functions.R 33.7 KB
Newer Older
Rauschenberger's avatar
Rauschenberger committed
1

Rauschenberger's avatar
Rauschenberger committed
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

#' @name spliceQTL-package
#' @md
#' @aliases spliceQTL
#' 
#' @title
#' 
#' Alternative Splicing
#' 
#' @description
#' 
#' This R package includes various functions
#' for applying the global test of alternative splicing.
#' Some functions only work on the virtual machine (see below).
#' 
#' @seealso 
#' 
#' Prepare BBMRI and Geuvadis data:
#' * \code{\link{get.snps.geuvadis}} (not VM)
#' * \code{\link{get.snps.bbmri}} (only VM)
#' * \code{\link{get.exons.geuvadis}} (only VM)
#' * \code{\link{get.exons.bbmri}} (only VM)
#' 
#' Process samples and covariates:
#' * \code{\link{match.samples}}
#' * \code{\link{adjust.samples}}
#' * \code{\link{adjust.covariates}}
#' 
#' Search for exons and SNPs:
#' * \code{\link{map.genes}}
#' * \code{\link{map.exons}}
#' * \code{\link{map.snps}}
#' * \code{\link{drop.trivial}}
#' 
#' Test for alternative splicing:
#' * \code{\link{test.single}}
#' * \code{\link{test.multiple}}
#'
#' @keywords documentation
#' @docType package
#' 
NULL


Rauschenberger's avatar
Rauschenberger committed
46
47
48
49
50
51
52
53
54
55
#' @export
#' @title
#' Get SNP data (Geuvadis)
#' 
#' @description
#' This function transforms SNP data (local machine).
#' 
#' @param chr
#' chromosome: integer \eqn{1-22}
#' 
Rauschenberger's avatar
Rauschenberger committed
56
#' @param data
Rauschenberger's avatar
Rauschenberger committed
57
58
#' local directory for VCF (variant call format)
#' and SDRF (sample and data relationship format) files
Rauschenberger's avatar
Rauschenberger committed
59
#' 
Rauschenberger's avatar
Rauschenberger committed
60
61
62
#' @param path
#' local directory for output
#' 
Rauschenberger's avatar
Rauschenberger committed
63
64
65
#' @examples
#' path <- "C:/Users/a.rauschenbe/Desktop/spliceQTL/data"
#' 
Rauschenberger's avatar
Rauschenberger committed
66
get.snps.geuvadis <- function(chr,data=NULL,path=getwd()){
Rauschenberger's avatar
Rauschenberger committed
67
    
Rauschenberger's avatar
Rauschenberger committed
68
69
70
71
72
73
74
75
76
77
78
79
    if(is.null(data)){
        data <- path
        # download SNP data
        file <- paste0("GEUVADIS.chr",chr,".PH1PH2_465.IMPFRQFILT_BIALLELIC_PH.annotv2.genotypes.vcf.gz")
        url <- paste0("http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/genotypes/",file)
        destfile <- file.path(data,file)
        if(!file.exists(destfile)){
            utils::download.file(url=url,destfile=destfile,method="auto")
        }
        # transform with PLINK
        setwd(data)
        system(paste0("plink --vcf GEUVADIS.chr",chr,".PH1PH2_465.IMPFRQFILT_BIALLELIC_PH.annotv2.genotypes.vcf.gz",
Rauschenberger's avatar
Rauschenberger committed
80
                  " --maf 0.05 --geno 0 --make-bed --out snps",chr),invisible=FALSE)
Rauschenberger's avatar
Rauschenberger committed
81
82
83
84
85
86
87
88
        # obtain identifiers
        file <- "E-GEUV-1.sdrf.txt"
        url <- paste("http://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/",file,sep="")
        destfile <- file.path(data,file)
        if(!file.exists(destfile)){
            utils::download.file(url=url,destfile=destfile,method="auto")
        }
    }
Rauschenberger's avatar
Rauschenberger committed
89
90
    
    # read into R
Rauschenberger's avatar
Rauschenberger committed
91
92
93
    bed <- file.path(data,paste("snps",chr,".bed",sep=""))
    bim <- file.path(data,paste("snps",chr,".bim",sep=""))
    fam <- file.path(data,paste("snps",chr,".fam",sep=""))
Rauschenberger's avatar
Rauschenberger committed
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
    X <- snpStats::read.plink(bed=bed,bim=bim,fam=fam)
    X$fam <- NULL; all(diff(X$map$position) > 0)
    
    # fitler MAF
    maf <- snpStats::col.summary(X$genotypes)$MAF
    cond <- maf >= 0.05
    X$genotypes <- X$genotypes[,cond]
    X$map <- X$map[cond,]
    
    # format
    colnames(X$genotypes) <- paste0(X$map$chromosome,":",X$map$position)
    snps <- methods::as(object=X$genotypes,Class="numeric")
    class(snps) <- "integer"
    
    # change identifiers
Rauschenberger's avatar
Rauschenberger committed
109
    samples <- utils::read.delim(file=file.path(data,"E-GEUV-1.sdrf.txt"))
Rauschenberger's avatar
Rauschenberger committed
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
    match <- match(rownames(snps),samples$Source.Name)
    rownames(snps) <- samples$Comment.ENA_RUN.[match]
    snps <- snps[!is.na(rownames(snps)),]
    
    save(object=snps,file=file.path(path,paste0("Geuvadis.chr",chr,".RData")))
}


#' @export
#' @title
#' Get SNP data (BBMRI)
#' 
#' @description
#' This function transforms SNP data (virtual machine).
#' 
#' @param chr
#' chromosome: integer \eqn{1-22}
#' 
#' @param biobank
#' character "CODAM", "LL", "LLS", "NTR", "PAN", "RS", or NULL (all)
#' 
#' @param path
#' data directory
#' 
Rauschenberger's avatar
Rauschenberger committed
134
#' @param size
Rauschenberger's avatar
Rauschenberger committed
135
136
#' maximum number of SNPs to read in at once;
#' trade-off between memory usage (low) and speed (high)
Rauschenberger's avatar
Rauschenberger committed
137
#' 
Rauschenberger's avatar
Rauschenberger committed
138
139
140
#' @examples
#' path <- "/virdir/Scratch/arauschenberger/trial"
#'
Rauschenberger's avatar
Rauschenberger committed
141
get.snps.bbmri <- function(chr,biobank=NULL,path=getwd(),size=500*10^3){
Rauschenberger's avatar
Rauschenberger committed
142
143
144
145
146
147
148
149
150
151
152
153

    start <- Sys.time()
    message(rep("-",times=20)," chromosome ",chr," ",rep("-",times=20))
    
    p <- 5*10^6 # (maximum number of SNPs per chromosome, before filtering)
    skip <- seq(from=0,to=p,by=size)
    if(is.null(biobank)){
        study <- c("CODAM","LL","LLS0","LLS1","NTR0","NTR1","PAN","RS")
    } else if(biobank=="LLS"){
        study <- c("LLS0","LLS1")
    } else if(biobank=="NTR"){
        study <- c("NTR0","NTR1")
Rauschenberger's avatar
Rauschenberger committed
154
    } else if(biobank %in% c("CODAM","LL","PAN","RS")){
Rauschenberger's avatar
Rauschenberger committed
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
        study <- biobank
    } else{
        stop("Invalid biobank.",call.=FALSE)
    }
    collect <- matrix(list(),nrow=length(skip),ncol=length(study))
    colnames(collect) <- study
    
    for(i in seq_along(skip)){
        message("\n","chunk ",i,": ",appendLF=FALSE)
        for(j in seq_along(study)){
            message(study[j],"  ",appendLF=FALSE)
            
