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Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNN^XMBD.

ABSTRACT: Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-DNN protocol to achieve robust cell type proportion estimation on the target dataset. We describe the preparation of calibrated samples from human blood samples. We then detail steps to train a dataset-specific deep neural network (DNN) model and cell type proportion estimation using the trained model. For complete details on the use and execution of this protocol, please refer to Lin et al. (2022).

SUBMITTER: Lin Y

PROVIDER: S-EPMC9356155 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Protocol to estimate cell type proportions from bulk RNA-seq using DAISM-DNNXMBD.

Lin Yating Y Wu Shangze S Xiao Xu X Zhao Jingbo J Wang Minshu M Li Haojun H Wang Kejia K Zhang Minwei M Zheng Frank F Yang Wenxian W Zhang Lei L Han Jiahuai J Yu Rongshan R

STAR protocols 20220731 3

Computational protocols for cell type deconvolution from bulk RNA-seq data have been used to understand cellular heterogeneity in disease-related samples, but their performance can be impacted by batch effect among datasets. Here, we present a DAISM-DNN protocol to achieve robust cell type proportion estimation on the target dataset. We describe the preparation of calibrated samples from human blood samples. We then detail steps to train a dataset-specific deep neural network (DNN) model and cel ...[more]

PMID: 35942344

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