mergeOutput := flags.Bool("merge-output", false, "merge output into one matrix.npy and one matrix.annotations.csv")
hgvsSingle := flags.Bool("single-hgvs-matrix", false, "also generate hgvs-based matrix")
hgvsChunked := flags.Bool("chunked-hgvs-matrix", false, "also generate hgvs-based matrix per chromosome")
- onehotChunked := flags.Bool("chunked-onehot", false, "generate one-hot tile-based matrix")
+ onehotSingle := flags.Bool("single-onehot", false, "generate one-hot tile-based matrix")
+ onehotChunked := flags.Bool("chunked-onehot", false, "generate one-hot tile-based matrix per input chunk")
flags.IntVar(&cmd.threads, "threads", 16, "number of memory-hungry assembly threads")
flags.StringVar(&cmd.chi2CaseControlFile, "chi2-case-control-file", "", "tsv file or directory indicating cases and controls for Χ² test (if directory, all .tsv files will be read)")
flags.StringVar(&cmd.chi2CaseControlColumn, "chi2-case-control-column", "", "name of case/control column in case-control files for Χ² test (value must be 0 for control, 1 for case)")
"-merge-output=" + fmt.Sprintf("%v", *mergeOutput),
"-single-hgvs-matrix=" + fmt.Sprintf("%v", *hgvsSingle),
"-chunked-hgvs-matrix=" + fmt.Sprintf("%v", *hgvsChunked),
+ "-single-onehot=" + fmt.Sprintf("%v", *onehotSingle),
"-chunked-onehot=" + fmt.Sprintf("%v", *onehotChunked),
"-chi2-case-control-file=" + cmd.chi2CaseControlFile,
"-chi2-case-control-column=" + cmd.chi2CaseControlColumn,
if *mergeOutput || *hgvsSingle {
toMerge = make([][]int16, len(infiles))
}
+ var onehotChunks [][][]int8
+ var onehotXrefs [][]onehotXref
+ if *onehotSingle {
+ onehotChunks = make([][][]int8, len(infiles))
+ onehotXrefs = make([][]onehotXref, len(infiles))
+ }
throttleMem := throttle{Max: cmd.threads} // TODO: estimate using mem and data size
throttleNumpyMem := throttle{Max: cmd.threads/2 + 1}
throttleCPU.Wait()
var onehotChunk [][]int8
- var onehotXrefs []onehotXref
+ var onehotXref []onehotXref
annotationsFilename := fmt.Sprintf("%s/matrix.%04d.annotations.csv", *outputDir, infileIdx)
log.Infof("%04d: writing %s", infileIdx, annotationsFilename)
maxv = v
}
}
- if *onehotChunked {
+ if *onehotChunked || *onehotSingle {
onehot, xrefs := cmd.tv2homhet(cgs, maxv, remap, tag, tagstart)
onehotChunk = append(onehotChunk, onehot...)
- onehotXrefs = append(onehotXrefs, xrefs...)
+ onehotXref = append(onehotXref, xrefs...)