            # Locating files on virtual machine.
            dir <- study[j]
            if(study[j]=="LLS0"){dir <- "LLS/660Q"}
            if(study[j]=="LLS1"){dir <- "LLS/OmniExpr"}
            if(study[j]=="NTR0"){dir <- "NTR/Affy6"}
            if(study[j]=="NTR1"){dir <- "NTR/GoNL"}
            path0 <- file.path("/mnt/virdir/Backup/RP3_data/HRCv1.1_Imputation",dir)
Rauschenberger's avatar
Rauschenberger committed
174
            path1 <- path
Rauschenberger's avatar
Rauschenberger committed
175
176
177
178
179
            file0 <- paste0("chr",chr,".dose.vcf.gz")
            file1 <- paste0(study[j],".chr",chr,".dose.vcf.gz")
            file2 <- paste0(study[j],".chr",chr,".dose.vcf")
            
            # Reading in files.
Rauschenberger's avatar
Rauschenberger committed
180
181
            #vcf <- vcfR::read.vcfR(file=file.path(path1,file2),skip=skip[i],nrows=size,verbose=FALSE)
            vcf <- vcfR::read.vcfR(file=file.path(path0,file0),skip=skip[i],nrows=size,verbose=FALSE)
Rauschenberger's avatar
Rauschenberger committed
182
183
184
185
186
187
188
189
190
191
192
            vcf <- vcf[vcf@fix[,"CHROM"]!="",] # bug fix
            vcf@fix[,"ID"] <- paste0(vcf@fix[,"ID"],"_",seq_len(dim(vcf)["variants"]))
            collect[i,j][[1]] <- vcf
            stop <- dim(vcf)["variants"]==0
            final <- dim(vcf)["variants"]<size
            if(stop){break}
        }
        print(utils::object.size(collect),units="Gb")
        end <- Sys.time()
        if(stop){break}
        
Rauschenberger's avatar
Rauschenberger committed
193
        # only retain SNPs measured in all studies
Rauschenberger's avatar
Rauschenberger committed
194
        position <- lapply(seq_along(study),function(j) collect[i,j][[1]]@fix[,"POS"])
Rauschenberger's avatar
Rauschenberger committed
195
196
197
198
199
200
        common <- Reduce(f=intersect,x=position)
        for(j in seq_along(study)){
            cond <- match(x=common,table=position[[j]])
            collect[i,j][[1]] <- collect[i,j][[1]][cond,]
        }
        
Rauschenberger's avatar
Rauschenberger committed
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
        # Calculating minor allele frequency.
        num <- numeric(); maf <- list()
        for(j in seq_along(study)){
            num[j] <- dim(collect[i,j][[1]])["gt_cols"] # replace by adjusted sample sizes?
            maf[[j]] <- num[j]*vcfR::maf(collect[i,j][[1]])[,"Frequency"]
        }
        cond <- rowSums(do.call(what="cbind",args=maf))/sum(num)>0.05
        if(sum(cond)==0){if(final){break}else{next}}
        
        # Filtering out genotypes.
        for(j in seq_along(study)){
            gt <- vcfR::extract.gt(collect[i,j][[1]][cond,])
            gt[gt=="0|0"] <- 0
            gt[gt=="0|1"|gt=="1|0"] <- 1
            gt[gt=="1|1"] <- 2
            storage.mode(gt) <- "integer"
            collect[i,j][[1]] <- gt
        }
        
        if(final){break}
    }
    
    # Removing empty rows.
    cond <- apply(collect,1,function(x) all(sapply(x,length)==0))
    collect <- collect[!cond,,drop=FALSE]
Rauschenberger's avatar
Rauschenberger committed
226

Rauschenberger's avatar
Rauschenberger committed
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
    # Fusing all matrices.
    snps <- NULL
    for(i in seq_len(nrow(collect))){
        inner <- NULL
        for(j in seq_len(ncol(collect))){
            add <- collect[i,j][[1]]
            colnames(add) <- paste0(colnames(collect)[j],":",colnames(add))
            inner <- cbind(inner,add)
        }
        snps <- rbind(snps,inner)
    }
    attributes(snps)$time <- end-start
    rownames(snps) <- sapply(strsplit(x=rownames(snps),split="_"),function(x) x[[1]])
    snps <- t(snps)
    
    # Filter samples.
    rownames(snps) <- sub(x=rownames(snps),pattern="LLS0|LLS1",replacement="LLS")
    rownames(snps) <- sub(x=rownames(snps),pattern="NTR0|NTR1",replacement="NTR")
Rauschenberger's avatar
Rauschenberger committed
245

Rauschenberger's avatar
Rauschenberger committed
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
    if(is.null(biobank)){
        save(object=snps,file=file.path(path1,paste0("BBMRI.chr",chr,".RData")))
    } else {
        save(object=snps,file=file.path(path1,paste0(biobank,".chr",chr,".RData")))
    }
}


#' @export
#' @title
#' Get exon data (Geuvadis)
#' 
#' @description
#' This function transforms exon data (virtual machine).
#' 
#' @param path
#' data directory 
#' 
#' @examples
#' path <- "/virdir/Scratch/arauschenberger/trial"
#' 
get.exons.geuvadis <- function(path=getwd()){

    nrows <- 303544
    file <-"/virdir/Scratch/rmenezes/data_counts.txt"
    exons <- utils::read.table(file=file,header=TRUE,nrows=nrows)
    exons <- exons[exons[,"chr"] %in% 1:22,] # autosomes
    rownames(exons) <- exon_id <- paste0(exons[,"chr"],"_",exons[,"start"],"_",exons[,"end"])
    gene_id <- as.character(exons[,4])
    exons <- t(exons[,-c(1:4)])

    save(list=c("exons","exon_id","gene_id"),file=file.path(path,"Geuvadis.exons.RData"))
}


#' @export
#' @title
#' Get exon data (BBMRI)
#' 
#' @description
#' This function transforms exon data (virtual machine).
#' 
#' @param path
#' data directory 
#' 
#' @examples
#' path <- "/virdir/Scratch/arauschenberger/trial"
#' 
get.exons.bbmri <- function(path=getwd()){
    
