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Merged
MaximilianStammnitz merged 7 commits into from
Feb 27, 2026
Merged

Update fitness_calculation.R #27

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b16605d
Update fitness_calculation.R
MaximilianStammnitz Feb 15, 2026
c85dcc1
Update fitness_calculation.R
MaximilianStammnitz Feb 18, 2026
958715a
Merge branch 'nf-core:dev' into dev
MaximilianStammnitz Feb 25, 2026
5f8f48c
Update usage.md
MaximilianStammnitz Feb 25, 2026
bde7577
Merge branch 'nf-core:dev' into dev
MaximilianStammnitz Feb 27, 2026
31a98e7
Update possible_mutations.R
MaximilianStammnitz Feb 27, 2026
43aa32a
Update test.config
MaximilianStammnitz Feb 27, 2026
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2 changes: 1 addition & 1 deletion conf/test.config
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Expand Up @@ -27,7 +27,7 @@ params {
fasta = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A.fasta'
reading_frame = 352-1383
min_counts = 2
mutagenesis_type = max_diff_to_wt
mutagenesis_type = nnk_nns
run_seqdepth = true
fitness = true
}
4 changes: 2 additions & 2 deletions docs/usage.md
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Expand Up @@ -31,7 +31,7 @@ Note that the pipeline will create the following files in your working directory

```console title="working directory"
work # Directory containing the nextflow working files
results # Finished results in specified location (defined with --outdir)
results # Finished results in specified location (defined with --outdir); needs full writing access
.nextflow_log # Log file from Nextflow
```

Expand Down Expand Up @@ -82,7 +82,7 @@ Several optional parameters are available for `nf-core/deepmutscan`, some of whi
| `--run_seqdepth` | `false` | Estimate sequencing saturation by rarefaction |
| `--fitness` | `false` | Default fitness inference module |
| `--dimsum` | `false` | Optional fitness inference module _(AMD/x86_64 systems only)_ |
| `--mutagenesis` | `nnk` | Deep mutational scanning strategy used _(in development)_ |
| `--mutagenesis` | `nnk` | Deep mutational scanning strategy used |
| `--error-estimation` | `wt_sequencing` | Error model used to correct 1nt counts _(in development)_ |
| `--read-align` | `bwa-mem` | Customised read aligner _(in development)_ |

Expand Down
2 changes: 1 addition & 1 deletion modules/local/dmsanalysis/templates/possible_mutations.R
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Expand Up @@ -7,7 +7,7 @@
# Input:
# - wt_seq_input: Wild-type sequence (string or path to FASTA file).
# - start_stop_pos: Target sequence range format "start-stop".
# - mutagenesis_type: Strategy ('nnk', 'nns', 'max_diff_to_wt', 'custom').
# - mutagenesis_type: Strategy ('nnk', 'nns', 'nnh', 'nnn', 'nnk_nns', 'nnk_nns_nnh', 'custom').

@mashehu mashehu Feb 27, 2026

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is this reflected in nextlfow_schema.json? or not relevant?

@MaximilianStammnitz MaximilianStammnitz Feb 27, 2026

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yep, that's already been accounted for – thanks for checking

# - custom_codon_library_path: Path to custom library. Automatically detects
# if the file is a global list ("AAA, AAC...") or a position-wise CSV
# (requires a "Position" header).
Expand Down
68 changes: 48 additions & 20 deletions modules/local/fitness/templates/fitness_calculation.R
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@@ -1,7 +1,7 @@
#!/usr/bin/env Rscript

## default fitness estimation for nf-core/deepmutscan
## 27.10.2025
## 18.02.2026
## maximilian.stammnitz@crg.eu

## 0. Libraries ##
Expand Down Expand Up @@ -110,6 +110,22 @@ aggregate_by_aa <- function(merged.counts) {
merged.counts
}

# calculate bimodal fitness distribution
density_peaks <- function(x, adjust = 1, ...) {

## obtain density distribution
d <- density(x, adjust = adjust, n = 5000, na.rm = T, ...)
y <- d$y
idx <- which(diff(sign(diff(y))) == -2) + 1
if (length(idx) == 0) return(numeric(0))

