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. 2020 Sep 30;70(3):803–815. doi: 10.1007/s00262-020-02727-0

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Schematic representation of study design. Tumor tissue specimens from 13 CRC patients were used to sort highly pure CD33^high myeloid cells. Libraries were generated from sorted cells for RNA-Seq. Different bioinformatics tools were utilized for analyses and visualization of RNA-Seq data. The experimental flowchart of this study is shown in (a), and the workflow for RNA-Seq data pre-processing and analysis using various bioinformatics tools is shown in (b)