    # sample identifiers:
    # (1) loading quality controlled gene expression data 
    # (2) extracting sample identifiers
    # (3) removing identifiers without SNP data
    # (4) translating identifiers
    utils::data(rnaSeqData_ReadCounts_BIOS_cleaned,package="BBMRIomics") # (1)
Rauschenberger's avatar
Rauschenberger committed
302
303
    cd <- SummarizedExperiment::colData(counts)[,c("biobank_id","imputation_id","run_id")] # (2)
    counts <- NULL
Rauschenberger's avatar
Rauschenberger committed
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
    names(cd) <- substr(names(cd),start=1,stop=3) # abbreviate names
    cd <- cd[!is.na(cd$imp),] # (3)
    cd$id <- NA # (4)
    cd$id[cd$bio=="CODAM"] <- sapply(strsplit(x=cd$imp[cd$bio=="CODAM"],split="_"),function(x) x[[2]])
    cd$id[cd$bio=="LL"] <- sub(pattern="1_LLDeep_",replacement="",x=cd$imp[cd$bio=="LL"])
    cd$id[cd$bio=="LLS"] <- sapply(strsplit(x=cd$imp[cd$bio=="LLS"],split="_"),function(x) x[[2]])
    cd$id[cd$bio=="NTR"] <- sapply(strsplit(x=cd$imp[cd$bio=="NTR"],split="_"),function(x) x[[2]])
    cd$id[cd$bio=="PAN"] <- cd$imp[cd$bio=="PAN"]
    cd$id[cd$bio=="RS"] <- sub(pattern="RS1_|RS2_|RS3_",replacement="",x=cd$imp[cd$bio=="RS"])
    
    # Identify individual not with "id" but with "bio:id".
    any(duplicated(cd$id)) # TRUE
    sapply(unique(cd$bio),function(x) any(duplicated(cd$id[x]))) # FALSE
    
    # exon data:
    # (1) loading exon expression data
    # (2) extracting sample identifiers
    # (3) retaining autosomes
    # (4) retaining samples from above
    load("/virdir/Backup/RP3_data/RNASeq/v2.1.3/exon_base/exon_base_counts.RData") # (1)
    colnames(counts) <- sub(pattern=".exon.base.count.gz",replacement="",x=colnames(counts)) # (2)
    autosomes <- sapply(strsplit(x=rownames(counts),split="_"),function(x) x[[1]] %in% 1:22) # (3)
    exons <- counts[autosomes,cd$run] # (3) and (4)
    exon_id <- exon_id[autosomes] # (3)
    gene_id <- gene_id[autosomes] # (3)
    colnames(exons) <- paste0(cd$bio,":",cd$id)
    exons <- t(exons)
    
    save(list=c("exons","exon_id","gene_id"),file=file.path(path,"BBMRI.exons.RData"))
}


Rauschenberger's avatar
Rauschenberger committed
336
337
338
339
340
#' @export
#' @title
#' Prepare data matrices
#' 
#' @description
Rauschenberger's avatar
Rauschenberger committed
341
342
#' This function removes duplicate samples from each matrix,
#' only retains samples appearing in all matrices,
Rauschenberger's avatar
Rauschenberger committed
343
#' and brings samples into the same order.
Rauschenberger's avatar
Rauschenberger committed
344
#' 
Rauschenberger's avatar
Rauschenberger committed
345
#' @param ...
Rauschenberger's avatar
Rauschenberger committed
346
347
#' matrices with samples in the rows and variables in the columns,
#' with sample identifiers as rows names
Rauschenberger's avatar
Rauschenberger committed
348
#' 
Rauschenberger's avatar
Rauschenberger committed
349
350
#' @param message
#' display messages\strong{:} logical
Rauschenberger's avatar
Rauschenberger committed
351
352
#' 
#' @examples
Rauschenberger's avatar
Rauschenberger committed
353
354
355
#' X <- matrix(rnorm(6),nrow=3,ncol=2,dimnames=list(c("A","B","C")))
#' Z <- matrix(rnorm(9),nrow=3,ncol=3,dimnames=list(c("B","A","B")))
#' match.samples(X,Z)
Rauschenberger's avatar
Rauschenberger committed
356
#' 
Rauschenberger's avatar
Rauschenberger committed
357
match.samples <- function(...,message=TRUE){
Rauschenberger's avatar
Rauschenberger committed
358
    
Rauschenberger's avatar
Rauschenberger committed
359
360
    list <- list(...)
    if(length(list)==1 & is.list(list[[1]])){list <- list[[1]]}
Rauschenberger's avatar
Rauschenberger committed
361
362
363
364
365
    if(is.null(names(list))){
        names(list) <- sapply(substitute(list(...))[-1],deparse)
    }
    names <- names(list)
    
Rauschenberger's avatar
Rauschenberger committed
366
    # check input
Rauschenberger's avatar
Rauschenberger committed
367
368
369
    cond <- sapply(list,function(x) !is.matrix(x))
    if(any(cond)){
        stop("Provide matrices!",call.=FALSE)
Rauschenberger's avatar
Rauschenberger committed
370
    }
Rauschenberger's avatar
Rauschenberger committed
371
372
373
    cond <- sapply(list,function(x) is.null(rownames(x)))
    if(any(cond)){
        stop("Provide row names!",call.=FALSE)
Rauschenberger's avatar
Rauschenberger committed
374
375
    }
    
Rauschenberger's avatar
Rauschenberger committed
376
    # remove duplicated samples
Rauschenberger's avatar
Rauschenberger committed
377
    duplic <- lapply(list,function(x) duplicated(rownames(x)))
Rauschenberger's avatar
Rauschenberger committed
378
    for(i in seq_along(list)){
Rauschenberger's avatar
Rauschenberger committed
379
380
381
        number <- round(100*mean(duplic[[i]]))
        if(message){message(number," duplicates in \"",names[i],"\"")}
        list[[i]] <- list[[i]][!duplic[[i]],,drop=FALSE]
Rauschenberger's avatar
Rauschenberger committed
382
    }
Rauschenberger's avatar
Rauschenberger committed
383
384
    
    # retain overlapping samples
Rauschenberger's avatar
Rauschenberger committed
385
386
387
    all <- Reduce(f=intersect,x=lapply(list,rownames))
    for(i in seq_along(list)){
        percent <- round(100*mean(rownames(list[[i]]) %in% all))
Rauschenberger's avatar
Rauschenberger committed
388
        if(message){message(percent,"% overlap in \"",names[i],"\"")}
Rauschenberger's avatar
Rauschenberger committed
389
        list[[i]] <- list[[i]][all,,drop=FALSE]
Rauschenberger's avatar
Rauschenberger committed
390
    }
Rauschenberger's avatar
Rauschenberger committed
391
392
    
    # check output
Rauschenberger's avatar
Rauschenberger committed
393
394
    cond <- sapply(list,function(x) any(duplicated(rownames(x))))
    if(any(cond)){
Rauschenberger's avatar
Rauschenberger committed
395
396
        stop("Duplicate samples!",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
397
398
    cond <- sapply(list,function(x) nrow(x)!=nrow(list[[1]]))
    if(any(cond)){
Rauschenberger's avatar
Rauschenberger committed
399
400
        stop("Different sample sizes!",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
401
402
    cond <- sapply(list,function(x) any(rownames(x)!=rownames(list[[1]])))
    if(any(cond)){
Rauschenberger's avatar
Rauschenberger committed
403
404
405
        stop("Different sample names!",call.=FALSE)
    }
    
Rauschenberger's avatar
Rauschenberger committed
406
    return(list)
Rauschenberger's avatar
Rauschenberger committed
407
408
409
410
411
412
413
}