## order density peaks by height (y value); take top 2
idx <- idx[order(y[idx], decreasing = TRUE)]
peaks_x <- d$x[idx]
peaks_x[seq_len(min(2, length(peaks_x)))]

}

# 3. Raw fitness calculations ##
calc_raw_fitness <- function(merged.counts, exp.design) {
## how many fitness replicates are there
Expand Down Expand Up @@ -164,24 +180,36 @@ rescale_and_summarize <- function(merged.counts, reps) {
### rescale
tmp.wt.fitness.med <- median(tmp.wt.fitness, na.rm = TRUE)
tmp.stop.fitness.med <- median(tmp.stop.fitness, na.rm = TRUE)
if(tmp.stop.fitness.med >= tmp.wt.fitness.med){

tmp.wt.fitness.mean <- mean(tmp.wt.fitness, na.rm = TRUE)
tmp.stop.fitness.mean <- mean(tmp.stop.fitness, na.rm = TRUE)
lm.rescale <- lm(c(0, -1) ~ c(tmp.wt.fitness.mean, tmp.stop.fitness.mean))
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale\$coefficients[[2]] + lm.rescale\$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.mean, tmp.stop.fitness.mean,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale)
next

}else{

## if both WT and STOP mutants are available
if(!is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med)){
lm.rescale <- lm(c(0, -1) ~ c(tmp.wt.fitness.med, tmp.stop.fitness.med))
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale\$coefficients[[2]] + lm.rescale\$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale)
next
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale$coefficients[[2]] + lm.rescale$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale)

## if only WT mutants are available: lower peak determined by bimodal distribution fitting
}else if(!is.na(tmp.wt.fitness.med) & is.na(tmp.stop.fitness.med)){
tmp.peaks <- sort(density_peaks(x = merged.counts[,ncol(merged.counts) - reps]))
lm.rescale <- lm(c(0, -1) ~ c(tmp.wt.fitness.med, tmp.peaks[1]))
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale$coefficients[[2]] + lm.rescale$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale, tmp.peaks)

## if only STOP mutants are available: higher peak determined by bimodal distribution fitting
}else if(is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med)){
tmp.peaks <- sort(density_peaks(x = merged.counts[,ncol(merged.counts) - reps]))
lm.rescale <- lm(c(0, -1) ~ c(tmp.peaks[2], tmp.stop.fitness.med))
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale$coefficients[[2]] + lm.rescale$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale, tmp.peaks)

## if neither WT nor STOP mutants are available: both peak determined by bimodal distribution fitting
}else if(is.na(tmp.wt.fitness.med) & is.na(tmp.stop.fitness.med)){
tmp.peaks <- sort(density_peaks(x = merged.counts[,ncol(merged.counts) - reps]))
lm.rescale <- lm(c(0, -1) ~ c(tmp.peaks[2], tmp.peaks[1]))
merged.counts[,ncol(merged.counts)] <- merged.counts[,ncol(merged.counts) - reps] * lm.rescale$coefficients[[2]] + lm.rescale$coefficients[[1]]
rm(tmp.wt.fitness, tmp.stop.fitness,
tmp.wt.fitness.med, tmp.stop.fitness.med, lm.rescale, tmp.peaks)

}
}
Expand All @@ -197,11 +225,11 @@ rescale_and_summarize <- function(merged.counts, reps) {

}else if(reps > 1){

merged.counts\$`mean fitness` <- apply(merged.counts[,c(ncol(merged.counts) - 2*reps + 1, ncol(merged.counts) - reps)],
merged.counts\$`mean fitness` <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
1,
mean,
na.rm = TRUE)
merged.counts\$`fitness sd` <- apply(merged.counts[,c(ncol(merged.counts) - 2*reps + 1, ncol(merged.counts) - reps)],
merged.counts\$`fitness sd` <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
1,
sd,
na.rm = TRUE)
Expand Down
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