Showing 7 of total 7 results (show query)
bioc
BERT:High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT)
Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees.
Maintained by Yannis Schumann. Last updated 2 months ago.
batcheffectpreprocessingexperimentaldesignqualitycontrolbatch-effectbioconductor-packagebioinformaticsdata-integrationdata-science
49.4 match 2 stars 5.40 score 18 scriptsbioc
BEclear:Correction of batch effects in DNA methylation data
Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.
Maintained by Livia Rasp. Last updated 5 months ago.
batcheffectdnamethylationsoftwarepreprocessingstatisticalmethodbatch-effectsbioconductor-packagedna-methylationlatent-factor-modelmethylationmissing-datamissing-valuesstochastic-gradient-descentcpp
44.8 match 4 stars 5.90 score 11 scripts
stopsack
batchtma:Batch Effect Adjustments
Different adjustment methods for batch effects in biomarker data, such as from tissue microarrays. Some methods attempt to retain differences between batches that may be due to between-batch differences in "biological" factors that influence biomarker values.
Maintained by Konrad Stopsack. Last updated 9 months ago.
batch-effectsmeasurement-errortissue-microarray-analysis
50.2 match 1 stars 3.70 score 3 scriptsbioc
NewWave:Negative binomial model for scRNA-seq
A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.
Maintained by Federico Agostinis. Last updated 5 months ago.
softwaregeneexpressiontranscriptomicssinglecellbatcheffectsequencingcoverageregressionbatch-effectsdimensionality-reductionnegative-binomialscrna-seq
23.3 match 4 stars 5.33 score 27 scriptsbioc
BUScorrect:Batch Effects Correction with Unknown Subtypes
High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.
Maintained by Xiangyu Luo. Last updated 5 months ago.
geneexpressionstatisticalmethodbayesianclusteringfeatureextractionbatcheffect
30.4 match 4.00 score 2 scripts
martinloza
Canek:Batch Correction of Single Cell Transcriptome Data
Non-linear/linear hybrid method for batch-effect correction that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.
Maintained by Martin Loza. Last updated 1 years ago.
batch-effectsbioinformaticssingle-cell-rna-seqtranscriptomics
22.3 match 5 stars 5.06 score 23 scripts
tengfei-emory
QuantNorm:Mitigating the Adverse Impact of Batch Effects in Sample Pattern Detection
Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on 'GitHub' <https://github.com/tengfei-emory/QuantNorm>.
Maintained by Teng Fei. Last updated 5 years ago.
25.0 match 9 stars 3.65 score 9 scripts