Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis
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Abstract
Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.
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SciScore for 10.1101/2021.10.26.465846: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Its expression data were downloaded from GEO with code GSE65133 and its cell fractions were provided on the webpage of CIBERSORT [7]. CIBERSORTsuggested: (CIBERSORT, RRID:SCR_016955)Due to the original information loss of the processed single-cell expression profile, we just normalized raw counts of a certain gene with its maximum transcripts length obtained from BioMart [37]. BioMartsuggested: (biomaRt, RRID:SCR_019214)Next, to maintain the meaningful signature matrix, we decide to use MinMaxScaler() function provided by scikit-learn [39] to scale data into a range between 0 and 1. scikit-le…SciScore for 10.1101/2021.10.26.465846: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Its expression data were downloaded from GEO with code GSE65133 and its cell fractions were provided on the webpage of CIBERSORT [7]. CIBERSORTsuggested: (CIBERSORT, RRID:SCR_016955)Due to the original information loss of the processed single-cell expression profile, we just normalized raw counts of a certain gene with its maximum transcripts length obtained from BioMart [37]. BioMartsuggested: (biomaRt, RRID:SCR_019214)Next, to maintain the meaningful signature matrix, we decide to use MinMaxScaler() function provided by scikit-learn [39] to scale data into a range between 0 and 1. scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)For RNA-Sieve, we used the python package provided by the author and used the default settings to deconvolve data. pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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