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dreamai

Imputation of missing values of a matrix or data.frame using iterative prediction model

WangLab-MSSM/DreamAI00

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README.md

DreamAI

Authors
Overview
Installation
Usage
Arguments
Value
Note
Examples
Contributions

Authors

Shrabanti Chowdhury<sup>1</sup>, Weiping Ma<sup>1</sup>, Sunkyu Kim<sup>2</sup>; Zhi Li<sup>3</sup>, Thomas Yu<sup>4</sup>, Mi Yang<sup>5,6</sup>, Francesca Petralia<sup>1</sup>, Jeremy Jacobsen<sup>7</sup>, Jingyi Jessica Li<sup>8</sup>, Xinzhou Ge<sup>8</sup>, Kexin Li<sup>9</sup>, Nathan Edwards<sup>10</sup>, Samuel Payne<sup>11</sup>, Henry Rodriguez<sup>12</sup>, Paul Boutros<sup>13</sup>, Gustavo Stolovitzky<sup>14</sup>, Jaewoo Kang<sup>2</sup>, David Fenyo<sup>3</sup>, Julio Saez-Rodriguez,<sup>6,15</sup>, Pei Wang<sup>1</sup>

<sup>1</sup>Icahn School of Medicine at Mount Sinai (USA), <sup>2</sup>Department of Computer Science and Engineering, Korea University (South Korea), <sup>3</sup>New York University (USA), <sup>4</sup>Sage Bionetworks (USA), <sup>5</sup>Heidelberg University, Faculty of Biosciences (Germany), <sup>6</sup>RWTH Aachen University (Germany), <sup>7</sup>University of Colorado (USA), <sup>8</sup>Department of Statistics, University of California (USA), <sup>9</sup>Department of Mathematics, Tsinghua University (China), <sup>10</sup>Georgetown University (USA), <sup>11</sup>Pacific Northwest National Laboratory (USA), <sup>12</sup>National Cancer Institute (USA), <sup>13</sup>Ontario Institute of Cancer Research (Canada), <sup>14</sup>IBM Research & Mount Sinai (USA), <sup>15</sup>European Molecular Biology Laboratory-European Bioinformatics Institute (UK)

Overview

To develop powerful computational tools to extract the most information from the proteome, Clinical Proteomic Tumor Analysis Consortium (CPTAC) and DREAM organization launched The NCI-CPTAC DREAM Proteogenomics Challenge in 2016, one of the subchallenges: impute missing values in proteomics data given observed proteins.

require("cluster")
require("survival")
require("randomForest")
require("missForest")
require("glmnet")
require("Rcpp")
require("foreach")
require("itertools")
require("iterators")
require("Matrix")
require("devtools")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("impute", version = "3.8")
require("impute")

require("remotes")
install_github("WangLab-MSSM/DreamAI/Code")

DreamAI(data, k = 10, maxiter_MF = 10, ntree = 100,
  maxnodes = NULL, maxiter_ADMIN = 30, tol = 10^(-2),
  gamma_ADMIN = NA, gamma = 50, CV = FALSE,
  fillmethod = "row_mean", maxiter_RegImpute = 10,
  conv_nrmse = 1e-06, iter_SpectroFM = 40, method = c("KNN",
  "MissForest", "ADMIN", "Birnn", "SpectroFM", "RegImpute"),
  out = c("Ensemble"))

| Parameter | Default | Description | | :------------------------ |:-------------:| :-------------| | data | |dataset in the form of a matrix or dataframe with missing values or NA's. The function throws an error message and stops if any row or column in the dataset is missing all values | k | 10 |number of neighbors to be used in the imputation by KNN and ADMIN | maxiter_MF | 10 |maximum number of iteration to be performed in the imputation by "MissForest" if the stopping criteria is not met beforehand | ntree | 100 | number of trees to grow in each forest in "MissForest" | maxnodes | NULL | maximum number of terminal nodes for trees in the forest in "MissForest", has to equal at least the number of columns in the given data | maxiter_ADMIN | 30 | maximum number of iteration to be performed in the imputation by "ADMIN" if the stopping criteria is not met beforehand | tol | 10^(-2) | convergence threshold for "ADMIN" | gamma_ADMIN | NA | parameter for ADMIN to control abundance dependent missing. Set gamma_ADMIN=0 for log ratio intensity data. For abundance data put gamma_ADMIN=NA, and it will be estimated accordingly | gamma | 50 | parameter of the supergradients of popular nonconvex surrogate functions, e.g. SCAD and MCP of L0-norm for Birnn | CV | FALSE | a logical value indicating whether to fit the best gamma with cross validation for "Birnn". If CV=FALSE, default gamma=50 is used, while if CV=TRUE gamma is calculated using cross-validation. | fillmethod | "row_mean" | a string identifying the method to be used to initially filling the missing values using simple imputation for "RegImpute". That could be "row_mean" or "zeros", with "row_mean" being the default. It throws an warning if "row_median" is used. | maxiter_RegImpute | 10 | maximum number of iterations to reach convergence in the imputation by "RegImpute" | conv_nrmse | 1e-06 | convergence threshold for "RegImpute" | iter_SpectroFM | 40 | number of iterations for "SpectroFM" | method | c("KNN","MissForest", "ADMIN", "Birnn", "SpectroFM", "RegImpute", "Ensemble") | a vector of imputation methods: ("KNN", "MissForest", "ADMIN", "Birnn", "SpectroFM, "RegImpute", "Ensemble"). Default is "Ensemble" if nothing is specified | out | c("Ensemble") | a vector of imputation methods for which the function will output the imputed matrices

data(datapnnl)
data<-datapnnl.rm.ref[1:100,1:21]
impute<- DreamAI(data,k=10,maxiter_MF = 10, ntree = 100,maxnodes = NULL,maxiter_ADMIN=30,tol=10^(-2),gamma_ADMIN=NA,gamma=50,CV=FALSE,fillmethod="row_mean",maxiter_RegImpute=10,conv_nrmse = 1e-6,iter_SpectroFM=40, method = c("KNN", "MissForest", "ADMIN", "Birnn", "SpectroFM", "RegImpute"),out="Ensemble")
impute$Ensemble