An integrative analysis tool to screen for Proteogenomic Functional traits perturbed by DNA-level alterations (e.g. somatic mutation, copy number variation (CNV) and DNA methylation)
The goal of iProFun is to characterize multi-omics functional consequences of DNA-level alterations in tumor.
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iProFun starts with linear regressions that consider multiple -omic outcomes (e.g. mRNA, protein and phosphoprotein) separately, so it allows different genes and samples for different outcomes. This analysis can be performed by
iProFun.regfunction. -
iProFun uses the summary statistics from multiple regressions to jointly detect their associations with DNA-level alternations.
For data types with few events (e.g. somatic mutations), iProFun
provides estimate, standard error, Student’s t-test p-value, family-wise
error rate (FWER), multi-omic directional filtering, and whether it’s
identified by iProFun.
- The
iProFunidentification here is determined by FWER< a cutoff & pass of a directional filtering criterion.
For data types with many genes, where parallel features of the genes can
be learned from each other to boost study power, iProFun provides
estimate, standard error, Student’s t-test p-value, posterior
association probability, empirical false discovery rate (eFDR),
multi-omic directional filtering, and whether it’s identified by
iProFun.
-
The
iProFunidentification here is determined by- posterior association probability > a cutoff (e.g. 75%),
- eFDR < a cutoff (e.g. 0.1), and
- pass of a directional filtering criterion (e.g. no filter, all positive/negative associations across all outcome data types, consistent association directions across all outcome data types).
A full description of the method can be found in our paper.
You can install the latest version directly from GitHub with devtools:
install.packages("devtools")
devtools::install_github("songxiaoyu/iProFun")Below is an example of iProFun Integrative analysis pipeline.
The preprocessed data from National Cancer Institute’s Clinical
Proteomic Tumor Analysis Consortium Lung Squamous Cell Carcinoma (lscc)
study are included in the package, including cnv, mut, rna,
protein, phospho and cov.
- Data reference: ’Satpathy, Shankha, et al. “A proteogenomic portrait of lung squamous cell carcinoma.” Cell 184.16 (2021): 4348-4371.
A brief description of the sample dataset can be found in the help page of the data.
library(iProFun)
data(lscc_iProFun_Data) # load all data
objects() # list all loaded data## [1] "cnv" "cov" "mut" "phospho" "protein" "rna"
?cnv # help file is available for each individual dataAnalysis should specify multi-omics outcome data types, predictor data
types, covariates and prior association probability. Here we consider
RNA, protein and phosphoprotein as outcomes, and mutation and cnv as
predictors, and use the same set of covariates for three outcomes. We
use a conservative prior pi1 = 0.05. Note, the impact of prior is
minimal on the results.
yList = list(rna, protein, phospho); xList = list(mut, cnv)
covariates = list(cov, cov, cov) # iProFun allows different covariates for different regressions, and here we repeat the same covariates for simplicity
pi1 = 0.05 # prior association probability. Try regression on one outcome data type for checking the implementation
ft1=iProFun.reg.1y(yList.1y=yList[[1]], xList=xList, covariates.1y=covariates[[1]],
var.ID=c("geneSymbol"))The result ft1 is a list, which contains
- xName (Predictor variable name corresponds to each predictor-outcome pair),
- yName (Outcome variable name corresponds to each predictor-outcome pair),
- betas (Coefficient estimate for predictors),
- betas_se (Coefficent SE for predictors),
- sigma2 (Regression error terms for predictors),
- dfs (Regression degrees of freedom for predictors),
- v_g ((X^T X)^-1 projection on predictors). Each of the element contains a sublist, which stores the corresponding results for each of the element in xList.
For multi-omic iProFun analysis, we need regression on all three outcome data types:
reg.all=iProFun.reg(yList=yList, xList=xList, covariates=covariates,
var.ID=c("geneSymbol"), var.ID.additional=c("id"))The result reg.all is a list with length equals to the length of
yList. The first element reg.all[[1]] is essentially the same as
ft1 since both of them store the results between yList[[1]] and
xList. Similarly the second element reg.all[[2]] stores the results
between yList[[2]] and xList and so on and so forth.
