Effect of pore pressure magnitude on the frictional properties and permeability evolution of fractures in schist

Li, Ziyan; Elsworth, Derek; Wang, Chaoyi

doi:10.1007/s40948-022-00504-0

Cited by 1 publication

(3 citation statements)

References 39 publications

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“…Seven publicly available scRNA-seq datasets were collected, comprising of 185 NSCLC human primary tumor samples in total. Datasets GSE131907 28,39 , GSE136246 40,41 , GSE148071 42,43 , GSE153935 44,45 , and KU_loom (https://gbiomed.kuleuven.be/scRNAseq-NSCLC) 46,47 were used to create a large reference dataset, whereas datasets GSE127465 27,48 and GSE119911 49,50 served as validation. Details on samples included in the analysis are summarized in Tables 1, 2.…”

Section: Methodsmentioning

confidence: 99%

“…Validation dataset analysis. Datasets GSE127465 27,48 and GSE119911 49,50 acquired from NCBI GEO were processed individually with the previously described workflow for clustering analysis (Table 3). For dataset GSE127465 27,48 identified clusters were annotated based on metadata provided by the authors, whereas clusters of dataset GSE119911 49,50 were annotated manually based on canonical markers (Fig.…”

Section: Integration Of Reference Datasetsmentioning

confidence: 99%

“…2). Due to a small number of QC-passed cells from dataset GSE119911 49,50 (1359 cells), Seurat anchor-based integration of cells from the two validation datasets was conducted to form a single validation dataset (see "Integration of reference datasets"). The integrated validation dataset included 39511 cells and was subjected to clustering analysis (no.…”

Section: Integration Of Reference Datasetsmentioning

confidence: 99%

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An integrated single-cell transcriptomic dataset for non-small cell lung cancer

Prazanowska

Lim

2023

Sci Data

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As single-cell RNA sequencing (scRNA-seq) has emerged as a great tool for studying cellular heterogeneity within the past decade, the number of available scRNA-seq datasets also rapidly increased. However, reuse of such data is often problematic due to a small cohort size, limited cell types, and insufficient information on cell type classification. Here, we present a large integrated scRNA-seq dataset containing 224,611 cells from human primary non-small cell lung cancer (NSCLC) tumors. Using publicly available resources, we pre-processed and integrated seven independent scRNA-seq datasets using an anchor-based approach, with five datasets utilized as reference and the remaining two, as validation. We created two levels of annotation based on cell type-specific markers conserved across the datasets. To demonstrate usability of the integrated dataset, we created annotation predictions for the two validation datasets using our integrated reference. Additionally, we conducted a trajectory analysis on subsets of T cells and lung cancer cells. This integrated data may serve as a resource for studying NSCLC transcriptome at the single cell level.

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