README.md
In tianwei-yu/metapone: Conducts pathway test of metabolomics data using a weighted permutation test
Metapone
The metapone package conducts pathway tests for untargeted metabolomics data. It has three main characteristics: (1) expanded database combining SMPDB and Mummichog databases, with manual cleaning to remove redundancies; (2) A new weighted testing scheme to address the issue of metabolite-feature matching uncertainties; (3) Can consider positive mode and negative mode data in a single analysis. The two databases are at:
https://shuzhao-li.github.io/mummichog.org/software.html
Metapone can be install by calling:
devtools::install_github("tianwei-yu/metapone").
To use the package, you need to have testing results on untargetted metabolomics data ready. The test result should contain at least three clumns - m/z, retention time, and feature p-value. An example input data can be seen here:
> library(metapone)
> data(pos)
> head(pos)
m.z retention.time p.value statistic
1 85.04027 55.66454 0.22109229 -1.231240
2 85.07662 56.93586 0.52181695 -0.642790
3 85.57425 125.97117 0.13483680 -1.507372
4 86.06064 194.81306 0.26069118 1.131101
5 86.08001 54.74512 0.17206535 1.375352
6 86.09704 177.73650 0.07541608 1.796427
The package can use data from a single ion mode. But it can also handle the situation where data are collected on the same samples using both modes. If both positive mode and negative mode data are present, each is input into the algorithm as a separate matrix.
```{r example input second matrix}
data(neg)
head(neg)
mz chr pval t-statistic
result.1 85.00448 268.83027 0.2423777 1.1690645
result.2 87.00881 48.84882 0.2222984 1.2204394
result.3 87.92531 161.99560 0.1341622 1.4978887
result.4 88.00399 129.88520 0.2941855 -1.0489839
result.5 88.01216 35.81698 0.8510984 -0.1877171
result.6 88.98808 127.47973 0.1748255 -1.3568608
The test is based on HMDB identification. The common adduct ions are pre-processed and stored in:
```{r example load database}
> data(hmdbCompMZ)
> head(hmdbCompMZ)
HMDB_ID m.z ion.type
1 HMDB0059597 1.343218 M+3H
2 HMDB0001362 1.679159 M+3H
3 HMDB0037238 2.341477 M+3H
4 HMDB0005949 3.345944 M+3H
5 HMDB0002387 4.011337 M+3H
6 HMDB0002386 4.677044 M+3H
Pathway information is built-in. It combines SMPDB and Mummichog databases. The two databases can be found here:
https://shuzhao-li.github.io/mummichog.org/software.html
Here is the combined database after manual pruning of some highly-overlaping pathways:
```{r example load pathway}
data(pa)
head(pa)
database pathway.name HMDB.ID KEGG.ID category
1 Metapone 191 Pterine Biosynthesis HMDB0006822 C05922 Metabolic
2 Metapone 191 Pterine Biosynthesis HMDB0002111 C00001 Metabolic
3 Metapone 191 Pterine Biosynthesis HMDB0006821 C05923 Metabolic
4 Metapone 191 Pterine Biosynthesis HMDB0000142 C00058 Metabolic
5 Metapone 191 Pterine Biosynthesis HMDB0015532 Metabolic
6 Metapone 191 Pterine Biosynthesis HMDB0001273 C00044 Metabolic
The user can specify which adduct ions are allowed by setting the allowed adducts. For example:
```{r example adduct ions}
> pos.adductlist = c("M+H", "M+NH4", "M+Na", "M+ACN+H", "M+ACN+Na", "M+2ACN+H", "2M+H", "2M+Na", "2M+ACN+H")
> neg.adductlist = c("M-H", "M-2H", "M-2H+Na", "M-2H+K", "M-2H+NH4", "M-H2O-H", "M-H+Cl", "M+Cl", "M+2Cl")
It is common for a feature to be matched to multiple metabolites. Assume a feature is matched to n metabolites, metapone weighs the feature by (1/n)^p, where p is a power term to tune the penalty. n can also be capped at a certain level such that the penalty is limited. These are controlled by parameters:
Setting p: fractional.count.power = 0.5
Setting the cap of n: max.match.count = 10 (this is to cap the level of penalty on a multiple-matched feature.
