scholarly journals Automated Contamination Detection in Single-Cell Sequencing

2015 ◽  
Author(s):  
Markus Lux ◽  
Barbara Hammer ◽  
Alexander Sczyrba
Keyword(s):  
Single Cell ◽  
Automated Detection ◽  
Single Cell Sequencing ◽  
Machine Learning Methods ◽  
Automatic Clustering ◽  
Ill Posed ◽  
Foreign Dna ◽  
Subspace Projections ◽  
Oligonucleotide Signatures ◽  
Modern Machine

Novel methods for the sequencing of single-cell DNA offer tremendous opportunities. However, many techniques are still in their infancy and a major obstacle is given by sample contamination with foreign DNA. In this contribution, we present a pipeline that allows for fast, automated detection of contaminated samples by the use of modern machine learning methods. First, a vectorial representation of the genomic data is obtained using oligonucleotide signatures. Using non-linear subspace projections, data is transformed to be suitable for automatic clustering. This allows for the detection of one vs. more genomes (clusters) in a sample. As clustering is an ill-posed problem, the pipeline relies on a thorough choice of all involved methods and parameters. We give an overview of the problem and evaluate techniques suitable for this task.

Indirect Fourier transform in the context of statistical inference

2016 ◽  
Vol 72 (5) ◽  
pp. 557-569 ◽  
Author(s):  
Michael Muthig ◽  
Sylvain Prévost ◽  
Reinhold Orglmeister ◽  
Michael Gradzielski
Keyword(s):  
Fourier Transform ◽  
Statistical Inference ◽  
Structural Information ◽  
Distance Distribution Function ◽  
Machine Learning Methods ◽  
Angle Scattering ◽  
Ill Posed ◽  
Pair Distance Distribution Function ◽  
Modern Machine

Inferring structural information from the intensity of a small-angle scattering (SAS) experiment is an ill-posed inverse problem. Thus, the determination of a solution is in general non-trivial. In this work, the indirect Fourier transform (IFT), which determines the pair distance distribution function from the intensity and hence yields structural information, is discussed within two different statistical inference approaches, namely a frequentist one and a Bayesian one, in order to determine a solution objectively From the frequentist approach the cross-validation method is obtained as a good practical objective function for selecting an IFT solution. Moreover, modern machine learning methods are employed to suppress oscillatory behaviour of the solution, hence extracting only meaningful features of the solution. By comparing the results yielded by the different methods presented here, the reliability of the outcome can be improved and thus the approach should enable more reliable information to be deduced from SAS experiments.

scholarly journals From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2278
Author(s):  
Afshin Derakhshani ◽  
Zeinab Rostami ◽  
Hossein Safarpour ◽  
Mahdi Abdoli Shadbad ◽  
Niloufar Sadat Nourbakhsh ◽  
...  
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cancer Development ◽  
Immune Checkpoints ◽  
Single Cell Sequencing ◽  
Increased Risk

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

Single-cell sequencing technology in tumor research

2021 ◽  
Author(s):  
Xue Bai ◽  
Yuxuan Li ◽  
Xuemei Zeng ◽  
Qiang Zhao ◽  
Zhiwei Zhang
Keyword(s):  
Single Cell ◽  
Sequencing Technology ◽  
Single Cell Sequencing ◽  
Tumor Research

Single-Cell Sequencing in Precision Medicine

2019 ◽  
pp. 237-252 ◽  
Author(s):  
Julia E. Wiedmeier ◽  
Pawan Noel ◽  
Wei Lin ◽  
Daniel D. Von Hoff ◽  
Haiyong Han
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Single Cell Sequencing
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