31 research outputs found
Consensus clustering comparison of SABEC and K-means.
No full textIn the case of the Moignard dataset, SABEC is better than K-means at grouping cells by their FACS label. (A, B) For each pair of cells, we can count the number of times a pair of cells cluster together– a cumulative density function of these values, for K varying from 5 to 10, is shown in for SABEC (A) and K-means (B). Pairs of cells that are grouped together greater than 10% of the time and less than 90% of the time–represented by the vertical lines in (A) and (B)– are considered to be ambiguously clustered. (C) The proportion of cells that are ambiguously clustered (PAC) for SABEC and K-means is used to evaluate the robustness of the clustering approach. (D, E) Next we quantify the accuracy of the clusters, as compared to the labelled values (as per FACS). Specifically, this was done using the variable information (VI) (D) and the corrected Rand index (corrected Rand) (E). (F, G) The full consensus matrix, by cell type label according to FACS, for K = 6 are shown for SABEC (F) and K-means (G).</p
The Best Art Collection Possible: Australian Art Collectors and their Collections 1920-1940
No full textFor
the Degree of Doctor of Philosophy, submitted in total fulfillment for that
Degree to the Department of Archaeology and History, College of Humanities and
Social Sciences, La Trobe University, Victoria, Australia.<br
<i>In silico</i> validation of single cell analysis pipeline.
No full textIn order to estimate the accuracy of the new tools developed in this paper, each tool was evaluated against simulated datasets. (A-C) The kinetic parameter estimation method was tested against simulated data in which 90% of the data was randomly discarded, to simulate the loss of biological material through inefficient cDNA preparation. The known kinetic parameters and the estimated kinetic parameters were compared for Kon (A), Koff (B) and Kt (C). (D-F) Next, SABEC was tested against simulated datasets that had the same kinetic parameters as those estimated for the cell populations in the Moignard single cell dataset (124 simulated cells for each of the 5 cell populations)—SABEC was tested on 100 of such simulated datasets and the robustness of the clustering was measured by calculating the proportion of ambiguously clustered cells (PAC). This figure depicts a hierarchical clustering of the consensus matrices that come from (D) the dataset whose clustering had the worst PAC score, (E) a randomly selected dataset, and (F) the dataset whose clustering had the best PAC score. The coloured bars along the side and bottom represent the true class labels of each cell. (G-H) Next, the true positive and false positive rates were calculated for each proposed component of EPiK, the union of the MP and subset method, and the intersection of all three methods. For the MP and Subset methods, thresholds were set so the false positive rate would be approximately 2%– receiver operating characteristic (ROC) curves for these are found in S13 and S14.</p
Application of SABEC to hematopoietic stem cells and progenitor populations.
No full textSABEC was used to identify subpopulations of cells in the Moignard et al. dataset and to identify specific cells that do not cluster well with the other cells of the same type. (A) Hierarchical clustering was applied to the SABEC consensus matrix to reveal six subpopulations of HSC in subfigure. (Bi) The expected hematopoiesis differentiation tree is shown–the names of the cell populations are color-coded to match the vertical and horizontal bars in A. (Bii) We hypothesized a possible alternative differentiation tree, based on the clustering results. (C) Another application of this clustering method is to remove possible outliers, which may have arisen due to poorly sorted cells or extrinsic variability. Within each cell population (i.e. CLP (i), GMP (ii), HSC (iii), LMPP (iv) and PreM (v)), we sorted the cells by how often the cells are clustered with other cells from the same FACS label (% match). The vertical lines depict thresholds selected to approximately correspond to the region with the steepest slope– cells to the left are disregarded as outliers in the EPiK analysis.</p
Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling
No full textEstablishing the stem cell state: insights from regulatory network analysis of blood stem cell development
No full textProcessing, visualising and reconstructing network models from single-cell data.
No full textNew single-cell technologies readily permit gene expression profiling of thousands of cells at single-cell resolution. In this review, we will discuss methods for visualisation and interpretation of single-cell gene expression data, and the computational analysis needed to go from raw data to predictive executable models of gene regulatory network function. We will focus primarily on single-cell real-time quantitative PCR and RNA-sequencing data, but much of what we cover will also be relevant to other platforms, such as the mass cytometry technology for high-dimensional single-cell proteomics
Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.
No full textMany genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics
