Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data

Zhang, Yaru; Ma, Yunlong; Huang, Yen-Yu; Jiang, Qi; Zhou, Meng; Su, Jianzhong

doi:10.1016/j.csbj.2020.10.007

Cited by 58 publications

(42 citation statements)

References 60 publications

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“…To rank methods, the overall scores of the methods were calculated through aggregating accuracy, stability, and computing cost ( Zhang et al, 2020 ). After k-means clustering, we used the known cell populations to calculate the ARI, NMI, and Silhouette scores for simulated data and real data, respectively.…”

Section: Methodsmentioning

confidence: 99%

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

et al. 2021

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Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and sparse data. Dimension reduction is an important step in downstream analysis of scRNA-seq. Therefore, several dimension reduction methods have been developed. We developed a strategy to evaluate the stability, accuracy, and computing cost of 10 dimensionality reduction methods using 30 simulation datasets and five real datasets. Additionally, we investigated the sensitivity of all the methods to hyperparameter tuning and gave users appropriate suggestions. We found that t-distributed stochastic neighbor embedding (t-SNE) yielded the best overall performance with the highest accuracy and computing cost. Meanwhile, uniform manifold approximation and projection (UMAP) exhibited the highest stability, as well as moderate accuracy and the second highest computing cost. UMAP well preserves the original cohesion and separation of cell populations. In addition, it is worth noting that users need to set the hyperparameters according to the specific situation before using the dimensionality reduction methods based on non-linear model and neural network.

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Section: Methodsmentioning

confidence: 99%

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

et al. 2021

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“…We next compared the three methods against consensus—gene sets that were identified as up or down by at least two methods. Though consensus is not ground truth, it is more reliable than individual methods, as was shown in many benchmark studies 30,31 .…”

Section: Resultsmentioning

confidence: 97%

Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

Noureen

Chen

et al. 2021

Preprint

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Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

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“…To compute and analyze pathway scores in malignant cholangiocarcinoma cells, we used scTPA, which is a web tool for single-cell analysis of activated pathways (http://sctpa.biodata.cn:8080/index.html) (27,28). The malignant cell expression matrix was extracted by sample origins in malignancy and cholangiocyte cluster, and then expression matrix was uploaded online.…”

Section: Analysis Of Pathway Changes In Malignant Cholangiocarcinomamentioning

confidence: 99%

PNOC Expressed by B Cells in Cholangiocarcinoma Was Survival Related and LAIR2 Could Be a T Cell Exhaustion Biomarker in Tumor Microenvironment: Characterization of Immune Microenvironment Combining Single-Cell and Bulk Sequencing Technology

Chen

Yan

et al. 2021

Front. Immunol.

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BackgroundCholangiocarcinoma was a highly malignant liver cancer with poor prognosis, and immune infiltration status was considered an important factor in response to immunotherapy. In this investigation, we tried to locate immune infiltration related genes of cholangiocarcinoma through combination of bulk-sequencing and single-cell sequencing technology.MethodsSingle sample gene set enrichment analysis was used to annotate immune infiltration status in datasets of TCGA CHOL, GSE32225, and GSE26566. Differentially expressed genes between high- and low-infiltrated groups in TCGA dataset were yielded and further compressed in other two datasets through backward stepwise regression in R environment. Single-cell sequencing data of GSE138709 was loaded by Seurat software and was used to examined the expression of infiltration-related gene set. Pathway changes in malignant cell populations were analyzed through scTPA web tool.ResultsThere were 43 genes differentially expressed between high- and low-immune infiltrated patients, and after further compression, PNOC and LAIR2 were significantly correlated with high immune infiltration status in cholangiocarcinoma. Through analysis of single-cell sequencing data, PNOC was mainly expressed by infiltrated B cells in tumor microenvironment, while LAIR2 was expressed by Treg cells and partial GZMB+ CD8 T cells, which were survival related and increased in tumor tissues. High B cell infiltration levels were related to better overall survival. Also, malignant cell populations demonstrated functionally different roles in tumor progression.ConclusionPNOC and LAIR2 were biomarkers for immune infiltration evaluation in cholangiocarcinoma. PNOC, expressed by B cells, could predict better survival of patients, while LAIR2 was a potential marker for exhaustive T cell populations, correlating with worse survival of patients.

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Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data

Cited by 58 publications

References 60 publications

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer

PNOC Expressed by B Cells in Cholangiocarcinoma Was Survival Related and LAIR2 Could Be a T Cell Exhaustion Biomarker in Tumor Microenvironment: Characterization of Immune Microenvironment Combining Single-Cell and Bulk Sequencing Technology

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