#' @export
#' @title
#' Adjust library sizes
#' 
#' @description
Rauschenberger's avatar
Rauschenberger committed
414
#' This function adjusts RNA-seq expression data for different library sizes.
Rauschenberger's avatar
Rauschenberger committed
415
#' 
Rauschenberger's avatar
Rauschenberger committed
416
417
#' @param x
#' matrix with \eqn{n} rows (samples) and \eqn{p} columns (variables)
Rauschenberger's avatar
Rauschenberger committed
418
419
#' 
#' @examples
Rauschenberger's avatar
Rauschenberger committed
420
421
422
423
#' n <- 5; p <- 10
#' x <- matrix(rnbinom(n=n*p,mu=5,size=1/0.5),nrow=n,ncol=p)
#' x[1,] <- 10*x[1,]
#' adjust.samples(x)
Rauschenberger's avatar
Rauschenberger committed
424
#' 
Rauschenberger's avatar
Rauschenberger committed
425
adjust.samples <- function(x){
Rauschenberger's avatar
Rauschenberger committed
426
427
428
429
430
431
    if(!is.matrix(x)){
        stop("no matrix argument",call.=FALSE)
    }
    if(!is.numeric(x)){
        stop("no numeric argument",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
432
    if(!is.integer(x)&&any(round(x)!=x)){
Rauschenberger's avatar
Rauschenberger committed
433
        warning("non-integer values",call.=FALSE)
Rauschenberger's avatar
Rauschenberger committed
434
    }
Rauschenberger's avatar
Rauschenberger committed
435
    if(any(x<0)){
Rauschenberger's avatar
Rauschenberger committed
436
        warning("negative values",call.=FALSE)
Rauschenberger's avatar
Rauschenberger committed
437
    }
Rauschenberger's avatar
Rauschenberger committed
438
439
440
    n <- nrow(x); p <- ncol(x)
    lib.size <- rowSums(x)
    norm.factors <- edgeR::calcNormFactors(object=t(x),lib.size=lib.size)
Rauschenberger's avatar
Rauschenberger committed
441
    gamma <- norm.factors*lib.size/mean(lib.size)
Rauschenberger's avatar
Rauschenberger committed
442
    gamma <- matrix(gamma,nrow=n,ncol=p,byrow=FALSE)
Rauschenberger's avatar
Rauschenberger committed
443
444
    x <- x/gamma
    return(x)
Rauschenberger's avatar
Rauschenberger committed
445
446
447
448
449
450
451
452
453
}

#' @export
#' @title
#' Adjust exon length
#' 
#' @description
#' This function adjusts exon expression data for different exon lengths.
#' 
Rauschenberger's avatar
Rauschenberger committed
454
#' @param x
Rauschenberger's avatar
Rauschenberger committed
455
456
#' matrix with \eqn{n} rows (samples) and \eqn{p} columns (exons)
#' 
Rauschenberger's avatar
Rauschenberger committed
457
#' @param offset
Rauschenberger's avatar
Rauschenberger committed
458
#' exon length\strong{:} vector of length \eqn{p}
Rauschenberger's avatar
Rauschenberger committed
459
#' 
Rauschenberger's avatar
Rauschenberger committed
460
461
#' @param group
#' gene names\strong{:} vector of length \eqn{p}
Rauschenberger's avatar
Rauschenberger committed
462
463
464
465
#' 
#' @examples
#' NA
#' 
Rauschenberger's avatar
Rauschenberger committed
466
adjust.covariates <- function(x,offset,group){
Rauschenberger's avatar
Rauschenberger committed
467
468
469
    if(!is.numeric(x)|!is.matrix(x)){
        stop("Argument \"x\" is no numeric matrix.",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
470
471
472
473
474
    if(!is.numeric(offset)|!is.vector(offset)){
        stop("Argument \"offset\" is no numeric vector.",call.=FALSE)
    }
    if(any(offset<0)){
        stop("Argument \"offset\" takes negative values",call.=FALSE)   
Rauschenberger's avatar
Rauschenberger committed
475
    }
Rauschenberger's avatar
Rauschenberger committed
476
477
478
479
    if(!is.character(group)|!is.vector(group)){
        stop("Argument \"group\" is no character vector.",call.=FALSE)
    }
    if(ncol(x)!=length(group)|ncol(x)!=length(offset)){
Rauschenberger's avatar
Rauschenberger committed
480
481
        stop("Contradictory dimensions.",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
482
483
    n <- nrow(x); p <- ncol(x); names <- dimnames(x)
    x <- as.numeric(x)
Rauschenberger's avatar
Rauschenberger committed
484
    offset <- rep(offset,each=n)
Rauschenberger's avatar
Rauschenberger committed
485
486
487
    group <- strsplit(group,split=",")
    group <- sapply(group,function(x) x[[1]][1])
    group <- rep(group,each=n)
Rauschenberger's avatar
Rauschenberger committed
488
    lmer <- lme4::lmer(x ~ offset + (1|group)); gc()
Rauschenberger's avatar
Rauschenberger committed
489
490
491
    x <- matrix(stats::residuals(lmer),nrow=n,ncol=p,dimnames=names)
    x <- x-min(x)
    return(x)
Rauschenberger's avatar
Rauschenberger committed
492
493
494
495
496
497
498
}

#' @export
#' @title
#' Search for genes
#' 
#' @description
Rauschenberger's avatar
Rauschenberger committed
499
#' This function retrieves all protein-coding genes on a chromosome.
Rauschenberger's avatar
Rauschenberger committed
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
#' 
#' @param chr
#' chromosome\strong{:} integer 1-22
#' 
#' @param path
#' path to gene transfer format files (.gtf)
#' 
#' @param release
#' character "NCBI36", "GRCh37", or "GRCh38"
#' 
#' @param build
#' integer 49-91
#' 
#' @examples
#' NA
#' 
Rauschenberger's avatar
Rauschenberger committed
516
517
518
map.genes <- function(chr,path=getwd(),release="GRCh37",build=71){
    
    # check input
Rauschenberger's avatar
Rauschenberger committed
519
    if(!chr %in% 1:22){
Rauschenberger's avatar
Rauschenberger committed
520
521
522
523
524
525
526
527
528
        stop("Invalid argument \"chr\".",call.=FALSE)
    }
    if(!release %in% c("NCBI36","GRCh37","GRCh38")){
        stop("Invalid argument \"release\".",call.=FALSE)
    }
    if(!build %in% 49:91){
        stop("Invalid argument \"build\".",call.=FALSE)
    }
    
Rauschenberger's avatar
Rauschenberger committed
529
530
    file <- paste0("Homo_sapiens.",release,".",build,".gtf")
    if(!file.exists(file.path(path,file))){
Rauschenberger's avatar
Rauschenberger committed
531
532
533
534
535
536
537
538
539
        url <- paste0("ftp://ftp.ensembl.org/pub/release-",build,
                      "/gtf/homo_sapiens/",file,".gz")
        destfile <- file.path(path,paste0(file,".gz"))
        utils::download.file(url=url,destfile=destfile,method="auto")
        R.utils::gunzip(filename=destfile,remove=FALSE,overwrite=TRUE)
    }
    object <- refGenome::ensemblGenome()
    refGenome::basedir(object) <- path
    refGenome::read.gtf(object,filename=file)
Rauschenberger's avatar
Rauschenberger committed
540
541
542
543
544
545
    x <- refGenome::getGenePositions(object=object,by="gene_id")
    x <- x[x$seqid==chr & x$gene_biotype=="protein_coding",]
    x <- x[,c("gene_id","seqid","start","end")]
    rownames(x) <- NULL
    colnames(x)[colnames(x)=="seqid"] <- "chr"
    return(x)
Rauschenberger's avatar
Rauschenberger committed
546
547
548
549
550
551
552
553
554
}

