LLSimpute statistics

Tool stats & trends

Looking to identify usage trends or leading experts?


LLSimpute specifications


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


No version available


This tool is not available anymore.

Publication for Local Least Squares imputation method

LLSimpute citations


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

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 […]


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

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 […]


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

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. (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 […]


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

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 […]


Gene expression prediction using low rank matrix completion

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 […]


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

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 […]


Looking to check out a full list of citations?

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.

LLSimpute reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review LLSimpute