PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbour (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif outperform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

iCancer-Pred: A tool for identifying cancer and its type using DNA methylation

DNA methylation is an important epigenetics, which occurs in the early stages of tumor formation. And it also is of great significance to find the relationship between DNA methylation and cancer. This paper proposes a novel model, iCancer-Pred, to identify cancer and classify its types further. The datasets of DNA methylation information of 7 cancer types have been collected from The Cancer Genome Atlas (TCGA). The coefficient of variation firstly is used to reduce the number of features, and then the elastic network is applied to select important features. Finally, a fully connected neural network is constructed with these selected features. In predicting seven types of cancers, iCancer-Pred has achieved an overall accuracy of over 97% accuracy with 5-fold cross-validation. For the convenience of the application, a user-friendly web server: http://bioinfo.jcu.edu.cn/cancer or http://121.36.221.79/cancer/ is available. And the source codes are freely available for download at https://github.com/Huerhu/iCancer-Pred.     

Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer

Paired-end sequencing is emerging as a key technique for assessing genome rearrangements and structural variation on a genome-wide scale. This technique is particularly useful for detecting copy-neutral rearrangements, such as inversions and translocations, which are common in cancer and can produce novel fusion genes. We address the question of how much sequencing is required to detect rearrangement breakpoints and to localize them precisely using both theoretical models and simulation. We derive a formula for the probability that a fusion gene exists in a cancer genome given a collection of paired-end sequences from this genome. We use this formula to compute fusion gene probabilities in several breast cancer samples, and we find that we are able to accurately predict fusion genes in these samples with a relatively small number of fragments of large size. We further demonstrate how the ability to detect fusion genes depends on the distribution of gene lengths, and we evaluate how different parameters of a sequencing strategy impact breakpoint detection, breakpoint localization, and fusion gene detection, even in the presence of errors that suggest false rearrangements. These results will be useful in calibrating future cancer sequencing efforts, particularly large-scale studies of many cancer genomes that are enabled by next-generation sequencing technologies.     

利用RNA-seq技术分析棘腹蛙编码区微卫星特征

棘腹蛙Paa boulengeri的遗传研究和基因组信息比较匮乏,致使可有效利用的分子标记非常有限。以棘腹蛙RNA-seq高通量测序数据为基础进行微卫星分子标记的大规模发掘和特征分析,结果显示:在121.6 Mb的棘腹蛙转录组序列中发现微卫星位点3165个,包含于3034条Contig序列中。在筛选到的1~6碱基重复核心的微卫星中,单碱基重复核心的比例最高,之后为三碱基、二碱基、四碱基、六碱基和五碱基重复核心,分别占29.0%、25.2%、21.7%、10.0%、10.0%和3.0%。其中A/T、AC/GT、AGG/CCT、ACAT/ATCT、AAAAT/ATTTT和AAAAAG/CTTTTT分别是单碱基、二碱基、三碱基、四碱基、五碱基、六碱基重复类型中对应的优势重复单元。棘腹蛙编码区微卫星多为重复长度小于24 bp的短序列,长度大于24 bp的微卫星仅占总数的0.92%。对编码区微卫星的侧翼序列分析发现,微卫星侧翼序列的GC含量显著低于转录组整体GC含量,且在含有微卫星上下游侧翼序列的Contig中,71.9%的序列可以设计特异引物扩增出含有微卫星序列的位点。研究结果为棘腹蛙的遗传研究和分子系统地理学研究提供了丰富的序列信息和标记资源。