#' @export
#' @title
#' Search for exons
#' 
#' @description
#' This function
#' 
Rauschenberger's avatar
Rauschenberger committed
555
#' @param gene
Rauschenberger's avatar
Rauschenberger committed
556
557
#' gene names\strong{:} vector with one entry per gene,
#' including the gene names
Rauschenberger's avatar
Rauschenberger committed
558
#' 
Rauschenberger's avatar
Rauschenberger committed
559
#' @param exon
Rauschenberger's avatar
Rauschenberger committed
560
561
562
#' exon names\strong{:} vector with one entry per exon,
#' including the corresponding \emph{gene} names
#' (separated by comma if multiple gene names)
Rauschenberger's avatar
Rauschenberger committed
563
564
565
566
567
568
#' 
#' @details
#' The exon names should contain the gene names. For each gene, this function
#' returns the indices of the exons.
#' 
#' @examples
Rauschenberger's avatar
Rauschenberger committed
569
570
571
#' gene <- c("A","B","C")
#' exon <- c("A","A,B","B","B,C","C")
#' map.exons(gene,exon)
Rauschenberger's avatar
Rauschenberger committed
572
#'
Rauschenberger's avatar
Rauschenberger committed
573
574
575
map.exons <- function(gene,exon){
    p <- length(gene)
    x <- list()
Rauschenberger's avatar
Rauschenberger committed
576
577
578
    pb <- utils::txtProgressBar(min=0,max=p,style=3)
    for(i in seq_len(p)){
        utils::setTxtProgressBar(pb=pb,value=i)
Rauschenberger's avatar
Rauschenberger committed
579
580
        which <- as.integer(grep(pattern=gene[i],x=exon))
        x[[i]] <- which
Rauschenberger's avatar
Rauschenberger committed
581
    }
Rauschenberger's avatar
Rauschenberger committed
582
583
584
    close(con=pb)
    names(x) <- gene
    return(x)
Rauschenberger's avatar
Rauschenberger committed
585
586
}

Rauschenberger's avatar
Rauschenberger committed
587

Rauschenberger's avatar
Rauschenberger committed
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
#' @export
#' @title
#' Search for SNPs
#' 
#' @description
#' This function
#' 
#' @param gene.chr
#' chromosome\strong{:}
#' numeric vector with one entry per gene
#' 
#' @param gene.start
#' start position\strong{:}
#' numeric vector with one entry per gene
#' 
#' @param gene.end
#' end position\strong{:}
#' numeric vector with one entry per gene
#' 
#' @param snp.chr
#' integer 1-22
#' 
#' @param snp.pos
#' chromosomal position of SNPs\strong{:}
#' numeric vector with one entry per SNP
#' 
Rauschenberger's avatar
Rauschenberger committed
614
615
616
617
#' @param dist
#' number of base pairs before start position\strong{:}
#' integer
#' 
Rauschenberger's avatar
Rauschenberger committed
618
#' @examples
Rauschenberger's avatar
Rauschenberger committed
619
620
621
#' gene.chr <- rep(1,times=5)
#' gene.start <- 1:5
#' gene.end <- 2:6
Rauschenberger's avatar
Rauschenberger committed
622
#'
Rauschenberger's avatar
Rauschenberger committed
623
624
625
626
627
628
#' snp.chr <- rep(1,times=100)
#' snp.pos <- seq(from=1,to=4.9,length.out=100)
#' 
#' map.snps(gene.chr,gene.start,gene.end,snp.chr,snp.pos,dist=0)
#'
map.snps <- function(gene.chr,gene.start,gene.end,snp.chr,snp.pos,dist=10^3){
Rauschenberger's avatar
Rauschenberger committed
629
630
631
    if(length(gene.chr)!=length(gene.start)|length(gene.chr)!=length(gene.end)){
        stop("Invalid.",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
632
633
634
    if(!is.numeric(snp.chr)|!is.numeric(snp.pos)){
        stop("Invalid.",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
635
    p <- length(gene.start)
Rauschenberger's avatar
Rauschenberger committed
636
    x <- data.frame(from=integer(length=p),to=integer(length=p))
Rauschenberger's avatar
Rauschenberger committed
637
638
639
640
641
    pb <- utils::txtProgressBar(min=0,max=p,style=3)
    for(i in seq_len(p)){ # 
        utils::setTxtProgressBar(pb=pb,value=i)
        chr <- snp.chr == gene.chr[i]
        if(!any(chr)){next}
Rauschenberger's avatar
Rauschenberger committed
642
        start <- snp.pos >= (gene.start[i] - dist)
Rauschenberger's avatar
Rauschenberger committed
643
644
645
        end <- snp.pos <= gene.end[i] + 0
        which <- as.integer(which(chr & start & end))
        if(length(which)==0){next}
Rauschenberger's avatar
Rauschenberger committed
646
647
        x$from[i] <- min(which)
        x$to[i] <- max(which)
Rauschenberger's avatar
Rauschenberger committed
648
649
650
        if(length(which)==1){next}
        if(!all(diff(which)==1)){stop("SNPs are in wrong order!")}
    }
Rauschenberger's avatar
Rauschenberger committed
651
652
    close(con=pb)
    return(x)
Rauschenberger's avatar
Rauschenberger committed
653
654
}

Rauschenberger's avatar
Rauschenberger committed
655

Rauschenberger's avatar
Rauschenberger committed
656
657
#' @export
#' @title
Rauschenberger's avatar
Rauschenberger committed
658
#' Drop trivial tests
Rauschenberger's avatar
Rauschenberger committed
659
660
661
662
663
664
#' 
#' @description
#' This function
#' 
#' @param map
#' list with names "genes", "exons", and "snps"
Rauschenberger's avatar
Rauschenberger committed
665
666
#' (output from \code{\link{map.genes}}, \code{\link{map.exons}},
#' and \code{\link{map.snps}})
Rauschenberger's avatar
Rauschenberger committed
667
668
#' 
#' @details
Rauschenberger's avatar
Rauschenberger committed
669
#' This functions drops tests for genes without SNPs or with a single exon.
Rauschenberger's avatar
Rauschenberger committed
670
671
672
673
#' 
#' @examples
#' NA
#' 
Rauschenberger's avatar
Rauschenberger committed
674
drop.trivial <- function(map){
Rauschenberger's avatar
Rauschenberger committed
675
676
677
678
679
680
681
682
683
684
685
    
    # check input
    if(length(map)!=3){
        stop("Unexpected argument length.",call.=FALSE)
    }
    if(any(names(map)!=c("genes","exons","snps"))){
        stop("Unexpected argument names.",call.=FALSE)
    }
    
    # search
    p <- nrow(map$genes)
Rauschenberger's avatar
Rauschenberger committed
686
687
    pass <- rep(NA,times=p)
    pb <- utils::txtProgressBar(min=0,max=p,style=3)
Rauschenberger's avatar
Rauschenberger committed
688
    for(i in seq_len(p)){
Rauschenberger's avatar
Rauschenberger committed
689
690
691
692
693
694
695
696
697
698
        utils::setTxtProgressBar(pb=pb,value=i)
        ys <- map$exons[[i]]
        check <- logical()
        # Exclude genes without SNPs:
        check[1] <- map$snps$from[i] > 0
        check[2] <- map$snps$to[i] > 0
        # Exclude genes with single exon:
        check[3] <- length(ys) > 1
        pass[i] <- all(check)
    }
Rauschenberger's avatar
Rauschenberger committed
699
    close(con=pb)
Rauschenberger's avatar
Rauschenberger committed
700
701
702
703
704
705
706
707
708
    