Other parameters include p.threshold, which controls which metabolic feature is considered significant. The testing is done by permutation. Overall, the analysis is conducted this way:
```{r example analysis}
dat <- list(pos, neg)
type <- list("pos", "neg")
r<-metapone(dat, type, pa, hmdbCompMZ=hmdbCompMZ, pos.adductlist=pos.adductlist, neg.adductlist=neg.adductlist,
p.threshold=0.05,n.permu=100,fractional.count.power=0.5, max.match.count=10)
hist(ptable(r)[,1])
We can subset the pathways that are significant, and with a good number of metabolites matched:
```{r example continued}
> selection<-which(ptable(r)[,1]<0.025)
> ptable(r)[selection,]
p_value n_significant metabolites n_mapped_metabolites n_metabolites
tRNA Charging: Isoleucine 0.01 0.3960824 0.6633436 4
tRNA Charging: Leucine 0.01 0.3960824 0.6633436 4
Drug metabolism - cytochrome P450 0.01 1.5796691 6.1259660 50
Vitamin A (retinol) metabolism 0.01 1.3333333 3.2871677 19
Fatty acid oxidation, peroxisome 0.02 1.2886751 4.1875417 26
Glycosphingolipid metabolism 0.02 2.8395164 13.9627029 45
Leukotriene metabolism 0.01 1.5344457 6.3645097 14
C21-steroid hormone biosynthesis and metabolism 0.00 4.8363062 28.3710206 80
Lysine metabolism 0.02 1.8848575 11.3149960 33
Androgen and Estrogen Metabolism 0.00 2.7672612 13.2744270 63
Glycine and Serine Metabolism 0.01 3.8434006 28.9817190 56
beta-Alanine Metabolism 0.02 2.8133149 16.5311037 42
Porphyrin Metabolism 0.01 3.1213203 12.8947329 47
Purine Metabolism 0.02 4.6733053 31.7643456 91
(some columns omitted)
> ftable(r)[which(ptable(r)[,1]<0.025 & ptable(r)[,2]>=2)]
$`Glycosphingolipid metabolism`
m.z retention.time p.value statistic HMDB_ID m.z ion.type counts
[1,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0000277" 380.256 "M+H" 0.2672612
[2,] 282.2793 522.1412 0.03861876 2.095591 "HMDB0001551" 282.2791 "M+ACN+H" 0.5
[3,] 371.3268 546.4621 0.02881342 -2.217735 "HMDB0006752" 371.3268 "M+ACN+H" 0.7071068
[4,] 179.0563 104.8357 0.02843861 -2.191182 "HMDB0000122" 179.0561 "M-H" 0.1825742
[5,] 179.0563 104.8357 0.02843861 -2.191182 "HMDB0003449" 179.0561 "M-H" 0.1825742
[6,] 380.2575 194.7167 0.02327888 -2.268827 "HMDB0001383" 380.2571 "M-H" 1
$`C21-steroid hormone biosynthesis and metabolism`
m.z retention.time p.value statistic HMDB_ID m.z ion.type counts
[1,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0000253" 380.256 "M+ACN+Na" 0.2672612
[2,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0003069" 380.256 "M+ACN+Na" 0.2672612
[3,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006280" 482.3605 "M+ACN+Na" 0.2672612
[4,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006281" 482.3605 "M+ACN+Na" 0.2672612
[5,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006763" 482.3605 "M+ACN+Na" 0.2672612
[6,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0002829" 465.2494 "M-H" 0.5
[7,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0004484" 465.2494 "M-H" 0.5
[8,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0006203" 465.2494 "M-H" 0.5
[9,] 465.3047 178.2345 0.03315628 -2.130186 "HMDB0000653" 465.3044 "M-H" 1
[10,] 349.1476 235.4682 0.02916411 2.181261 "HMDB0001032" 349.1474 "M-H2O-H" 0.5
[11,] 349.1476 235.4682 0.02916411 2.181261 "HMDB0002833" 349.1474 "M-H2O-H" 0.5
(some rows omitted)
tianwei-yu/metapone documentation built on Aug. 21, 2022, 3:09 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Metapone
The metapone package conducts pathway tests for untargeted metabolomics data. It has three main characteristics: (1) expanded database combining SMPDB and Mummichog databases, with manual cleaning to remove redundancies; (2) A new weighted testing scheme to address the issue of metabolite-feature matching uncertainties; (3) Can consider positive mode and negative mode data in a single analysis. The two databases are at:
https://shuzhao-li.github.io/mummichog.org/software.html
Metapone can be install by calling:
devtools::install_github("tianwei-yu/metapone").
