scholarly journals Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Drosophila Embryo Using Genetic Algorithm

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Shruti Gupta ◽  
Ajay Kumar Verma ◽  
Shandar Ahmad
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Single Cells ◽  
Drosophila Embryo ◽  
Main Challenge ◽  
Selection For

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in Drosophila embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap. Resulting gene selection was found to perform well and was ranked among top 10 in two of the three sub-challenges. However, the details of the method did not make it to the main challenge publication, due to an intricate aggregation ranking. In this work, we discuss the detailed implementation of GA and its post-challenge parameterization, with a view to identify potential areas where GA-based approaches of gene-set selection for topological association prediction may be improved, to be more effective. We believe this work provides additional insights into the feature-selection strategies and their relevance to single-cell similarity prediction and will form a strong addendum to the recently published work from the consortium.

scholarly journals Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1014
Author(s):  
Maryam Zand ◽  
Jianhua Ruan
Keyword(s):  
Single Cell ◽  
Gold Standard ◽  
Gene Selection ◽  
Single Cells ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Weighted Averaging ◽  
Marker Genes ◽  
Spatial Mapping ◽  
Successful Recovery

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

scholarly journals Spatial charting of single cell transcriptomes in tissues

2021 ◽  
Author(s):  
Nicholas Navin ◽  
Runmin Wei ◽  
Siyuan He ◽  
Shanshan Bai ◽  
Emi Sei ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Spatial Information ◽  
Ductal Carcinoma ◽  
Single Cells ◽  
Cell Types ◽  
Spatial Structures ◽  
Tissue Sections ◽  
Normal Mouse Brain

Single cell RNA sequencing (scRNA-seq) methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics (ST) assays can profile spatial regions in tissue sections, but do not have single cell genomic resolution. Here, we developed a computational approach called SChart, that combines these two datasets to achieve single cell spatial mapping of cell types, cell states and continuous phenotypes. We applied SChart to reconstruct cellular spatial structures in existing datasets from normal mouse brain and kidney tissues to validate our approach. We also performed scRNA-seq and ST experiments on two ductal carcinoma in situ (DCIS) tissues and applied SChart to identify subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data shows that SChart can accurately map single cells in diverse tissue types to resolve their spatial organization into cellular neighborhoods and tissue structures.

scholarly journals Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

2019 ◽  
Author(s):  
Jovan Tanevski ◽  
Thin Nguyen ◽  
Buu Truong ◽  
Nikos Karaiskos ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Gene Selection ◽  
Spatial Information ◽  
Spatial Clustering ◽  
Spatial Location ◽  
Drosophila Embryo ◽  
Rna Seq ◽  
Hybridization Data ◽  
Transcriptomics Data

AbstractSingle-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

scholarly journals Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1014
Author(s):  
Maryam Zand ◽  
Jianhua Ruan
Keyword(s):  
Single Cell ◽  
Gold Standard ◽  
Gene Selection ◽  
Single Cells ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Weighted Averaging ◽  
Marker Genes ◽  
Spatial Mapping ◽  
Successful Recovery

The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

scholarly journals scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

2021 ◽  
Author(s):  
Dongyuan Song ◽  
Kexin Aileen Li ◽  
Zachary Hemminger ◽  
Roy Wollman ◽  
Jingyi Jessica Li
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Spatial Information ◽  
Dimensional Space ◽  
Single Cells ◽  
High Sensitivity ◽  
Cell Types ◽  
Gene Profiling ◽  
Selection Methods ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

scholarly journals Unsupervised gene selection for predicting cell spatial positions in the Drosophila embryo

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 124
Author(s):  
Yang Chen ◽  
Disheng Mao ◽  
Yuping Zhang ◽  
Zhengqing Ouyang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Hierarchical Clustering ◽  
Gene Selection ◽  
Single Cells ◽  
Expression Patterns ◽  
Drosophila Embryo ◽  
Similarity Matrix ◽  
Gene Expressions ◽  
Driver Genes