    # check output
    if(any(pass[map$snps$to==0 & map$snps$from==0])){
        stop("Genes without any SNPs.",call.=FALSE)
    }
    if(any(pass[sapply(map$exons,length)<2])){
        stop("Genes without multiple exons.",call.=FALSE)
    }
    
Rauschenberger's avatar
Rauschenberger committed
709
710
711
712
    map$genes <- map$genes[pass,]
    map$exons <- map$exons[pass]
    map$snps <- map$snps[pass,]
    return(map)
Rauschenberger's avatar
Rauschenberger committed
713
714
715
716
717
}


#' @export
#' @title
Rauschenberger's avatar
Rauschenberger committed
718
#' Conduct single test
Rauschenberger's avatar
Rauschenberger committed
719
720
#' 
#' @description
Rauschenberger's avatar
Rauschenberger committed
721
#' This function tests for alternative splicing.
Rauschenberger's avatar
Rauschenberger committed
722
723
724
725
726
727
728
729
730
731
732
#' 
#' @param Y
#' exon expression\strong{:}
#' matrix with \eqn{n} rows (samples) and \eqn{p} columns (exons)
#' 
#' @param X
#' SNP genotype\strong{:}
#' matrix with \eqn{n} rows (samples) and \eqn{q} columns (SNPs)
#' 
#' @param map
#' list with names "genes", "exons", and "snps"
Rauschenberger's avatar
Rauschenberger committed
733
734
#' (output from \code{\link{map.genes}}, \code{\link{map.exons}},
#' and \code{\link{map.snps}})
Rauschenberger's avatar
Rauschenberger committed
735
736
737
738
739
740
741
742
743
744
745
746
#' 
#' @param i
#' gene index\strong{:}
#' integer between \eqn{1} and \code{nrow(map$genes)}
#' 
#' @param limit
#' cutoff for rounding \code{p}-values
#' 
#' @param steps
#' size of permutation chunks\strong{:}
#' integer vector
#' 
Rauschenberger's avatar
Rauschenberger committed
747
748
749
#' @param rho
#' correlation\strong{:}
#' numeric vector with values between \eqn{0} and \eqn{1}
Rauschenberger's avatar
Rauschenberger committed
750
#' 
Rauschenberger's avatar
Rauschenberger committed
751
752
753
754
755
756
757
758
#' @details
#' The maximum number of permutations equals \code{sum(steps)}. Permutations is
#' interrupted if at least \code{limit} test statistics for the permuted data
#' are larger than the test statistic for the observed data.
#' 
#' @examples
#' NA
#' 
Rauschenberger's avatar
Rauschenberger committed
759
test.single <- function(Y,X,map,i,limit=NULL,steps=NULL,rho=c(0,0.5,1)){
Rauschenberger's avatar
Rauschenberger committed
760
761
762
    
    if(is.null(limit)){limit <- 5}
    if(is.null(steps)){steps <- c(10,20,20,50)}
Rauschenberger's avatar
Rauschenberger committed
763
    
Rauschenberger's avatar
Rauschenberger committed
764
    # check input
Rauschenberger's avatar
Rauschenberger committed
765
766
767
768
769
770
    if(!is.numeric(limit)){
        stop("Argument \"limit\" is not numeric.",call.=FALSE)
    }
    if(limit<1){
        stop("Argument \"limit\" is below one.",call.=FALSE)
    }
Rauschenberger's avatar
Rauschenberger committed
771
    if(!is.numeric(steps)|!is.vector(steps)){
Rauschenberger's avatar
Rauschenberger committed
772
773
774
775
776
777
        stop("Argument \"steps\" is no numeric vector.",call.=FALSE)
    }
    if(sum(steps)<2){
        stop("Too few permutations \"sum(steps)\".",call.=FALSE)
    }
    
Rauschenberger's avatar
Rauschenberger committed
778
    # extract data
Rauschenberger's avatar
Rauschenberger committed
779
    ys <- map$exons[[i]]
Rauschenberger's avatar
Rauschenberger committed
780
    y <- Y[,ys,drop=FALSE]
Rauschenberger's avatar
Rauschenberger committed
781
    xs <- seq(from=map$snps$from[i],to=map$snps$to[i],by=1)
Rauschenberger's avatar
Rauschenberger committed
782
783
    x <- X[,xs,drop=FALSE]
    rm(Y,X); silent <- gc()
Rauschenberger's avatar
Rauschenberger committed
784
    
Rauschenberger's avatar
Rauschenberger committed
785
    # test effects
Rauschenberger's avatar
Rauschenberger committed
786
787
    pvalue <- rep(x=NA,times=length(rho))
    for(j in seq_along(rho)){
Rauschenberger's avatar
Rauschenberger committed
788
789
        tstat <- spliceQTL:::G2.multin(
            dep.data=y,indep.data=x,nperm=steps[1]-1,rho=rho[j])$Sg
Rauschenberger's avatar
Rauschenberger committed
790
        for(nperm in steps[-1]){
Rauschenberger's avatar
Rauschenberger committed
791
792
            tstat <- c(tstat,spliceQTL:::G2.multin(
                dep.data=y,indep.data=x,nperm=nperm,rho=rho[j])$Sg[-1])
Rauschenberger's avatar
Rauschenberger committed
793
            if(sum(tstat >= tstat[1]) >= limit){break}
Rauschenberger's avatar
Rauschenberger committed
794
        }
Rauschenberger's avatar
Rauschenberger committed
795
        pvalue[j] <- mean(tstat >= tstat[1],na.rm=TRUE)
Rauschenberger's avatar
Rauschenberger committed
796
    }
Rauschenberger's avatar
Rauschenberger committed
797

Rauschenberger's avatar
Rauschenberger committed
798
799
800
801
    return(pvalue)
}


Rauschenberger's avatar
Rauschenberger committed
802
803
#' @export
#' @title
Rauschenberger's avatar
Rauschenberger committed
804
#' Conduct multiple tests
Rauschenberger's avatar
Rauschenberger committed
805
806
#' 
#' @description
Rauschenberger's avatar
Rauschenberger committed
807
#' This function tests for alternative splicing.
Rauschenberger's avatar
Rauschenberger committed
808
809
810
811
812
813
814
815
816
#' 
#' @param Y
#' exon expression\strong{:}
#' matrix with \eqn{n} rows (samples) and \eqn{p} columns (exons)
#' 
#' @param X
#' SNP genotype\strong{:}
#' matrix with \eqn{n} rows (samples) and \eqn{q} columns (SNPs)
#' 
Rauschenberger's avatar
Rauschenberger committed
817
818
819
820
#' @param map
#' list with names "genes", "exons", and "snps"
#' (output from \code{map.genes}, \code{map.exons}, and \code{map.snps})
#' 
Rauschenberger's avatar
Rauschenberger committed
821
822
823
824
#' @param rho
#' correlation\strong{:}
#' numeric vector with values between \eqn{0} and \eqn{1}
#' 
Rauschenberger's avatar
Rauschenberger committed
825
826
827
828
#' @param spec
#' number of cores\strong{:}
#' positive integer
#' 
Rauschenberger's avatar
Rauschenberger committed
829
830
831
832
#' @param steps
#' number of iteration chunks\strong{:}
#' positive integer
#' 
Rauschenberger's avatar
Rauschenberger committed
833
834
835
836
837
838
839
#' @details
#' Automatic adjustment of the number of permutations
#' such that Bonferroni-significant p-values are possible.
#' 
#' @examples
#' NA
#' 
Rauschenberger's avatar
Rauschenberger committed
840
test.multiple <- function(Y,X,map,rho=c(0,0.5,1),spec=1,steps=20){
Rauschenberger's avatar
Rauschenberger committed
841
    