To use the package, you need to have testing results on untargetted metabolomics data ready. The test result should contain at least three clumns - m/z, retention time, and feature p-value. An example input data can be seen here:
> library(metapone)
> data(pos)
> head(pos)
m.z retention.time p.value statistic
1 85.04027 55.66454 0.22109229 -1.231240
2 85.07662 56.93586 0.52181695 -0.642790
3 85.57425 125.97117 0.13483680 -1.507372
4 86.06064 194.81306 0.26069118 1.131101
5 86.08001 54.74512 0.17206535 1.375352
6 86.09704 177.73650 0.07541608 1.796427
The package can use data from a single ion mode. But it can also handle the situation where data are collected on the same samples using both modes. If both positive mode and negative mode data are present, each is input into the algorithm as a separate matrix.
```{r example input second matrix}
data(neg) head(neg) mz chr pval t-statistic result.1 85.00448 268.83027 0.2423777 1.1690645 result.2 87.00881 48.84882 0.2222984 1.2204394 result.3 87.92531 161.99560 0.1341622 1.4978887 result.4 88.00399 129.88520 0.2941855 -1.0489839 result.5 88.01216 35.81698 0.8510984 -0.1877171 result.6 88.98808 127.47973 0.1748255 -1.3568608
The test is based on HMDB identification. The common adduct ions are pre-processed and stored in:
```{r example load database}
> data(hmdbCompMZ)
> head(hmdbCompMZ)
HMDB_ID m.z ion.type
1 HMDB0059597 1.343218 M+3H
2 HMDB0001362 1.679159 M+3H
3 HMDB0037238 2.341477 M+3H
4 HMDB0005949 3.345944 M+3H
5 HMDB0002387 4.011337 M+3H
6 HMDB0002386 4.677044 M+3H
Pathway information is built-in. It combines SMPDB and Mummichog databases. The two databases can be found here:
https://shuzhao-li.github.io/mummichog.org/software.html
Here is the combined database after manual pruning of some highly-overlaping pathways:
```{r example load pathway}
data(pa) head(pa) database pathway.name HMDB.ID KEGG.ID category 1 Metapone 191 Pterine Biosynthesis HMDB0006822 C05922 Metabolic 2 Metapone 191 Pterine Biosynthesis HMDB0002111 C00001 Metabolic 3 Metapone 191 Pterine Biosynthesis HMDB0006821 C05923 Metabolic 4 Metapone 191 Pterine Biosynthesis HMDB0000142 C00058 Metabolic 5 Metapone 191 Pterine Biosynthesis HMDB0015532 Metabolic 6 Metapone 191 Pterine Biosynthesis HMDB0001273 C00044 Metabolic
The user can specify which adduct ions are allowed by setting the allowed adducts. For example:
```{r example adduct ions}
> pos.adductlist = c("M+H", "M+NH4", "M+Na", "M+ACN+H", "M+ACN+Na", "M+2ACN+H", "2M+H", "2M+Na", "2M+ACN+H")
> neg.adductlist = c("M-H", "M-2H", "M-2H+Na", "M-2H+K", "M-2H+NH4", "M-H2O-H", "M-H+Cl", "M+Cl", "M+2Cl")
It is common for a feature to be matched to multiple metabolites. Assume a feature is matched to n metabolites, metapone weighs the feature by (1/n)^p, where p is a power term to tune the penalty. n can also be capped at a certain level such that the penalty is limited. These are controlled by parameters:
Setting p: fractional.count.power = 0.5
Setting the cap of n: max.match.count = 10 (this is to cap the level of penalty on a multiple-matched feature.
