Cell-type-specific co-expression inference from single cell RNA-sequencing data

Su, Chang; Xu, Zichun; Shan, Xinning; Cai, Biao; Zhao, Hongyu; Zhang, Jingfei

doi:10.1038/s41467-023-40503-7

Cited by 17 publications

(7 citation statements)

References 63 publications

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“…The challenge in this task stems from the Granger causal relationship or time-dependent correlation [109]. A GCN can be used to analyze genes with similar functions or uncover the characteristics of genes in some diseases [110]. GCN and GRN are two different tasks because correlation does not imply causal relation [111].…”

Section: Explanations Of Scevalmentioning

confidence: 99%

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Liu,

Li,

Wang

et al. 2023

Preprint

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Large Language Models (LLMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of LLMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing seven different single-cell LLMs with task-specific methods, we found that single-cell LLMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of LLMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell LLMs based on a proposedscEvalframework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell LLMs, and points to their constraints and avenues for future developments.

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Section: Explanations Of Scevalmentioning

confidence: 99%

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Liu,

Li,

Wang

et al. 2023

Preprint

Self Cite

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“…The accurate identification and comprehensive study of these cells necessitate sequencing more cells. Beyond elucidating the nuances of DE analysis, our results imply the impacts on other analytical methodologies, including cell classification 22 , eQTL mapping 23 , and the construction of co-expression networks 24 . For example, in a fetal brain study, a reduction in the number of cells sequenced has resulted in diminished accuracy in cell type classification 25 .…”

Section: Discussionmentioning

confidence: 93%

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Dai,

Zhang,

Chu

et al. 2024

Preprint

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Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

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“…Then, the data matrix is log-transformed (scanpy.pp.log1p). In the end, we selected the top 1000 genes ( 20 – 22 ) by the ranking variances of all samples (scanpy.pp.highly_variable_genes), which was performed variance calculation in Scanpy. More specifically, a normalized variance for each gene is computed.…”

Section: Methodsmentioning

confidence: 99%

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

Jiang,

Zhou,

Jiang

et al. 2024

Front. Oncol.

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Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

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Cell-type-specific co-expression inference from single cell RNA-sequencing data

Cited by 17 publications

References 63 publications

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning

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