Rauschenberger's avatar
Rauschenberger committed
842
843
844
    p <- nrow(map$genes)
    
    # permutations
Rauschenberger's avatar
Rauschenberger committed
845
    if(FALSE){ # old
Rauschenberger's avatar
Rauschenberger committed
846
847
848
849
850
851
852
853
854
        min <- 5
        max <- p/0.05+1
        limit <- ceiling(0.05*max/p)
        base <- 1.5 # adjust sequence
        from <- log(min,base=base)
        to <- log(max,base=base)
        steps <- c(min,diff(unique(round(base^(seq(from=from,to=to,length.out=20))))))
    }
    
Rauschenberger's avatar
Rauschenberger committed
855
    if(FALSE){ # new
Rauschenberger's avatar
Rauschenberger committed
856
857
        max <- p/0.05+1
        limit <- ceiling(0.05*max/p)
Rauschenberger's avatar
Rauschenberger committed
858
        steps <- diff(limit^seq(from=1,to=log(max)/log(limit),length.out=pmin(p,steps))) # was (p,20)
Rauschenberger's avatar
Rauschenberger committed
859
        steps <- c(limit,round(steps)) # Or replace "limit" by "minimum # of permutations"!
Rauschenberger's avatar
Rauschenberger committed
860
        steps[steps==0] <- 1
Rauschenberger's avatar
Rauschenberger committed
861
862
        steps[length(steps)] <- max-sum(steps[-length(steps)])
    }
Rauschenberger's avatar
Rauschenberger committed
863
    
Rauschenberger's avatar
Rauschenberger committed
864
865
866
867
    if(TRUE){ # temporary trial, delete this!
        max <- limit <- steps <- 100
    }
    
Rauschenberger's avatar
Rauschenberger committed
868
    if(max != sum(steps)){stop("Invalid combination?",call.=FALSE)}
Rauschenberger's avatar
Rauschenberger committed
869
    
Rauschenberger's avatar
Rauschenberger committed
870
871
872
873
874
875
    if(spec==1){
        pvalue <- lapply(X=seq_len(p),FUN=function(i) spliceQTL::test.single(Y=Y,X=X,map=map,i=i,limit=limit,steps=steps,rho=rho))
    } else {
        type <- ifelse(test=.Platform$OS.type=="windows",yes="PSOCK",no="FORK")
        cluster <- parallel::makeCluster(spec=spec,type=type)
        parallel::clusterSetRNGStream(cl=cluster,iseed=1)
Rauschenberger's avatar
Rauschenberger committed
876
877
        #parallel::clusterExport(cl=cluster,varlist=c("Y","X","map","limit","steps","rho"),envir=environment())
        #parallel::clusterEvalQ(cl=cluster,library(spliceQTL,lib.loc="/virdir/Scratch/arauschenberger/library"))
Rauschenberger's avatar
Rauschenberger committed
878
879
880
        pvalue <- parallel::parLapply(cl=cluster,X=seq_len(p),fun=function(i) test.single(Y=Y,X=X,map=map,i=i,limit=limit,steps=steps,rho=rho))
        #pvalue <- parallel::parLapply(cl=cluster,X=seq_len(p),fun=function(i) test.trial(y=Y[,map$exons[[i]],drop=FALSE],x=X[,seq(from=map$snps$from[i],to=map$snps$to[i],by=1),drop=FALSE],limit=limit,steps=steps,rho=rho))
        parallel::stopCluster(cluster)
Rauschenberger's avatar
Rauschenberger committed
881
        rm(cluster)
Rauschenberger's avatar
Rauschenberger committed
882
    }
Rauschenberger's avatar
Rauschenberger committed
883
    
Rauschenberger's avatar
Rauschenberger committed
884
    # tyding up
Rauschenberger's avatar
Rauschenberger committed
885
    pvalue <- do.call(what=rbind,args=pvalue)
Rauschenberger's avatar
Rauschenberger committed
886
    colnames(pvalue) <- paste0("rho=",rho)
Rauschenberger's avatar
Rauschenberger committed
887
888
    rownames(pvalue) <- map$genes$gene_id
    
Rauschenberger's avatar
Rauschenberger committed
889
    return(pvalue)
Rauschenberger's avatar
Rauschenberger committed
890
891
892
}


Rauschenberger's avatar
Rauschenberger committed
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
# test.trial <- function(y,x,limit=NULL,steps=NULL,rho=c(0,0.5,1)){
#     
#     if(is.null(limit)){limit <- 5}
#     if(is.null(steps)){steps <- c(10,20,20,50)}
#     
#     # check input
#     if(!is.numeric(limit)){
#         stop("Argument \"limit\" is not numeric.",call.=FALSE)
#     }
#     if(limit<1){
#         stop("Argument \"limit\" is below one.",call.=FALSE)
#     }
#     if(!is.numeric(steps)|!is.vector(steps)){
#         stop("Argument \"steps\" is no numeric vector.",call.=FALSE)
#     }
#     if(sum(steps)<2){
#         stop("Too few permutations \"sum(steps)\".",call.=FALSE)
#     }
#     
#     # test effects
#     pvalue <- rep(x=NA,times=length(rho))
#     for(j in seq_along(rho)){
#         tstat <- spliceQTL:::G2.multin(
#             dep.data=y,indep.data=x,nperm=steps[1]-1,rho=rho[j])$Sg
#         for(nperm in steps[-1]){
#             tstat <- c(tstat,spliceQTL:::G2.multin(
#                 dep.data=y,indep.data=x,nperm=nperm,rho=rho[j])$Sg[-1])
#             if(sum(tstat >= tstat[1]) >= limit){break}
#         }
#         pvalue[j] <- mean(tstat >= tstat[1],na.rm=TRUE)
#     }
#     
#     return(pvalue)
# }

Rauschenberger's avatar
Rauschenberger committed
928

Rauschenberger's avatar
Rauschenberger committed
929
930
931
932
933
934
935
936
937
938
939
940
941
#--- spliceQTL test functions --------------------------------------------------

