75 research outputs found
Cytokine activity estimation and receptor abundance approximation with multimodal scRNA-seq/CITE-seq data.
No full textSingle-cell RNA-sequencing (scRNA-seq) data enables individual cell resolution quantification of messenger RNA. While revolutionary for revealing key cell type and cell phenotype-specific heterogeneity, scRNA-seq data has important statistical challenges. First, scRNA-seq data is usually more sparse and second, it is more variable than bulk transcriptomics data. Given these challenges, more intuitive and interpretive statistical and computational methods are needed to develop appropriate solutions. Here, we detail the development of three techniques to perform receptor abundance estimation and cytokine activity estimation for scRNA-seq data. While SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) methods address the sparsity constraints of scRNA-seq data via dimensionality reduction and a thresholding mechanism and co-expression analysis using joint scRNA-seq/CITE-seq data, respectively, SCAPE (Single cell transcriptomics-level Cytokine Activity Prediction and Estimation) aims to leverage a cytokine signaling activity database via a modified gene set testing approach to accommodate negative weights. Our approaches work well in practice and aim to provide more interpretive solutions to statistical challenges of scRNA-seq data. Furthermore, our methods have the potential to be integrated in bioinformatics pipelines for the tasks of cell type and cell state identification
Interventions for refugee integration in cities
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 83-92).In recent years, conflict and climate change around the world are not only displacing people at an unprecedented rate but also increasing the years of their displacement. With over 25.4 million refugees globally, the highest number in history, countries are forced to change how they respond to this crisis. In most cases, housing refugees in temporary camps is not sustainable over a long, and a majority of the global refugees end up living in urban areas. Since cities are starting to play an essential role in welcoming this new population, it is imperative for the planning field to understand how the built environment impacts refugee integration. Successful integration into host society is not the sole responsibility of a refugee but rather a process that involves both the refugee and the host community. This thesis investigates factors that affect refugee integration and examines how they play out spatially on a local scale through a case study of the Roxbury neighborhood in Boston, Massachusetts. The research analysis and case study affirm the influence of place in the refugee experience of community and belonging. Just as displacement is a place-based trauma, refugee resettlement must be approached as a place-based intervention. This thesis highlights the role of planners by outlining the spatial implications of successful integration in addition to introducing a multidisciplinary approach that can empower refugees to not only successfully integrate but to have agency in their new homes.by Azka Mohyuddin.M.C.P.M.C.P. Massachusetts Institute of Technology, Department of Urban Studies and Plannin
Case study of LDA
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 71-73).City development authorities are one of the key institutions in urban development and planning in South Asian cities. Pakistan and India share a history and have experienced the similar trend of Town Improvement Trusts established by the British transforming into Development Authorities. Both these forms of institutions had a similar mandate --to improve the living standards in the city through planned development. Development authorities, in particular, were envisioned to undertake comprehensive and integrated master planning in the face of rapid urbanization that its predecessor failed to do so because of its institutional set up as a Trust. In this thesis, I focus on one such urban development institution in Lahore, Pakistan, namely the Lahore Development Authority (LDA) which has come under immense criticism in recent years. In order to understand the urban sprawl of Lahore and the complementary planned development, one needs to understand the institutions that are propelling this form of urban planning. I aim to understand the unequal development in Lahore through the lens of an institutional framework.The premise of my analysis is that even though the forms of institutions that come about and the way they evolve over time are influenced by the broader political and economic trends, it is the urban development institutions that dictate what kind of policies under its purview are produced, hence affecting the urban form. I argue that LDA was a continuation of the Lahore Improvement Trust in many ways, with a supposedly more comprehensive approach to planning, and it faced similar challenges as its predecessor and failed to achieve one of the objectives this parallel institutional structure set out to achieve: providing housing for the low-income groups. In my analysis, I highlight the role of legislation and political influence on LDA's operations. Political leadership and influence differentiate it from LIT and it can be its greatest strength if it is leveraged in the right way.In order to understand LDA's challenges and how these can be overcome, I analyse the following in this thesis: 1) why was LDA established and to what extent it was a continuation of its predecessor 2) how has LDA's policies evolved over the years and why, and 3) what are the challenges to cater to low income population for LDA and what are the ways in which it can achieve them?by Azka Shoaib.M.C.P.M.C.P. Massachusetts Institute of Technology, Department of Urban Studies and Plannin
STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring.
No full textThe accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on six joint scRNA-seq/CITE-seq datasets that represent four human and mouse tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model
Fig 13 -
No full textGene set scoring versus thresholding sensitivity analysis examining frequency of receptors with highest average rank correlations between CITE-seq data and abundance values estimated using STREAK (i.e., estimation via gene set scoring followed by thresholding) or VAM (i.e., estimation using just gene set scoring) evaluated using the 5-fold cross-validation approach with the indicated training data ranging from 1,000 to 12,000 cells for the Hao data (Fig 13A) 1,000 to 10,000 cells for the Unterman data (Fig 13B) and 1,000 to 1,682 cells for the MALT data (Fig 13C).</p
S13 Fig -
No full textAverage rank correlations between CITE-seq data and receptor abundance values estimated using STREAK and comparative methods as evaluated using the 5-fold cross-validation approach for training data consisting of 12,000 cells from the Unterman dataset. (TIFF)</p
S8 Fig -
No full textTraining data sensitivity analysis examining frequency of receptors with highest average rank correlations between CITE-seq data and abundance values estimated using STREAK and cTP-net via the cross-training strategy. (TIFF)</p
S4 Fig -
No full textCorrelation versus correlation scatter plots for the MPEM data. Each point corresponds to a receptor from a sample size of 46 receptors. (TIFF)</p
S1 Fig -
No full textCorrelation versus correlation scatter plots for the PBMC Unterman data. Each point corresponds to a receptor from a sample size of 167 receptors. (TIFF)</p
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