Analyzing single cell RNA-seq data is important for deciphering the spatial relationships, expression patterns, and developmental processes of cells. Combining in situ hybridization-based gene expression atlas images, some works have successfully recovered spatial locations of cells in zebrafish and Drosophila embryos. In this article, we describe a highly ranked method in the DREAM Single Cell Transcriptomics Challenge for predicting cell positions in the Drosophila embryo. The method performs unsupervised feature extraction to select a small number of driver genes and then uses them to predict gene expression and spatial position of each individual cell. First, hierarchical clustering is used to select a subset of driver genes. Second, the similarity matrix of single cells in the bins of the reference atlas is computed. Based on the similarity matrix, the spatial positions of cells are then determined by hierarchical clustering. This method is evaluated on the cell positions and gene expressions in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “silver standard” suggests that our method is effective in reconstructing the cell spatial positions and gene expression patterns in tissues.

In-Situ Dual Beam (FIBSEM) Techniques for Probe Pad Deposition and Dielectric Integrity Inspection in 0.2 μm Technology DRAM Single Cells

1999 ◽  
Author(s):  
Gunnar Zimmermann ◽  
Richard Chapman
Keyword(s):  
Electron Beam ◽  
Single Cell ◽  
Single Cells ◽  
Ion Beam ◽  
Gate Oxide ◽  
Ion Milling ◽  
Dual Beam ◽  
Sem Imaging ◽  
Innovative Techniques

Abstract Dual beam FIBSEM systems invite the use of innovative techniques to localize IC fails both electrically and physically. For electrical localization, we present a quick and reliable in-situ FIBSEM technique to deposit probe pads with very low parasitic leakage (Ipara < 4E-11A at 3V). The probe pads were Pt, deposited with ion beam assistance, on top of highly insulating SiOx, deposited with electron beam assistance. The buried plate (n-Band), p-well, wordline and bitline of a failing and a good 0.2 μm technology DRAM single cell were contacted. Both cells shared the same wordline for direct comparison of cell characteristics. Through this technique we electrically isolated the fail to a single cell by detecting leakage between the polysilicon wordline gate and the cell diffusion. For physical localization, we present a completely in-situ FIBSEM technique that combines ion milling, XeF2 staining and SEM imaging. With this technique, the electrically isolated fail was found to be a hole in the gate oxide at the bad cell.

scholarly journals Label-Free and Quantitative Dry Mass Monitoring for Single Cells during In Situ Culture

Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1635
Author(s):  
Ya Su ◽  
Rongxin Fu ◽  
Wenli Du ◽  
Han Yang ◽  
Li Ma ◽  
...  
Keyword(s):  
Cell Growth ◽  
Single Cell ◽  
Hela Cells ◽  
Quantitative Measurement ◽  
Single Cells ◽  
Dry Mass ◽  
Quantitative Detection ◽  
Label Free ◽  
Mass Sensitivity

Quantitative measurement of single cells can provide in-depth information about cell morphology and metabolism. However, current live-cell imaging techniques have a lack of quantitative detection ability. Herein, we proposed a label-free and quantitative multichannel wide-field interferometric imaging (MWII) technique with femtogram dry mass sensitivity to monitor single-cell metabolism long-term in situ culture. We demonstrated that MWII could reveal the intrinsic status of cells despite fluctuating culture conditions with 3.48 nm optical path difference sensitivity, 0.97 fg dry mass sensitivity and 2.4% average maximum relative change (maximum change/average) in dry mass. Utilizing the MWII system, different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before the M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. Furthermore, HeLa cells were treated with Gemcitabine to reveal the relationship between single-cell heterogeneity and chemotherapeutic efficacy. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. In conclusion, MWII was presented as a technique for single-cell dry mass quantitative measurement, which had significant potential applications for cell growth dynamics research, cell subtype analysis, cell health characterization, medication guidance and adjuvant drug development.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

Sign in / Sign up

Export Citation Format

Share Document