MOTIVATION Although single-cell sequencing is becoming more widely available, many tissue samples such as intracranial aneurysms are both fibrous and minute, and therefore not easily dissociated into single cells. To account for the cell type heterogeneity in such tissues therefore requires a computational method. We present a computational deconvolution method, deconvSeq, for sequencing data (RNA and bisulfite) obtained from bulk tissue. This method can also be applied to single-cell RNA sequencing data. RESULTS DeconvSeq utilizes a generalized linear model to model effects of tissue type on feature quantification, which is specific to the data structure of the sequencing type used. Estimated model coefficients can then be used to predict the cell type mixture within a tissue. Predicted cell type mixtures were validated against actual cell counts in whole blood samples. Using this method, we obtained a mean correlation of 0.998 (95% CI 0.995-0.999) from the RNA sequencing data of 35 whole blood samples and 0.95 (95% CI 0.91-0.98) from the reduced representation bisulfite sequencing data from 35 whole blood samples. Using symmetric balances to obtain the correlation between compositional parts, we found that the lowest correlation occurred for monocytes for both RNA and bisulfite sequencing. Comparison with other methods of decomposition such as deconRNAseq, CIBERSORT, MuSiC and EpiDISH showed that deconvSeq is able to achieve good prediction using mean correlation with far fewer genes or CpG sites in the signature set. AVAILABILITY AND IMPLEMENTATION Software implementing deconvSeq is available at https://github.com/rosedu1/deconvSeq. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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deconvSeq：在测序数据中对细胞混合物分布进行反卷积。

动机尽管单细胞测序的应用越来越广泛，但许多组织样品（如颅内动脉瘤）既有纤维又很细，因此不容易分解成单细胞。因此，要考虑此类组织中细胞类型的异质性，需要一种计算方法。我们提出了一种计算解卷积方法deconvSeq，用于对从大块组织获得的数据（RNA和亚硫酸氢盐）进行测序。该方法也可以应用于单细胞RNA测序数据。结果DeconvSeq利用广义线性模型对组织类型对特征量化的影响进行建模，该特征特定于所用测序类型的数据结构。然后可以将估计的模型系数用于预测组织内的细胞类型混合物。针对全血样本中的实际细胞数验证了预测的细胞类型混合物。使用此方法，我们从35个全血样品的RNA测序数据中获得了0.998（95％CI 0.995-0.999）的均值相关性，以及从35个全血的还原亚硫酸氢盐测序数据中获得了0.95（95％CI 0.91-0.98）的均值相关性样品。使用对称平衡获得组成部分之间的相关性，我们发现最低的相关性发生在RNA和亚硫酸氢盐测序的单核细胞中。与其他分解方法（例如deconRNAseq，CIBERSORT，MuSiC和EpiDISH）的比较表明，deconvSeq能够使用均值相关性与签名集中少得多的基因或CpG位点实现良好的预测。可用性和实现可以在https：// github上获得实现deconvSeq的软件。com / rosedu1 / deconvSeq。补充信息补充数据可从Bioinformatics在线获得。