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")
samplesFilename := flags.String("samples", "", "`samples.csv` file with training/validation and case/control groups (see 'lightning choose-samples')")
- onlyPCA := flags.Bool("pca", false, "generate pca matrix")
+ caseControlOnly := flags.Bool("case-control-only", false, "drop samples that are not in case/control groups")
+ onlyPCA := flags.Bool("pca", false, "run principal component analysis, write components to pca.npy and samples.csv")
pcaComponents := flags.Int("pca-components", 4, "number of PCA components")
maxPCATiles := flags.Int("max-pca-tiles", 0, "maximum tiles to use as PCA input (filter, then drop every 2nd colum pair until below max)")
debugTag := flags.Int("debug-tag", -1, "log debugging details about specified tag")
flags.IntVar(&cmd.threads, "threads", 16, "number of memory-hungry assembly threads, and number of VCPUs to request for arvados container")
- flags.Float64Var(&cmd.chi2PValue, "chi2-p-value", 1, "do Χ² test and omit columns with p-value above this threshold")
+ flags.Float64Var(&cmd.chi2PValue, "chi2-p-value", 1, "do Χ² test (or logistic regression if -samples file has PCA components) and omit columns with p-value above this threshold")
flags.BoolVar(&cmd.includeVariant1, "include-variant-1", false, "include most common variant when building one-hot matrix")
cmd.filter.Flags(flags)
err := flags.Parse(args)
return nil
} else if err != nil {
return err
+ } else if flags.NArg() > 0 {
+ return fmt.Errorf("errant command line arguments after parsed flags: %v", flags.Args())
}
if *pprof != "" {
"-single-onehot=" + fmt.Sprintf("%v", *onehotSingle),
"-chunked-onehot=" + fmt.Sprintf("%v", *onehotChunked),
"-samples=" + *samplesFilename,
+ "-case-control-only=" + fmt.Sprintf("%v", *caseControlOnly),
"-pca=" + fmt.Sprintf("%v", *onlyPCA),
"-pca-components=" + fmt.Sprintf("%d", *pcaComponents),
"-max-pca-tiles=" + fmt.Sprintf("%d", *maxPCATiles),
if err != nil {
return err
}
+ } else if *caseControlOnly {
+ return fmt.Errorf("-case-control-only does not make sense without -samples")
}
cmd.cgnames = nil
} else if len(cmd.cgnames) != len(cmd.samples) {
return fmt.Errorf("mismatched sample list: %d samples in library, %d in %s", len(cmd.cgnames), len(cmd.samples), *samplesFilename)
} else {
- cmd.trainingSetSize = 0
for i, name := range cmd.cgnames {
if s := trimFilenameForLabel(name); s != cmd.samples[i].id {
return fmt.Errorf("mismatched sample list: sample %d is %q in library, %q in %s", i, s, cmd.samples[i].id, *samplesFilename)
}
+ }
+ if *caseControlOnly {
+ for i := 0; i < len(cmd.samples); i++ {
+ if !cmd.samples[i].isTraining && !cmd.samples[i].isValidation {
+ if i+1 < len(cmd.samples) {
+ copy(cmd.samples[i:], cmd.samples[i+1:])
+ copy(cmd.cgnames[i:], cmd.cgnames[i+1:])
+ }
+ cmd.samples = cmd.samples[:len(cmd.samples)-1]
+ cmd.cgnames = cmd.cgnames[:len(cmd.cgnames)-1]
+ i--
+ }
+ }
+ }
+ cmd.chi2Cases = nil
+ cmd.trainingSetSize = 0
+ for i := range cmd.cgnames {
if cmd.samples[i].isTraining {
cmd.trainingSet[i] = cmd.trainingSetSize
cmd.trainingSetSize++
+ cmd.chi2Cases = append(cmd.chi2Cases, cmd.samples[i].isCase)
} else {
cmd.trainingSet[i] = -1
}
cmd.minCoverage = int(math.Ceil(cmd.filter.MinCoverage * float64(len(cmd.cgnames))))
}
+ {
+ samplesOutFilename := *outputDir + "/samples.csv"
+ log.Infof("writing sample metadata to %s", samplesOutFilename)
+ var f *os.File
+ f, err = os.Create(samplesOutFilename)
+ if err != nil {
+ return err
+ }
+ defer f.Close()
+ for i, si := range cmd.samples {
+ var cc, tv string
+ if si.isCase {
+ cc = "1"
+ } else if si.isControl {
+ cc = "0"
+ }
+ if si.isTraining {
+ tv = "1"
+ } else {
+ tv = "0"
+ }
+ _, err = fmt.Fprintf(f, "%d,%s,%s,%s\n", i, si.id, cc, tv)
+ if err != nil {
+ err = fmt.Errorf("write %s: %w", samplesOutFilename, err)
