read2_paths: Read to SNPs to sets; Map SNPs to gene-sets/pathways

Description Usage Arguments Value Format See Also Examples

Description

Reads the sets/pathways, map the SNPs and genes to the gene-sets/pathways read2_paths uses the "genome_order" output(ordered_alldata, gs_locs) to assign genomic location indexes to each element in the gene-set. The permutation method must be defined (i.e. level = "snp" OR level = "gene").

Usage

1
2
read2_paths(ordered_alldata="",gs_locs="",sets_from="workspace",
sets_prefix="hsa",level="snp",envir="")

Arguments

ordered_alldata

Ordered data according to the SNPs genomic location. Traits start at column 7
Return variable from:
genome_results <-genome_order(all_data=all_data)
ordered_alldata <- genome_results$ordered_alldata

gs_locs

Gene annotation,indexes and number of observations
Return variable from genome_order():
genome_results <-genome_order(all_data=all_data)
gs_locs <- genome_results$gs_locs

sets_from

Location of the gene-sets. Default set to "workspace"
sets_from="workspace" OR sets_from="directory"
"directory", only will search for information in the working directory.

sets_prefix

Prefix of the gene-set variables or files.
Default set to sets_prefix= "hsa" e.g. Variables "hsa00010","hsa00020". OR files "hsaXXXXX.txt"
each variable/file contains the list of gene identifiers part of that pathway

level

The level at which the permutations will be performed. Assigns the indexes according to snps or genes
Default value "snp" level values = "snp" OR "gene"

envir

R environment where pathway data is stored. e.g(envir=.GlobalEnv, envir=gper.env)

Value

pathways

Pathway Id, Description,Number of Genes in the pathway, Number of genes found in the dataset, Number of SNPs found in the dataset

per_ors

A list of identifiers mapped to each pathway

Format

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Input: Ordered_alldata
name     Chromosome  Location GENE_ID   Symbol Orientation abpi abpilba
rs1001567          1 9194614     <NA>     <NA>        <NA> 0.96 0.89
rs1000313          1 15405489   23254 KIAA1026           + 0.93 0.57
rs1002365          1 19797248    <NA>     <NA>        <NA> 0.68 0.58
rs1002706          1 25051153    <NA>     <NA>        <NA> 0.71 0.02
rs1002487          1 26865971    6195  RPS6KA1           + 0.98 0.78

Input:gs_locs
      Symbol   Chromosome Location    Gene_ID Start_Indx Observations
 [1,] "ACYP2"  "2"        "54399633"  "98"    "35"       "1"         
 [2,] "AMPD3"  "11"       "10514707"  "272"   "898"      "1"         
 [3,] "ANK2"   "4"        "113830885" "287"   "479"      "4"
 
Input:pathway example
hsa04720
 [1]  10411    107  11261    114   1387 163688   ....
          
Output:pathways
ID         Name                           GenesInPath GenesFound SNPsInPath
"hsa00010" "Glycolysis / Gluconeogenesis" "  66"      "1"        "1"       
"hsa00020" "Citrate cycle (TCA cycle)"    "  31"      "0"        "0"       
"hsa00030" "Pentose phosphate pathway"    "  27"      "1"        "1"       

See Also

genes_permutation snps_permutation genome_order

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
## DEMO - SNP Level data stored in workspace #######################
# library(genomicper)
data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
genome_results <-genome_order(all_data=all_data)
ordered_alldata <- genome_results$ordered_alldata
gs_locs <- genome_results$gs_locs
data(hsa00100,hsa00120,hsa00130,hsa00140,hsa00190,hsa02010)

paths_res <- read2_paths(ordered_alldata=ordered_alldata,
gs_locs=gs_locs,sets_from="workspace",sets_prefix="hsa",
level="snp",envir=.GlobalEnv)		
pers_ids <- paths_res$per_ors
pathways<- paths_res$pathways
####################################################################

genomicper documentation built on May 1, 2019, 8:44 p.m.