q1: It prepares input parameter to be passed to the library function fist quartile, performing all the type conversions needed
Q2: It prepares input parameter to be passed to the library function second quartile, performing all the type conversions needed
Q3: It prepares input parameter to be passed to the library function third quartile, performing all the type conversions needed

Aggregate object

## This statement initializes and runs the GMQL server for local execution 
## and creation of results on disk. Then, with system.file() it defines 
## the path to the folder "DATASET" in the subdirectory "example"
## of the package "RGMQL" and opens such folder as a GMQL dataset 
## named "exp" using CustomParser

init_gmql()
test_path <- system.file("example", "DATASET", package = "RGMQL")
exp = read_gmql(test_path)

## This statement copies all samples of exp dataset into res dataset, and 
## then calculates new metadata attribute sum_score for each of them: 
## sum_score is the sum of score values of the sample regions.

res = extend(exp, sum_score = SUM("score"))

## This statement copies all samples of exp dataset into res dataset, 
## and then calculates new metadata attribute min_pvalue for each of them: 
## min_pvalue is the minimum pvalue of the sample regions.

res = extend(exp, min_pvalue = MIN("pvalue"))

## This statement copies all samples of exp dataset into res dataset, 
## and then calculates new metadata attribute max_score for each of them: 
## max_score is the maximum score of the sample regions.

res = extend(exp, max_score = MAX("score"))

## The following cover operation produces output regions where at least 2 
## and at most 3 regions of exp dataset overlap, having as resulting region 
## attribute the average signal of the overlapping regions; 
## the result has one sample for each input cell value.

res = cover(exp, 2, 3, groupBy = conds("cell"), avg_signal = AVG("signal"))

## This statement copies all samples of 'exp' dataset into 'out' dataset, 
## and then for each of them it adds another metadata attribute, allScore, 
## which is the aggregation comma-separated list of all the values 
## that the region attribute score takes in the sample.

out = extend(exp, allScore = BAG("score"))

## This statement counts the regions in each sample and stores their number 
## as value of the new metadata RegionCount attribute of the sample.

out = extend(exp, RegionCount = COUNT())

## This statement copies all samples of exp dataset into res dataset, 
## and then calculates new metadata attribute std_score for each of them: 
## std_score is the standard deviation of the score values of the sample 
## regions.

res = extend(exp, std_score = STD("score"))

## This statement copies all samples of exp dataset into res dataset, 
## and then calculates new metadata attribute m_score for each of them: 
## m_score is the median score of the sample regions.

res = extend(exp, m_score = MEDIAN("score"))