const annotationMaxTileSpan = 100
type sliceNumpy struct {
- filter filter
- threads int
- chi2Cases []bool
- chi2PValue float64
- glmMinFrequency float64
- pcaComponents int
- minCoverage int
- includeVariant1 bool
- debugTag tagID
+ filter filter
+ threads int
+ chi2Cases []bool
+ chi2PValue float64
+ pvalueMinFrequency float64
+ maxFrequency float64
+ pcaComponents int
+ minCoverage int
+ includeVariant1 bool
+ debugTag tagID
cgnames []string
samples []sampleInfo
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 (or logistic regression if -samples file has PCA components) and omit columns with p-value above this threshold")
- flags.Float64Var(&cmd.glmMinFrequency, "glm-min-frequency", 0.01, "skip GLM calculation on tile variants below this frequency in the training set")
+ flags.Float64Var(&cmd.pvalueMinFrequency, "pvalue-min-frequency", 0.01, "skip p-value calculation on tile variants below this frequency in the training set")
+ flags.Float64Var(&cmd.maxFrequency, "max-frequency", 1, "do not output variants above this frequency in the training set")
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)
"-pca-components=" + fmt.Sprintf("%d", cmd.pcaComponents),
"-max-pca-tiles=" + fmt.Sprintf("%d", *maxPCATiles),
"-chi2-p-value=" + fmt.Sprintf("%f", cmd.chi2PValue),
- "-glm-min-frequency=" + fmt.Sprintf("%f", cmd.glmMinFrequency),
+ "-pvalue-min-frequency=" + fmt.Sprintf("%f", cmd.pvalueMinFrequency),
+ "-max-frequency=" + fmt.Sprintf("%f", cmd.maxFrequency),
"-include-variant-1=" + fmt.Sprintf("%v", cmd.includeVariant1),
"-debug-tag=" + fmt.Sprintf("%d", cmd.debugTag),
}
}
if len(cmd.samples[0].pcaComponents) > 0 {
- cmd.pvalue = glmPvalueFunc(cmd.samples, cmd.pcaComponents, cmd.glmMinFrequency)
+ cmd.pvalue = glmPvalueFunc(cmd.samples, cmd.pcaComponents)
// Unfortunately, statsmodel/glm lib logs stuff to
// os.Stdout when it panics on an unsolvable
// problem. We recover() from the panic in glm.go, but
if err == errSkip {
return nil
} else if err != nil {
- return fmt.Errorf("%04d: DecodeLibrary(%s): err", infileIdx, infile)
+ return fmt.Errorf("%04d: DecodeLibrary(%s): %w", infileIdx, infile, err)
}
tagstart := cgs[cmd.cgnames[0]].StartTag
tagend := cgs[cmd.cgnames[0]].EndTag
for i := range cmd.samples {
cmd.samples[i].pcaComponents = make([]float64, outcols)
for c := 0; c < outcols; c++ {
- cmd.samples[i].pcaComponents[i] = pca.At(i, c)
+ cmd.samples[i].pcaComponents[c] = pca.At(i, c)
}
}
log.Print("done")
variant tileVariantID
hom bool
pvalue float64
+ maf float64
}
const onehotXrefSize = unsafe.Sizeof(onehotXref{})
}
var onehot [][]int8
var xref []onehotXref
+ var maf float64
for col := 2; col < len(obs); col++ {
// col 0,1 correspond to tile variant 0, i.e.,
// no-call; col 2,3 correspond to the most common
if col < 4 && !cmd.includeVariant1 {
continue
}
+ if col&1 == 0 {
+ maf = homhet2maf(obs[col : col+2])
+ if maf < cmd.pvalueMinFrequency {
+ // Skip both columns (hom and het) if
+ // allele frequency is below threshold
+ col++
+ continue
+ }
+ if maf > cmd.maxFrequency {
+ // Skip both columns if allele
+ // frequency is above threshold
+ col++
+ continue
+ }
+ }
atomic.AddInt64(&cmd.pvalueCallCount, 1)
p := cmd.pvalue(obs[col])
if cmd.chi2PValue < 1 && !(p < cmd.chi2PValue) {
variant: tileVariantID(col >> 1),
hom: col&1 == 0,
pvalue: p,
+ maf: maf,
})
}
return onehot, xref
}
+func homhet2maf(onehot [][]bool) float64 {
+ if len(onehot[0]) == 0 {
+ return 0
+ }
+ n := 0
+ for i := range onehot[0] {
+ if onehot[0][i] {
+ // hom
+ n += 2
+ } else if onehot[1][i] {
+ // het
+ n += 1
+ }
+ }
+ return float64(n) / float64(len(onehot[0])*2)
+}
+
// convert a []onehotXref with length N to a numpy-style []int32
// matrix with N columns, one row per field of onehotXref struct.
//
// P-value row contains 1000000x actual p-value.
func onehotXref2int32(xrefs []onehotXref) []int32 {
xcols := len(xrefs)
- xdata := make([]int32, 5*xcols)
+ xdata := make([]int32, 6*xcols)
for i, xref := range xrefs {
xdata[i] = int32(xref.tag)
xdata[xcols+i] = int32(xref.variant)
}
xdata[xcols*3+i] = int32(xref.pvalue * 1000000)
xdata[xcols*4+i] = int32(-math.Log10(xref.pvalue) * 1000000)
+ xdata[xcols*5+i] = int32(xref.maf * 1000000)
}
return xdata
}
projects / lightning.git / blobdiff