Ying Qiao

Bio: Ying Qiao is an academic researcher from Xiamen University. The author has contributed to research in topics: Workflow & Segmentation. The author has an hindex of 3, co-authored 5 publications receiving 19 citations.

Comparison of high-throughput single-cell RNA sequencing data processing pipelines.

Abstract: With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.

Abstract: Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. %, which combine nuclear proteins and membrane/cytoplasm proteins as two channels of input. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

Abstract: With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry

Abstract: Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Artificial Intelligence in Cancer Research and Precision Medicine

Abstract: Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic

pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools

Abstract: We present pipeComp ( https://github.com/plger/pipeComp ), a flexible R framework for pipeline comparison handling interactions between analysis steps and relying on multi-level evaluation metrics. We apply it to the benchmark of single-cell RNA-sequencing analysis pipelines using simulated and real datasets with known cell identities, covering common methods of filtering, doublet detection, normalization, feature selection, denoising, dimensionality reduction, and clustering. pipeComp can easily integrate any other step, tool, or evaluation metric, allowing extensible benchmarks and easy applications to other fields, as we demonstrate through a study of the impact of removal of unwanted variation on differential expression analysis.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Abstract: Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.

Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

Abstract: Colon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used in the early diagnosis, follow‐up, and after the treatment process of colon cancer. Therefore, manually controlling a large number of medical images and their interpretation is a difficult process and consumes more time. In addition, the interpretation of data with traditional methods in this process can cause misdiagnosis due to human errors. For this reason, computer‐aided systems can be used in the diagnosis of colon cancer in order to both help experts and carry out the process more quickly and successfully. In this study, a novel method named by us, CNN‐based, MA_ColonNET is developed for detecting colon cancer image data. A 45‐layer model in MA_ColonNET has been used to classify. A success (accuracy) rate of 99.75% has been achieved by means of the new model. It is shown that the proposed model can detect colon cancer earlier. In this way, the treatment process can be carried out more successfully.