Selecting the most appropriate time points to profile in high-throughput studies

Kleyman, Michael; Sefer, Emre; Nicola, Teodora; Espinoza, Celia R.; Chhabra, Divya; Hagood, James S.; Kaminski, Naftali; Ambalavanan, Namasivayam; Bar‐Joseph, Ziv

doi:10.7554/elife.18541

Cited by 28 publications

(31 citation statements)

References 62 publications

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“…To address this issue, we adapted an algorithm we previously developed for the task of selecting optimal time points to profile in scRNA-seq studies using bulk data containing gene subsets. Our algorithm, Time Point Selection (TPS) (Kleyman et al, 2017), profiles a small set of selected genes sampled at a high rate. These are represented using splines and a combinatorial search is applied to select a subset of 8 suitable points so that combined, selected points provide enough information to reconstruct the values for all genes across all time points (including those not selected, Figure 1C).…”

Section: Selecting the Most Appropriate Time Points To Profile In A Smentioning

confidence: 99%

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

Hurley

Ding

Villacorta‐Martin

et al. 2019

Preprint

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Alveolar epithelial type 2 cells (AEC2s) are the facultative progenitors responsible for maintaining lung alveoli throughout life, yet are difficult to access from patients for biomedical research or lung regeneration applications. Here we engineer AEC2s from human induced pluripotent stem cells (iPSCs) in vitro and use single cell RNA sequencing (scRNA-seq) to profile the detailed kinetics of their differentiation over time. We focus on both the desired target cells as well as those that appear to diverge to alternative endodermal fates. By combining scRNA-seq with lentiviral barcoding to trace differentiating clones, we reveal the bifurcating cell fate trajectories followed as primordial lung progenitors differentiate into mature AEC2s. We define the global transcriptomic signatures of primary developing human AEC2s from fetal through adult stages in order to identify the subset of in vitro differentiating cells that appear to recapitulate the path of in vivo development. In addition, we develop computational methods based on Continuous State Hidden Markov Models (CSHMM) to identify the precise timing and type of signals, such as over-exuberant Wnt responses, that induce some early multipotent NKX2-1+ progenitors to lose lung fate as they clonally diverge into a variety of non-lung endodermal lineages. Finally, we find that this initial developmental plasticity is regulatable via Wnt modulation, and subsides over time, ultimately resulting in iPSC-derived AEC2s that exhibit a stable phenotype and nearly limitless self-renewal capacity in vitro. Our methods and computational approaches can be widely applied to study and control directed differentiation, producing an inexhaustible supply of mature lineages, exemplified here by the generation of AEC2s.

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Section: Selecting the Most Appropriate Time Points To Profile In A Smentioning

confidence: 99%

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

Hurley

Ding

Villacorta‐Martin

et al. 2019

Preprint

Self Cite

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“…One possible strategy would be to try to select a set of time points that are best able to distinguish the shape of the curves. For this we select a criteria very similar to that presented by Kleyman et al [2017] in that we want to minimise the L 2 -distance between the curve generated by sampling at all the time points, and the curve generated by sampling only at a subset of time points. However, Kleyman minimises this distance over all curves in the training set -instead, we use the training set to generate a probability density over the space of curves, and then minimise the expected distance.…”

Section: Defining Good Time Pointsmentioning

confidence: 99%

“…The recent Time Point Selection (TPS) method developed by Kleyman et al [2017] is a substantial improvement in that it does not depend on this sequential experimental design strategy, and it considers the full shape of the gene expression profile, a strategy also used by NITPicker ( Figure 1D). However, it has three downsides that might limit its use in practice.…”

Section: Introductionmentioning

confidence: 99%

“…First, it uses a greedy search strategy for finding time points, which might be prone to finding local optima rather than global optima. In NITPicker, we identify that an optimisation problem described by Kleyman et al [2017] is in fact the same as a simpler problem in computer science -finding the shortest path through a directed acyclic graph -which can be solved directly by a dynamic programming algorithm (specifically, a modified Viterbi algorithm).…”

Section: Introductionmentioning

confidence: 99%

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Selection of time points for costly experiments: a comparison between human intuition and computer-aided experimental design

Ezer

Keir

2018

Preprint

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Motivation:The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do not systematically bias research outcomes. At the same time, making optimal design decisions can produce results leading to statistically stronger conclusions. Deciding where and when to sample are among the most critical aspects of many experimental designs; for example, we might have to choose the time points at which to measure some quantity in a time series experiment. Choosing times which are too far apart could result in missing short bursts of activity. On the other hand, there may be time points which provide very little information regarding the overall behaviour of the quantity in question. Results: In this study, we design a survey to analyse how biologists use previous research outcomes to inform their decisions about which time points to sample in subsequent experiments. We then determine how the choice of time points affects the type of perturbations in gene expression that can be observed. Finally, we present our main result: NITPicker, a computational strategy for selecting optimal time points (or spatial points along a single axis), that eliminates some of the biases caused by human decision-making while maximising information about the shape of the underlying curves, utilising ideas from the field of functional data analysis.

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“…General purpose clustering methods may be able to detect synchronized temporal changes over time but cannot recognize that two entities have the same temporal profile if one is delayed or lagged after the other. In addition, in many cases the timepoints in a biological study are not uniformly distributed over time, and the selection of timepoints is an important aspect of the experimental design [5]. The duration between timepoints in irregular time series affects the similarity of temporal profiles, especially when allowing lags among the clustered entities.…”

Section: Introductionmentioning

confidence: 99%

Lag Penalized Weighted Correlation for Time Series Clustering

Chandereng

Gitter

2018

Preprint

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Motivation:The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. Results: We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns pairs of time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. Availability: The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT

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Selecting the most appropriate time points to profile in high-throughput studies

Cited by 28 publications

References 62 publications

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

Single-cell time-series mapping of cell fate trajectories reveals an expanded developmental potential for human PSC-derived distal lung progenitors

Selection of time points for costly experiments: a comparison between human intuition and computer-aided experimental design

Lag Penalized Weighted Correlation for Time Series Clustering

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