# Function: G2.multin
# This is to compute the G2 test statistic under the assumption that the response follows a multinomial distribution
### Input 
### dep data and indep data with samples on the rows and genes on the columns
### grouping: Either a logical value = F or a matrix with a single column and same number of rows as samples. 
###         Column name should be defined.
###         Contains clinical information of the samples. 
###         Should have two groups only. 
### nperm : number of permutations 
### rho: the null correlation between SNPs
### mu: the null correlation between observations corresponding to different exons and different individuals
Rauschenberger's avatar
Rauschenberger committed
942
#
Rauschenberger's avatar
Rauschenberger committed
943
944
### Output
### A list containing G2 p.values and G2 test statistics
Rauschenberger's avatar
Rauschenberger committed
945
#
Rauschenberger's avatar
Rauschenberger committed
946
947
948
### Example : G2T = G2(dep.data = cgh, indep.data = expr, grouping=F, stand=TRUE, nperm=1000)
### G2 p.values : G2T$G2p
### G2 TS : G2T$$Sg
Rauschenberger's avatar
Rauschenberger committed
949
#
Rauschenberger's avatar
Rauschenberger committed
950
G2.multin <- function(dep.data,indep.data,stand=TRUE,nperm=100,grouping=F,rho=0,mu=0){
Rauschenberger's avatar
Rauschenberger committed
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
    
    nperm = nperm
    ## check for the number of samples in dep and indep data
    
    
    if (nrow(dep.data)!=nrow(indep.data)){
        cat("number of samples not same in dep and indep data","\n")
    }
    
    if(any(abs(rho)>1)){
        cat("correlations rho larger than abs(1) are not allowed")
    }
    
    nresponses <- ncol(dep.data)
    ncovariates <- ncol(indep.data)
    ### centering and standardizing the data are not done in this case
    
    #  dep.data = scale(dep.data,center=T,scale=stand)
    #  indep.data = scale(indep.data,center=T,scale=stand)
    
    #### No  grouping of the samples.
    
    ## Calculate U=(I-H)Y and UU', where Y has observations on rows; also tau.mat=X*W.rho*X', 
    ##   where X has observations on rows and variables on columns
    ##  and W.rho = I + rho*(J-I), a square matrix with as many rows as columns in X
    ## NOTE: this formulation uses X with n obs on the rows and m covariates no the columns, so it is the transpose of the first calculations
    nsamples <- nrow(dep.data)
    n.persample <- rowSums(dep.data)
    n.all <- sum(dep.data)
    H <- (1/n.all)*matrix( rep(n.persample,each=nsamples),nrow=nsamples,byrow=T)
    U <- (diag(rep(1,nsamples)) - H) %*% dep.data
    ## Now we may have a vector of values for rho - so we define tau.mat as an array, with the 3rd index corresponding to the value of rho
    tau.mat <- array(0,dim=c(nsamples,nsamples,length(rho)))
    for(xk in 1:length(rho))  
    {  
        if (rho[xk]==0) { tau.mat[,,xk] <- tcrossprod(indep.data) } 
        else { w.rho <- diag(rep(1,ncovariates)) + rho[xk]*(tcrossprod(rep(1,ncovariates)) -diag(rep(1,ncovariates))  )
        tau.mat[,,xk] <- indep.data %*% w.rho %*% t(indep.data)}
        
    }
    ######################################
    ### NOTES ARMIN START ################
    # all(X %*% t(X) == tau.mat[,,1]) # rho = 0 -> TRUE
    # all(X %*% (t(X) %*% X) %*% t(X) == tau.mat[,,1]) # rho = 1
    # plot(as.numeric(X %*% (t(X) %*% X) %*% t(X)),as.numeric(tau.mat[,,1]))
    ### NOTES ARMIN END ##################
    ######################################
    samp_names = 1:nsamples ## this was rownames(indep.data), but I now do this so that rownames do not have to be added to the array tau.mat
    Sg = get.g2stat.multin(U,mu=mu,rho=rho,tau.mat)
    ### now we will have a vector as result, with one value per combination of values of rho and mu
    #
    ### G2 
    ### Permutations
    # When using permutations: only the rows of tau.mat are permuted
    # To check how the permutations can be efficiently applied, see tests_permutation_g2_multin.R
    
    
    perm_samp = matrix(0, nrow=nrow(indep.data), ncol=nperm)   ## generate the permutation matrix
    for(i in 1:ncol(perm_samp)){
        perm_samp[,i] = samp_names[sample(1:length(samp_names),length(samp_names))]
    }
    
    ## permutation starts - recompute tau.mat  (or recompute U each time)
    for (perm in 1:nperm){
        tau.mat.perm = tau.mat[perm_samp[,perm],,,drop=FALSE]          # permute rows
        tau.mat.perm = tau.mat.perm[,perm_samp[,perm],,drop=FALSE]     # permute columns
        
Rauschenberger's avatar
Rauschenberger committed
1018
        Sg = c(Sg,spliceQTL:::get.g2stat.multin(U, mu=mu,rho=rho,tau.mat.perm) )
Rauschenberger's avatar
Rauschenberger committed
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
    }
    
    ########################################################################
    
    #### G2 test statistic
    # *** recompute for a vector of values for each case - just reformat the result with as many rows as permutations + 1,
    # and as many columns as combinations of values of rho and mu
    Sg = matrix(Sg,nrow=nperm+1,ncol=length(mu)*length(rho))
    colnames(Sg) <- paste(rep("rho",ncol(Sg)),rep(1:length(rho),each=length(mu)),rep("mu",ncol(Sg)),rep(1:length(mu),length(rho)) )
    
    ### Calculte G2 pval
Rauschenberger's avatar
Rauschenberger committed
1030
    G2p =  apply(Sg,2,spliceQTL:::get.pval.percol) 
Rauschenberger's avatar
Rauschenberger committed
1031
1032
1033
1034
    
    return (list(perm = perm_samp,G2p = G2p,Sg = Sg))
}

Rauschenberger's avatar
Rauschenberger committed
1035

Rauschenberger's avatar
Rauschenberger committed
1036
1037
1038
1039
1040
1041
1042
1043
# Function: get.g2stat.multin
# Computes the G2 test statistic given two data matrices, under a multinomial distribution
# This is used internally by the G2 function
# Inputs: 
#  U = (I-H)Y, a n*K matrix where n=number obs and K=number multinomial responses possible
#  tau.mat = X' W.rho X, a n*n matrix : both square, symmetric matrices with an equal number of rows
# Output: test statistic (single value)
# 
Rauschenberger's avatar
Rauschenberger committed
1044
get.g2stat.multin <- function(U, mu, rho, tau.mat){
Rauschenberger's avatar
Rauschenberger committed
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
    g2tstat <- NULL
    for(xk in 1:length(rho))
    {
        for(xj in 1:length(mu))
        {
            if(mu[xj]==0) { g2tstat <- c(g2tstat, sum( diag( tcrossprod(U) %*% tau.mat[,,xk] ) ) )
            } else {
                g2tstat <- c(g2tstat, (1-mu[xj])*sum(diag( tcrossprod(U) %*% tau.mat[,,xk] ) ) + mu[xj]*sum( t(U) %*% tau.mat[,,xk] %*% U )  )
            }
            
        }
    }
    g2tstat
}

Rauschenberger's avatar
Rauschenberger committed
1060

Rauschenberger's avatar
Rauschenberger committed
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
# Function: get.pval.percol
# This function takes a vector containing the observed test stat as the first entry, followed by values generated by permutation,
# and computed the estimated p-value
# Input
# x: a vector with length nperm+1
# Output
# the pvalue computed
get.pval.percol <- function(x){
    pval = mean(x[1]<= c(Inf , x[2:length(x)]))
    pval
}