}
fmt.Fprintf(annow, "%d,%d,%d,=,%s,%d,,,\n", tag, outcol, rt.variant, rt.seqname, rt.pos)
variants := seq[tag]
// transpose onehotChunk[col][row] to numpy[row*ncols+col]
rows := len(cmd.cgnames)
cols := len(onehotChunk)
- log.Infof("%04d: preparing onehot numpy (rows=%d, cols=%d, mem=%d)", infileIdx, rows, cols, rows*cols)
+ log.Infof("%04d: preparing onehot numpy (rows=%d, cols=%d, mem=%d)", infileIdx, len(cmd.cgnames), len(onehotChunk), len(cmd.cgnames)*len(onehotChunk))
throttleNumpyMem.Acquire()
- out := make([]int8, rows*cols)
- for row := range cmd.cgnames {
- out := out[row*cols:]
- for colnum, values := range onehotChunk {
- out[colnum] = values[row]
- }
- }
- seq = nil
- cgs = nil
- debug.FreeOSMemory()
- throttleNumpyMem.Release()
-
+ out := onehotcols2int8(onehotChunk)
fnm := fmt.Sprintf("%s/onehot.%04d.npy", *outputDir, infileIdx)
err = writeNumpyInt8(fnm, out, rows, cols)
if err != nil {
return err
}
-
fnm = fmt.Sprintf("%s/onehot-columns.%04d.npy", *outputDir, infileIdx)
- xcols := len(onehotXrefs)
- xdata := make([]int32, 4*xcols)
- for i, xref := range onehotXrefs {
- xdata[i] = int32(xref.tag)
- xdata[xcols+i] = int32(xref.variant)
- if xref.het {
- xdata[xcols*2+i] = 1
- }
- xdata[xcols*3+i] = int32(xref.pvalue * 1000000)
- }
- err = writeNumpyInt32(fnm, xdata, 4, xcols)
+ err = writeNumpyInt32(fnm, onehotXref2int32(onehotXref), 4, len(onehotXref))
if err != nil {
return err
}
+ debug.FreeOSMemory()
+ throttleNumpyMem.Release()
}
- if !*onehotChunked || *mergeOutput || *hgvsSingle {
+ if *onehotSingle {
+ onehotChunks[infileIdx] = onehotChunk
+ onehotXrefs[infileIdx] = onehotXref
+ }
+ if !(*onehotSingle || *onehotChunked) || *mergeOutput || *hgvsSingle {
log.Infof("%04d: preparing numpy", infileIdx)
throttleNumpyMem.Acquire()
rows := len(cmd.cgnames)
log.Infof("%04d: matrix fragment %d rows x %d cols", infileIdx, rows, cols)
toMerge[infileIdx] = out
}
- if !*mergeOutput && !*onehotChunked {
+ if !*mergeOutput && !*onehotChunked && !*onehotSingle {
fnm := fmt.Sprintf("%s/matrix.%04d.npy", *outputDir, infileIdx)
err = writeNumpyInt16(fnm, out, rows, cols)
if err != nil {
}
}
}
+ if *onehotSingle {
+ var onehot [][]int8
+ var xrefs []onehotXref
+ for i := range onehotChunks {
+ onehot = append(onehot, onehotChunks[i]...)
+ onehotChunks[i] = nil
+ xrefs = append(xrefs, onehotXrefs[i]...)
+ onehotXrefs[i] = nil
+ }
+ fnm := fmt.Sprintf("%s/onehot.npy", *outputDir)
+ err = writeNumpyInt8(fnm, onehotcols2int8(onehot), len(cmd.cgnames), len(onehot))
+ if err != nil {
+ return 1
+ }
+ fnm = fmt.Sprintf("%s/onehot-columns.npy", *outputDir)
+ err = writeNumpyInt32(fnm, onehotXref2int32(xrefs), 4, len(xrefs))
+ if err != nil {
+ return 1
+ }
+ }
return 0
}
}
return out
}
+
+// convert a []onehotXref with length N to a numpy-style []int32
+// matrix with N columns, one row per field of onehotXref struct.
+//
+// Hom/het row contains hom=0, het=1.
+//
+// P-value row contains 1000000x actual p-value.
+func onehotXref2int32(xrefs []onehotXref) []int32 {
+ xcols := len(xrefs)
+ xdata := make([]int32, 4*xcols)
+ for i, xref := range xrefs {
+ xdata[i] = int32(xref.tag)
+ xdata[xcols+i] = int32(xref.variant)
+ if xref.het {
+ xdata[xcols*2+i] = 1
+ }
+ xdata[xcols*3+i] = int32(xref.pvalue * 1000000)
+ }
+ return xdata
+}
+
+// transpose onehot data from in[col][row] to numpy-style
+// out[row*cols+col].
+func onehotcols2int8(in [][]int8) []int8 {
+ if len(in) == 0 {
+ return nil
+ }
+ cols := len(in)
+ rows := len(in[0])
+ out := make([]int8, rows*cols)
+ for row := 0; row < rows; row++ {
+ outrow := out[row*cols:]
+ for col, incol := range in {
+ outrow[col] = incol[row]
+ }
+ }
+ return out
+}
projects / lightning.git / blobdiff