BIODICA: a computational environment for Independent Component Analysis of omics data

Captier, Nicolas; Merlevede, Jane; Molkenov, Askhat; Seisenova, Ainur; Zhubanchaliyev, Altynbek; Nazarov, Petr V.; Barillot, Emmanuel; Kairov, Ulykbek; Zinovyev, Andreï

doi:10.1093/bioinformatics/btac204

Cited by 7 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our experiments, we used the stabilized version of ICA ( 21 ) which is shown to be the optimal MF approach for reproducible analysis of transcriptomic data ( 22 ). We applied it to cells labeled as T-cells from all datasets to prevent dataset-specific cell type imbalance to bias the components.…”

Section: Methodsmentioning

confidence: 99%

Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration

Fouché

Chadoutaud

Delattre

et al. 2023

NAR Genomics and Bioinformatics

Self Cite

View full text Add to dashboard Cite

Data integration of single-cell RNA-seq (scRNA-seq) data describes the task of embedding datasets gathered from different sources or experiments into a common representation so that cells with similar types or states are embedded close to one another independently from their dataset of origin. Data integration is a crucial step in most scRNA-seq data analysis pipelines involving multiple batches. It improves data visualization, batch effect reduction, clustering, label transfer, and cell type inference. Many data integration tools have been proposed during the last decade, but a surge in the number of these methods has made it difficult to pick one for a given use case. Furthermore, these tools are provided as rigid pieces of software, making it hard to adapt them to various specific scenarios. In order to address both of these issues at once, we introduce the transmorph framework. It allows the user to engineer powerful data integration pipelines and is supported by a rich software ecosystem. We demonstrate transmorph usefulness by solving a variety of practical challenges on scRNA-seq datasets including joint datasets embedding, gene space integration, and transfer of cycle phase annotations. transmorph is provided as an open source python package.

show abstract

Section: Methodsmentioning

confidence: 99%

Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration

Fouché

Chadoutaud

Delattre

et al. 2023

NAR Genomics and Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our experiments we used the stabilized version of ICA [39] which is shown to be the optimal MF approach for reproducible analysis of transcriptomic data [40].…”

Section: Methodsmentioning

confidence: 99%

transmorph: a unifying computational framework for single-cell data integration

Fouché

Chadoutaud

Delattre

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Data integration of single-cell data describes the task of embedding datasets obtained from different sources into a common space, so that cells with similar cell type or state end up close from one another in this representation independently from their dataset of origin. Data integration is a crucial early step in most data analysis pipelines involving multiple batches and allows informative data visualization, batch effect reduction, high resolution clustering, accurate label transfer and cell type inference. Many tools have been proposed over the last decade to tackle data integration, and some of them are routinely used today within data analysis workflows. Despite constant endeavors to conduct exhaustive benchmarking studies, a recent surge in the number of these methods has made it difficult to choose one objectively for a given use case. Furthermore, these tools are generally provided as rigid pieces of software allowing little to no user agency on their internal parameters and algorithms, which makes it hard to adapt them to a variety of use cases. In an attempt to address both of these issues at once we introduce transmorph, an ambitious unifying framework for data integration. It allows building complex data integration pipelines by combining existing and original algorithmic modules, and is supported by a rich software ecosystem to easily benchmark modules, analyze and report results. We demonstrate transmorph capabilities and the value of its expressiveness by solving a variety of practical single-cell applications including supervised and unsupervised joint datasets embedding, RNA-seq integration in gene space and label transfer of cell cycle phase within cell cycle genes space. We provide transmorph as a free, open source and computationally efficient python library, with a particular effort to make it compatible with the other state-of-the-art tools and workflows.

show abstract

“…ICA is a method of separating independent source signals by applying statistical principles to the original observed signals. It plays a prominent role in blind source separation [ 23 , 24 ], feature recognition [ 25 ], and signal separation [ 26 ]. The source signal is estimated from the known mixed signal , and a certain linear relationship between X ( t ) and S ( t ) exists, which can be expressed as The principle underlying the ICA algorithm is that for a nonzero mean independent source signal S ( t ), the actual observed signal X ( t ) is obtained from X ( t ) = A S ( t ) after data preprocessing, and the unmixing matrix A is obtained, as detailed in the process shown in Fig 1 .…”

Section: Theoretical Basismentioning

confidence: 99%

Section: Ica Algorithmmentioning

confidence: 99%

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Zhu,

Qin,

Meng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation.

show abstract

BIODICA: a computational environment for Independent Component Analysis of omics data

Cited by 7 publications

References 16 publications

Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration

Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration

transmorph: a unifying computational framework for single-cell data integration

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model

Contact Info

Product

Resources

About