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Merge pull request #15 from Wedge-lab/mjakobs-patch-2
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Mjakobs patch 2
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@mjakobs
mjakobs authored Jan 5, 2024
2 parents 16027ca + 87b9fa3 commit 18000de
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: AmplificationTimeR
Type: Package
Title: Timing of Individual Amplicitation Events in Cancer
Version: 1.1.0
Version: 1.1.1
RoxygenNote: 7.2.2
Encoding: UTF-8
License: GPL (>= 3)
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2 changes: 1 addition & 1 deletion R/time_amplification.R
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Expand Up @@ -715,7 +715,7 @@ time_amplification_maths <- function(mult_data, max_amp, is_WGD, ordering_event)
t_1 <- (9*n7)/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)
t_2 <- (9*(n5 + n7))/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)
t_3 <- (9*(n4 + n5 + n7))/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)
t_4 <- (9*(n3 + n3 + n5 + n7))/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)
t_4 <- (9*(n3 + n4 + n5 + n7))/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)
t_5 <- (9*(n2 + n3 + n4 - n5))/(n1 + 2*n2 + 3*n3 + 4*n4 + 5*n5 + 7*n7)

amplification_results$t_1 <- t_1
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143 changes: 81 additions & 62 deletions README.md
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Expand Up @@ -57,66 +57,85 @@ data(demo_cn, demo_mult, demo_muts)
Copy number data should be derived from [`Battenberg`](https://github.com/Wedge-lab/battenberg) and must contain the following columns at the very least: `"chr"`, `"startpos"`, `"endpos"`, `"nMaj1_A"`, `"nMin1_A"`. `"nMaj2_A"` and `"nMin2_A"` should be included if applicable. Other columns may be present but the 5 columns listed above are a core requirement.
```r
demo_cn
## chr startpos endpos nMaj1_A nMin1_A
## 1 1 50000 1e+06 2 1

## chr startpos endpos nMaj1_A nMin1_A
## 1 1 50000 1e+06 5 2
```

### Multiplicity data
Multiplicity data should be derived from [`dpclust3p`](https://github.com/Wedge-lab/dpclust3p). The multiplicity data frame must have the following columns at a minimum: `"chr"`, `"end"`, and `"no.chrs.bearing.mut"`. Other columns may be present.

```r
deom_mult

## chr end no.chrs.bearing.mut
## 1 1 57682 2
## 2 1 57683 2
## 3 1 57684 2
## 4 1 57685 2
## 5 1 57686 2
## 6 1 57687 1
## 7 1 57688 1
## 8 1 57689 1
## 9 1 57690 1
## 10 1 57691 1
## 11 1 57692 1
## 12 1 57693 1
## 13 1 57694 1
## 14 1 57695 1
## 15 1 57696 1
## 16 1 57697 1
## 17 1 57698 1
## 18 1 57699 1
## 19 1 57700 1
## 20 1 57701 1
head(demo_mult,20)

## chr end no.chrs.bearing.mut
## 1 1 57682 1
## 2 1 62417 1
## 3 1 67153 1
## 4 1 71888 1
## 5 1 76623 1
## 6 1 81358 1
## 7 1 86094 1
## 8 1 90829 1
## 9 1 95564 1
## 10 1 100299 1
## 11 1 105035 1
## 12 1 109770 1
## 13 1 114505 1
## 14 1 119240 1
## 15 1 123976 1
## 16 1 128711 1
## 17 1 133446 1
## 18 1 138182 1
## 19 1 142917 1
## 20 1 147652 1
```
```r
str(demo_mult)

## 'data.frame': 200 obs. of 3 variables:
## $ chr : num 1 1 1 1 1 1 1 1 1 1 ...
## $ end : num 57682 62417 67153 71888 76623 ...
## $ no.chrs.bearing.mut: num 1 1 1 1 1 1 1 1 1 1 ...
```