+ return err
+ }
+ }
+ err = f.Close()
+ if err != nil {
+ err = fmt.Errorf("close %s: %w", samplesOutFilename, err)
+ return err
+ }
+ log.Print("done")
+ }
+
log.Info("indexing reference tiles")
type reftileinfo struct {
variant tileVariantID
break
}
remap := variantRemap[tag-tagstart]
+ if remap == nil {
+ // was not assigned above,
+ // because minCoverage
+ outcol++
+ continue
+ }
maxv := tileVariantID(0)
for _, v := range remap {
if maxv < v {
cols = (cols + 1) / 2
stride = stride * 2
}
+ if cols%2 == 1 {
+ // we work with pairs of columns
+ cols++
+ }
log.Printf("creating full matrix (%d rows) and training matrix (%d rows) with %d cols, stride %d", len(cmd.cgnames), cmd.trainingSetSize, cols, stride)
mtxFull := mat.NewDense(len(cmd.cgnames), cols, nil)
mtxTrain := mat.NewDense(cmd.trainingSetSize, cols, nil)
lineNum := 0
for _, csv := range bytes.Split(buf, []byte{'\n'}) {
lineNum++
+ if len(csv) == 0 {
+ continue
+ }
split := strings.Split(string(csv), ",")
- if len(split) != 4 {
- return nil, fmt.Errorf("%d fields != 4 in %s line %d: %q", len(split), samplesFilename, lineNum, csv)
+ if len(split) < 4 {
+ return nil, fmt.Errorf("%d fields < 4 in %s line %d: %q", len(split), samplesFilename, lineNum, csv)
}
if split[0] == "Index" && split[1] == "SampleID" && split[2] == "CaseControl" && split[3] == "TrainingValidation" {
continue
if idx != len(si) {
return nil, fmt.Errorf("%s line %d: index %d out of order", samplesFilename, lineNum, idx)
}
+ var pcaComponents []float64
+ if len(split) > 4 {
+ for _, s := range split[4:] {
+ f, err := strconv.ParseFloat(s, 64)
+ if err != nil {
+ return nil, fmt.Errorf("%s line %d: cannot parse float %q: %s", samplesFilename, lineNum, s, err)
+ }
+ pcaComponents = append(pcaComponents, f)
+ }
+ }
si = append(si, sampleInfo{
- id: split[1],
- isCase: split[2] == "1",
- isControl: split[2] == "0",
- isTraining: split[3] == "1",
- isValidation: split[3] == "0",
+ id: split[1],
+ isCase: split[2] == "1",
+ isControl: split[2] == "0",
+ isTraining: split[3] == "1",
+ isValidation: split[3] == "0",
+ pcaComponents: pcaComponents,
})
}
return si, nil
if coverage < cmd.minCoverage {
return nil, nil
}
+ // "observed" array for p-value calculation (training set
+ // only)
obs := make([][]bool, (maxv+1)*2) // 2 slices (hom + het) for each variant#
+ // one-hot output (all samples)
+ outcols := make([][]int8, (maxv+1)*2)
for i := range obs {
obs[i] = make([]bool, cmd.trainingSetSize)
+ outcols[i] = make([]int8, len(cmd.cgnames))
}
for cgid, name := range cmd.cgnames {
tsid := cmd.trainingSet[cgid]
- if tsid < 0 {
- continue
- }
cgvars := cgs[name].Variants[tagoffset*2:]
tv0, tv1 := remap[cgvars[0]], remap[cgvars[1]]
for v := tileVariantID(1); v <= maxv; v++ {
if tv0 == v && tv1 == v {
- obs[v*2][tsid] = true
+ if tsid >= 0 {
+ obs[v*2][tsid] = true
+ }
+ outcols[v*2][cgid] = 1
} else if tv0 == v || tv1 == v {
- obs[v*2+1][tsid] = true
+ if tsid >= 0 {
+ obs[v*2+1][tsid] = true
+ }
+ outcols[v*2+1][cgid] = 1
}
}
}
if col < 4 && !cmd.includeVariant1 {
continue
}
- p := pvalue(obs[col], cmd.chi2Cases)
+ var p float64
+ if len(cmd.samples[0].pcaComponents) > 0 {
+ p = pvalueGLM(cmd.samples, obs[col])
+ } else {
+ p = pvalue(obs[col], cmd.chi2Cases)
+ }
if cmd.chi2PValue < 1 && !(p < cmd.chi2PValue) {
continue
}
- onehot = append(onehot, bool2int8(obs[col]))
+ onehot = append(onehot, outcols[col])
xref = append(xref, onehotXref{
tag: tag,
variant: tileVariantID(col >> 1),
return onehot, xref
}
-func bool2int8(in []bool) []int8 {
- out := make([]int8, len(in))
- for i, v := range in {
- if v {
- out[i] = 1
- }
- }
- return out
-}
-
// convert a []onehotXref with length N to a numpy-style []int32
// matrix with N columns, one row per field of onehotXref struct.
//
projects / lightning.git / blobdiff