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comparison MatrixEQTL/man/MatrixEQTL_cis_code.Rd @ 0:cd4c8e4a4b5b draft

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author jasonxu
date Fri, 12 Mar 2021 08:12:46 +0000
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1 \name{MatrixEQTL_cis_code}
2 \alias{MatrixEQTL_cis_code}
3
4 \title{Sample code for cis/trans-eQTL analysis with Matrix eQTL}
5 \description{
6 The following code is the best starting point for those who want to perform cis-/trans-eQTL analysis with Matrix eQTL.
7 }
8 \references{
9 The package website: \url{http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/}
10 }
11 \seealso{
12 See \code{\link{Matrix_eQTL_engine}} for reference and other sample code.
13 }
14 \author{
15 Andrey Shabalin \email{ashabalin@vcu.edu}
16 }
17 \examples{
18 # Matrix eQTL by Andrey A. Shabalin
19 # http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/
20 #
21 # Be sure to use an up to date version of R and Matrix eQTL.
22
23 # source("Matrix_eQTL_R/Matrix_eQTL_engine.r");
24 library(MatrixEQTL)
25
26 ## Location of the package with the data files.
27 base.dir = find.package('MatrixEQTL');
28
29 ## Settings
30
31 # Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
32 useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS
33
34 # Genotype file name
35 SNP_file_name = paste(base.dir, "/data/SNP.txt", sep="");
36 snps_location_file_name = paste(base.dir, "/data/snpsloc.txt", sep="");
37
38 # Gene expression file name
39 expression_file_name = paste(base.dir, "/data/GE.txt", sep="");
40 gene_location_file_name = paste(base.dir, "/data/geneloc.txt", sep="");
41
42 # Covariates file name
43 # Set to character() for no covariates
44 covariates_file_name = paste(base.dir, "/data/Covariates.txt", sep="");
45
46 # Output file name
47 output_file_name_cis = tempfile();
48 output_file_name_tra = tempfile();
49
50 # Only associations significant at this level will be saved
51 pvOutputThreshold_cis = 2e-2;
52 pvOutputThreshold_tra = 1e-2;
53
54 # Error covariance matrix
55 # Set to numeric() for identity.
56 errorCovariance = numeric();
57 # errorCovariance = read.table("Sample_Data/errorCovariance.txt");
58
59 # Distance for local gene-SNP pairs
60 cisDist = 1e6;
61
62 ## Load genotype data
63
64 snps = SlicedData$new();
65 snps$fileDelimiter = "\t"; # the TAB character
66 snps$fileOmitCharacters = "NA"; # denote missing values;
67 snps$fileSkipRows = 1; # one row of column labels
68 snps$fileSkipColumns = 1; # one column of row labels
69 snps$fileSliceSize = 2000; # read file in slices of 2,000 rows
70 snps$LoadFile(SNP_file_name);
71
72 ## Load gene expression data
73
74 gene = SlicedData$new();
75 gene$fileDelimiter = "\t"; # the TAB character
76 gene$fileOmitCharacters = "NA"; # denote missing values;
77 gene$fileSkipRows = 1; # one row of column labels
78 gene$fileSkipColumns = 1; # one column of row labels
79 gene$fileSliceSize = 2000; # read file in slices of 2,000 rows
80 gene$LoadFile(expression_file_name);
81
82 ## Load covariates
83
84 cvrt = SlicedData$new();
85 cvrt$fileDelimiter = "\t"; # the TAB character
86 cvrt$fileOmitCharacters = "NA"; # denote missing values;
87 cvrt$fileSkipRows = 1; # one row of column labels
88 cvrt$fileSkipColumns = 1; # one column of row labels
89 if(length(covariates_file_name)>0) {
90 cvrt$LoadFile(covariates_file_name);
91 }
92
93 ## Run the analysis
94 snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE);
95 genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);
96
97 me = Matrix_eQTL_main(
98 snps = snps,
99 gene = gene,
100 cvrt = cvrt,
101 output_file_name = output_file_name_tra,
102 pvOutputThreshold = pvOutputThreshold_tra,
103 useModel = useModel,
104 errorCovariance = errorCovariance,
105 verbose = TRUE,
106 output_file_name.cis = output_file_name_cis,
107 pvOutputThreshold.cis = pvOutputThreshold_cis,
108 snpspos = snpspos,
109 genepos = genepos,
110 cisDist = cisDist,
111 pvalue.hist = TRUE,
112 min.pv.by.genesnp = FALSE,
113 noFDRsaveMemory = FALSE);
114
115 unlink(output_file_name_tra);
116 unlink(output_file_name_cis);
117
118 ## Results:
119
120 cat('Analysis done in: ', me$time.in.sec, ' seconds', '\n');
121 cat('Detected local eQTLs:', '\n');
122 show(me$cis$eqtls)
123 cat('Detected distant eQTLs:', '\n');
124 show(me$trans$eqtls)
125
126 ## Make the histogram of local and distant p-values
127
128 plot(me)
129 }