### Mutation data
The mutation data frame should contain either all mutations present in the sample or region, or only mutations attributed to mutational signatures SBS1 and SBS5. The presence of the following columns is required:`"chr"`, `"start"`, `"end"`, `"ref"`, `"alt"`. Other columns may be present.

```r
demo_muts

## chr start end ref alt
## 1 1 57682 57682 C T
## 2 1 57683 57683 C T
## 3 1 57684 57684 C T
## 4 1 57685 57685 C T
## 5 1 57686 57686 C T
## 6 1 57687 57687 C T
## 7 1 57688 57688 C T
## 8 1 57689 57689 C T
## 9 1 57690 57690 C T
## 10 1 57691 57691 C T
## 11 1 57692 57692 C T
## 12 1 57693 57693 C T
## 13 1 57694 57694 C T
## 14 1 57695 57695 C T
## 15 1 57696 57696 C T
## 16 1 57697 57697 A G
## 17 1 57698 57698 A G
## 18 1 57699 57699 A G
## 19 1 57700 57700 A G
## 20 1 57701 57701 A G
head(demo_muts, 20)

## chr start end ref alt
## 1 1 57682 57682 C T
## 2 1 62417 62417 C T
## 3 1 67153 67153 C T
## 4 1 71888 71888 C T
## 5 1 76623 76623 A G
## 6 1 81358 81358 C T
## 7 1 86094 86094 C T
## 8 1 90829 90829 C T
## 9 1 95564 95564 C T
## 10 1 100299 100299 A G
## 11 1 105035 105035 C T
## 12 1 109770 109770 C T
## 13 1 114505 114505 C T
## 14 1 119240 119240 C T
## 15 1 123976 123976 A G
## 16 1 128711 128711 C T
## 17 1 133446 133446 C T
## 18 1 138182 138182 C T
## 19 1 142917 142917 C T
## 20 1 147652 147652 A G
```
```r
str(demo_muts)

## 'data.frame': 200 obs. of 5 variables:
## $ chr : num 1 1 1 1 1 1 1 1 1 1 ...
## $ start: num 57682 62417 67153 71888 76623 ...
## $ end : num 57682 62417 67153 71888 76623 ...
## $ ref : chr "C" "C" "C" "C" ...
## $ alt : chr "T" "T" "T" "T" ...
```

### A Note on Mutation Types
Expand All @@ -135,7 +154,7 @@ amp_stop <- 1500000
A `TRUE` or `FALSE` logical value encoding whether a sample has been identified as whole genome duplicated or not.

### Reference genome
Either `"hg19"` or `"hg38"` depending on which is appropriate. This is required so that `AmplificationTimeR` can filter mutations for C>T variants occurring specifically at CpG sites, to ensre that only clock-like mutations are used.
Either `"hg19"` or `"hg38"` depending on which is appropriate. This is required so that `AmplificationTimeR` can filter mutations for C>T variants occurring specifically at CpG sites, to ensure that only clock-like mutations are used.

### Example

Expand Down Expand Up @@ -165,19 +184,19 @@ segment_time <- time_amplification(
```r
segment_time

## Data frame with 1 row and 47 columns
## sample region highest_copy_number event_order num_mutations_used clonality_status flags t_1
## 1 test_sample 1:50000-1e+06 2+1 W 4 clonal NA 0
## t_1_mean_bootstrap t_1_lower_ci t_1_upper_ci t_2 t_2_mean_bootstrap t_2_lower_ci t_2_upper_ci t_3
## 1 0 0 0 NA NA NA NA NA
## t_3_mean_bootstrap t_3_lower_ci t_3_upper_ci t_4 t_4_mean_bootstrap t_4_lower_ci t_4_upper_ci t_5
## 1 NA NA NA NA NA NA NA NA
## t_5_mean_bootstrap t_5_lower_ci t_5_upper_ci t_6 t_6_mean_bootstrap t_6_lower_ci t_6_upper_ci t_7
## 1 NA NA NA NA NA NA NA NA
## t_7_mean_bootstrap t_7_lower_ci t_7_upper_ci t_8 t_8_mean_bootstrap t_8_lower_ci t_8_upper_ci t_9
## 1 NA NA NA NA NA NA NA NA
## t_9_mean_bootstrap t_9_lower_ci t_9_upper_ci t_10 t_10_mean_bootstrap t_10_lower_ci t_10_upper_ci
## 1 NA NA NA NA NA NA NA
## Data frame with 1 row and 4 columns
## sample region highest_copy_number event_order num_mutations_used clonality_status flags t_1
## 1 test_sample 1:50000-1e+06 5+2 WGGG 34 clonal NA 0.893617
## t_1_mean_bootstrap t_1_lower_ci t_1_upper_ci t_2 t_2_mean_bootstrap t_2_lower_ci t_2_upper_ci t_3
## 1 0.8827177 0.8723866 0.8930488 0.893617 0.8827177 0.8723866 0.8930488 0.893617
## t_3_mean_bootstrap t_3_lower_ci t_3_upper_ci t_4 t_4_mean_bootstrap t_4_lower_ci t_4_upper_ci t_5
## 1 0.8827177 0.8723866 0.8930488 0.893617 0.9165478 0.8942302 0.9388653 NA
## t_5_mean_bootstrap t_5_lower_ci t_5_upper_ci t_6 t_6_mean_bootstrap t_6_lower_ci t_6_upper_ci t_7 t_7_mean_bootstrap
## 1 NA NA NA NA NA NA NA NA NA
## t_7_lower_ci t_7_upper_ci t_8 t_8_mean_bootstrap t_8_lower_ci t_8_upper_ci t_9 t_9_mean_bootstrap t_9_lower_ci
## 1 NA NA NA NA NA NA NA NA NA
## t_9_upper_ci t_10 t_10_mean_bootstrap t_10_lower_ci t_10_upper_ci
## 1 NA NA NA NA NA
```
### Output flags
We have provided a number of flags to assist users in identifying `AmplificationTimeR` results that may warrant further investigation.
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