If one is interested to save regression results in a single table, one can use this function to output the result table
reg.tab=iProFun.reg.table(reg.all=reg.all, xType = c("mutation", "cnv"),
yType = c("rna", "protein", "phospho"))This function calculates FWER. It’s preferred to be used for the data
type with few genes, such as somatic mutation. Mutation is the first
element in the xList, so we use FWER.Index=c(1) to calculate FWER for
this element.
FWER.all=iProFun.FWER(reg.all=reg.all, FWER.Index=c(1))This function calculates posterior association probability and eFDR rate
for the predictor data types on one outcome. It’s preferred to be used
for data types with many genes, such as cnv. As, we don’t want to
calculate the probabilities of association patterns between the mutation
(1st element of xList) and yList and we set NoProbXIndex = c(1). For a
fast demonstration, we permute the data only twice using
permutate_number=2.
eFDR1=iProFun.eFDR.1y(reg.all=reg.all, which.y=1, yList=yList, xList=xList,
covariates=covariates, pi1=pi1, NoProbXIndex=c(1),
permutate_number=2, var.ID=c("geneSymbol"),
var.ID.additional=c("id"))## [1] "perm" "1"
## [1] "perm" "2"
This is an expansion of iProFun.eFDR.1y to calculate eFDR for all
outcomes.
eFDR.all=iProFun.eFDR(reg.all=reg.all, yList=yList, xList=xList, covariates=covariates, pi1=pi1,
NoProbXIndex=c(1), permutate_number=2, var.ID=c("geneSymbol"),
var.ID.additional=c("id"), seed=123)## [1] "Outcome" "1"
## [1] "perm" "1"
## [1] "perm" "2"
## [1] "Outcome" "2"
## [1] "perm" "1"
## [1] "perm" "2"
## [1] "Outcome" "3"
## [1] "perm" "1"
## [1] "perm" "2"
The result eFDR is a list with length equals to the length of yList.
The first element eFDR[[1]] is essentially the same as eFDR1 since
both of them store the results between eFDR[[1]] and xList.
This function provides iProFun identifications for all predictors and
outcomes, based on FWER/eFDR, association probabilities, and biological
directional filtering. The output has been reformatted to a long-format
table for usage.
# iProFun identification
# For data types with many genes, it's based on
# (1) association probabilities > 0.75 as specified by `PostPob.cutoff=0.75`,
# (2) FDR 0.1 as specified by `fdr.cutoff = 0.1`, and
# (3) the association direction filtering (CNV requires positive associations as specified by the second element of`filter=c(0, 1)` ).
# For data types with few genes, it's based on
# (1) FWER 0.1 as specified by `fwer.cutoff=0.1`, and
# (2) the association direction filtering (mutation requires consistent association directions as specified by the first element of`filter=c(0, 1)`).
res=iProFun.detection(reg.all=reg.all, eFDR.all=eFDR.all, FWER.all=FWER.all, filter=c(0, 1),
NoProbButFWERIndex=1,fdr.cutoff = 0.1, fwer.cutoff=0.1, PostPob.cutoff=0.75,
xType=c("mutation", "cnv"), yType=c("rna", "protein", "phospho"))Output some results
head(res)## xName yName.1 yName.2 xType yType est
## 1 TP53 TP53 NP_000537.3_S315s _1_0_314_315 mutation rna -0.202703364
## 2 PTEN PTEN NP_001291646.2_S467s _1_1_467_467 mutation rna -0.059590144
## 3 CDKN2A <NA> <NA> mutation rna 1.231840027
## 4 KMT2D KMT2D NP_003473.3_T1843t _1_1_1843_1843 mutation rna -0.185339293
## 5 NFE2L2 NFE2L2 NP_006155.2_S215s _1_1_215_215 mutation rna 0.211863472
## 6 ARID1A ARID1A NP_006006.3_S1755s _1_1_1755_1755 mutation rna -0.004243765
## se pvalue FWER eFDR PostProb d.filter iProFun.identification
## 1 0.4010039 0.614294914 1.0000000 NA NA 0 0
## 2 0.1287821 0.644540765 1.0000000 NA NA 0 0
## 3 0.4643312 0.009243121 0.1201606 NA NA 0 0
## 4 0.1196013 0.124294431 1.0000000 NA NA 1 0
## 5 0.2408745 0.381145481 1.0000000 NA NA 1 0
## 6 0.1344626 0.974883272 1.0000000 NA NA 0 0
This time, we try rna/protein/phospho ~ cnv to see how iProFun works when we need to calcualte association probabilities for all predictors.
