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Unique identifier OMICS_25705
Name LLSimpute
Alternative name Local Least Squares imputation method
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Maintained No

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Publication for Local Least Squares imputation method

LLSimpute citations

 (16)
library_books

Molecular signatures reflecting microenvironmental metabolism and chemotherapy induced immunogenic cell death in colorectal liver metastases

2017
Oncotarget
PMCID: 5652706
PMID: 29100312
DOI: 10.18632/oncotarget.19350

[…] S, Chicago, IL, USA) was used for calculations.Gene expression data was log2-transformed and quantile normalized using the LIMMA []. Imputation of missing values was performed by local least squares (llsImpute from the R package pcaMethods []) with k=20. For subgroup discovery and visualization, data were assessed using a two-way, unsupervised average linkage hierarchical clustering on genes showi […]

call_split

Exploratory plasma proteomic analysis in a randomized crossover trial of aspirin among healthy men and women

2017
PLoS One
PMCID: 5444835
PMID: 28542447
DOI: 10.1371/journal.pone.0178444
call_split See protocol

[…] Antibodies with more than 30% missing values across the arrays were excluded from further analysis. Remaining missing data were imputed using the local least squares imputation method, which replaces a target protein that has missing values with a linear combination of 10 similar proteins, chosen by k-nearest neighbors based on Pearson correlat […]

call_split

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

2017
PMCID: 5372339
PMID: 28356166
DOI: 10.1186/s13058-017-0812-y
call_split See protocol

[…] nner G2565A, and signals were extracted using Feature Extraction v.10.7.3.1 (Agilent Technologies). Non-uniform spots were excluded and missing data were imputed using local least squares imputation (LLSimpute from the R package “pcaMethods” []). Arrays were log2-transformed, quantile-normalized and hospital-adjusted by subtracting from each probe value the mean probe value of the samples from tha […]

library_books

A feature selection method based on multiple kernel learning with expression profiles of different types

2017
BioData Min
PMCID: 5288949
PMID: 28184251
DOI: 10.1186/s13040-017-0124-x

[…] s of each expression dataset are estimated. If the missing values of one mRNA (or miRNA) are less than 20% of all samples, these missing values are estimated using the local least squares imputation (LLSimpute) method []. Then, the different probes of the same mRNA (or miRNA) are merged by the maximum expression value of these probes for each sample. After these processes, these datasets are norma […]

library_books

Gene expression prediction using low rank matrix completion

2016
BMC Bioinformatics
PMCID: 4912738
PMID: 27317252
DOI: 10.1186/s12859-016-1106-6

[…] AUROC, area under receiver operating curve; BPCA, Bayesian principal component analysis; LLSimpute, local least square imputation; NCBI, National Center for Biotechnology Information; NGS, next generation sequencing; NP, nondeterministic polynomial time; RKPM, reads per kilobase of transc […]

library_books

Missing value imputation for microRNA expression data by using a GO based similarity measure

2016
BMC Bioinformatics
PMCID: 4895707
PMID: 26818962
DOI: 10.1186/s12859-015-0853-0

[…] of methods have been proposed for imputing missing values in gene expression profiles, which mainly fall into two categories. The first type is based solely on expression data, such as KNNImpute [], LLSimpute [] and Bayesian method []. The second type is based on domain knowledge in addition to expression data [, ].K-nearest neighbor (KNN) is the most widely used algorithm for missing value imput […]

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LLSimpute institution(s)
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA; Computer Science Department, Stanford University, Stanford, CA, USA; The National Science Foundation, Arlington, VA, USA
LLSimpute funding source(s)
Supported by IR/D from the National Science Foundation and by the National Science Foundation Grants CCR-0204109 and ACI-0305543.

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