Other parameters include p.threshold, which controls which metabolic feature is considered significant. The testing is done by permutation. Overall, the analysis is conducted this way:
```{r example analysis}
dat <- list(pos, neg) type <- list("pos", "neg") r<-metapone(dat, type, pa, hmdbCompMZ=hmdbCompMZ, pos.adductlist=pos.adductlist, neg.adductlist=neg.adductlist, p.threshold=0.05,n.permu=100,fractional.count.power=0.5, max.match.count=10) hist(ptable(r)[,1])
We can subset the pathways that are significant, and with a good number of metabolites matched:
```{r example continued}
> selection<-which(ptable(r)[,1]<0.025)
> ptable(r)[selection,]
p_value n_significant metabolites n_mapped_metabolites n_metabolites
tRNA Charging: Isoleucine 0.01 0.3960824 0.6633436 4
tRNA Charging: Leucine 0.01 0.3960824 0.6633436 4
Drug metabolism - cytochrome P450 0.01 1.5796691 6.1259660 50
Vitamin A (retinol) metabolism 0.01 1.3333333 3.2871677 19
Fatty acid oxidation, peroxisome 0.02 1.2886751 4.1875417 26
Glycosphingolipid metabolism 0.02 2.8395164 13.9627029 45
Leukotriene metabolism 0.01 1.5344457 6.3645097 14
C21-steroid hormone biosynthesis and metabolism 0.00 4.8363062 28.3710206 80
Lysine metabolism 0.02 1.8848575 11.3149960 33
Androgen and Estrogen Metabolism 0.00 2.7672612 13.2744270 63
Glycine and Serine Metabolism 0.01 3.8434006 28.9817190 56
beta-Alanine Metabolism 0.02 2.8133149 16.5311037 42
Porphyrin Metabolism 0.01 3.1213203 12.8947329 47
Purine Metabolism 0.02 4.6733053 31.7643456 91
(some columns omitted)
> ftable(r)[which(ptable(r)[,1]<0.025 & ptable(r)[,2]>=2)]
$`Glycosphingolipid metabolism`
m.z retention.time p.value statistic HMDB_ID m.z ion.type counts
[1,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0000277" 380.256 "M+H" 0.2672612
[2,] 282.2793 522.1412 0.03861876 2.095591 "HMDB0001551" 282.2791 "M+ACN+H" 0.5
[3,] 371.3268 546.4621 0.02881342 -2.217735 "HMDB0006752" 371.3268 "M+ACN+H" 0.7071068
[4,] 179.0563 104.8357 0.02843861 -2.191182 "HMDB0000122" 179.0561 "M-H" 0.1825742
[5,] 179.0563 104.8357 0.02843861 -2.191182 "HMDB0003449" 179.0561 "M-H" 0.1825742
[6,] 380.2575 194.7167 0.02327888 -2.268827 "HMDB0001383" 380.2571 "M-H" 1
$`C21-steroid hormone biosynthesis and metabolism`
m.z retention.time p.value statistic HMDB_ID m.z ion.type counts
[1,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0000253" 380.256 "M+ACN+Na" 0.2672612
[2,] 380.2564 517.4015 0.02576156 2.263223 "HMDB0003069" 380.256 "M+ACN+Na" 0.2672612
[3,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006280" 482.3605 "M+ACN+Na" 0.2672612
[4,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006281" 482.3605 "M+ACN+Na" 0.2672612
[5,] 482.3603 568.9371 0.04738957 -2.007298 "HMDB0006763" 482.3605 "M+ACN+Na" 0.2672612
[6,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0002829" 465.2494 "M-H" 0.5
[7,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0004484" 465.2494 "M-H" 0.5
[8,] 465.2501 180.1967 0.01296878 2.484626 "HMDB0006203" 465.2494 "M-H" 0.5
[9,] 465.3047 178.2345 0.03315628 -2.130186 "HMDB0000653" 465.3044 "M-H" 1
[10,] 349.1476 235.4682 0.02916411 2.181261 "HMDB0001032" 349.1474 "M-H2O-H" 0.5
[11,] 349.1476 235.4682 0.02916411 2.181261 "HMDB0002833" 349.1474 "M-H2O-H" 0.5
(some rows omitted)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.