# We still need to put cnv into a list
yList = list(rna, protein, phospho); xList = list(cnv)
pi1 = 0.05 # prior association probability. Again, we start with regression on all three outcome data types:
reg.all=iProFun.reg(yList=yList, xList=xList, covariates=NULL,
var.ID=c("geneSymbol"), var.ID.additional=c("id"))To save regression results in a single table, one can use this function
reg.tab=iProFun.reg.table(reg.all=reg.all, xType = c("cnv"),
yType = c("rna", "protein", "phospho"))We skip the function to calculate FWER for predictors like mutation that
exists in few genes, and directly calculate posterior association
probabilities. In this case, we should specify NoProbXIndex=NULL.
eFDR.all=iProFun.eFDR(reg.all=reg.all, yList=yList, xList=xList, covariates=NULL,pi1=pi1,
NoProbXIndex=NULL, permutate_number=2, var.ID=c("geneSymbol"),
var.ID.additional=c("id"), seed=123)## [1] "Outcome" "1"
## [1] "perm" "1"
## [1] "perm" "2"
## [1] "Outcome" "2"
## [1] "perm" "1"
## [1] "perm" "2"
## [1] "Outcome" "3"
## [1] "perm" "1"
## [1] "perm" "2"
To summarize the results in a long-format table, we use
iProFun.detection.
# iProFun identification is based on
# (1) association probabilities > 0.75 as specified by `PostPob.cutoff=0.75`,
# (2) FDR 0.1 as specified by `fdr.cutoff = 0.1`, and
# (3) the association direction filtering (CNV requires positive associations as specified by `filter=c(1)` ).
res=iProFun.detection(reg.all=reg.all, eFDR.all=eFDR.all, FWER.all=NULL, filter=c( 1),NoProbButFWERIndex=NULL,fdr.cutoff = 0.1, fwer.cutoff=NULL, PostPob.cutoff=0.75,
xType=c("cnv"), yType=c("rna", "protein", "phospho"))Output some results
head(res)## xName yName.1 yName.2 yName.3 yName.4 xType yType
## 1 A1CF A1CF <NA> <NA> <NA> cnv rna
## 2 ABI1 ABI1 ABI1 ABI1 NP_001171590.1_S183s _1_1_183_183 cnv rna
## 3 ABL1 ABL1 ABL1 ABL1 NP_009297.2_S828s _1_0_823_828 cnv rna
## 4 ABL2 ABL2 ABL2 ABL2 NP_009298.1_S631s _1_1_631_631 cnv rna
## 5 ACKR3 ACKR3 ACKR3 ACKR3 NP_064707.1_S350s _1_1_350_350 cnv rna
## 6 ACSL3 ACSL3 ACSL3 ACSL3 NP_001341087.1_S683s _1_1_683_683 cnv rna
## est se pvalue FWER eFDR PostProb d.filter
## 1 -1.5186404 1.1998222 2.133184e-01 NA 0.155555556 0.7863618 0
## 2 0.5554881 0.1080023 1.244631e-06 NA 0.006313131 1.0000000 1
## 3 0.9888841 0.1431169 3.756320e-10 NA 0.006313131 1.0000000 1
## 4 0.5337644 0.1334711 1.178884e-04 NA 0.006313131 1.0000000 1
## 5 1.7055492 0.6351614 8.414236e-03 NA 0.119607843 0.9066117 1
## 6 0.8553145 0.1804231 6.663326e-06 NA 0.006313131 1.0000000 1
## iProFun.identification
## 1 0
## 2 1
## 3 1
## 4 1
## 5 0
## 6 1
If you find small bugs, larger issues, or have suggestions, please file them using the issue tracker or email the maintainer at xiaoyu.song@mountsinai.org. Contributions (via pull requests or otherwise) are welcome.