Abstract
Stefan Stanojevic, Lana Xia Garmire
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
europepmc
Confidence 96%
openalex
Confidence 95%
unpaywall
Confidence 95%
doaj
Confidence 92%
datacite
No local reference links have been materialized yet.
No local citing links have been materialized yet.
10.1038/s41586-021-03634-9
10.1038/s41586-021-03634-9
Unresolved referenced work
Kept as external metadata until matched
10.1038/nbt1385
10.1038/nbt1385
10.1126/science.aaw1219
10.1126/science.aaw1219
10.1038/s41587-020-0739-1
10.1038/s41587-020-0739-1
10.1101/2021.10.20.465066
10.1101/2021.10.20.465066
10.1126/science.aat5691
10.1126/science.aat5691
10.1073/pnas.1612826113
10.1073/pnas.1612826113
10.1038/s41586-019-1049-y
10.1038/s41586-019-1049-y
10.1038/s41592-021-01272-7
10.1038/s41592-021-01272-7
Computational challenges and opportunities in spatially resolved transcriptomic data analysis
10.1038/s41467-021-25557-9 · 2021
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Confidence 0%
10.1186/s13059-022-02653-7 · 2022
10.1038/nmeth.4634
10.1038/nmeth.4634
10.1038/nmeth.4636
10.1038/nmeth.4636
10.1038/s41592-019-0701-7
10.1038/s41592-019-0701-7
10.1101/2020.07.29.227207
10.1101/2020.07.29.227207
SOMDE: A scalable method for identifying spatially variable genes with self-organizing map
2021
10.1101/491472
10.1101/491472
10.1186/s13059-021-02286-2
10.1186/s13059-021-02286-2
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
10.1126/science.aaw1219 · 2019
Sur la sphere vide. Izv Akad Nauk SSSR
1934
10.1126/science.aaf2403
10.1126/science.aaf2403
Editorial Note to. In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus
10.1016/j.neuron.2017.05.009 · 2017
10.1101/2020.05.31.125658
10.1101/2020.05.31.125658
10.1038/s41592-021-01255-8
10.1038/s41592-021-01255-8
CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis
10.1186/s12859-021-04314-1 · 2021
10.1101/2021.06.15.448542
10.1101/2021.06.15.448542
10.1101/2021.07.08.451210
10.1101/2021.07.08.451210
10.1038/s41467-022-29439-6
10.1038/s41467-022-29439-6
10.1093/bioinformatics/btz914
10.1093/bioinformatics/btz914
10.1101/2022.01.14.476408
10.1101/2022.01.14.476408
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
10.1016/j.cell.2021.04.048
10.1016/j.cell.2021.04.048
Spatial transcriptomics at subspot resolution with BayesSpace
10.1038/s41587-021-00935-2 · 2021
10.1016/j.cell.2022.04.003
10.1016/j.cell.2022.04.003
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90
Unresolved referenced work
Kept as external metadata until matched
10.1186/s12864-020-06832-3
10.1186/s12864-020-06832-3
10.1101/2021.08.27.457741
10.1101/2021.08.27.457741 · doi-reference
10.1016/j.gpb.2022.11.013
10.1016/j.gpb.2022.11.013 · doi-reference
10.1038/s41586-019-1773-3
10.1038/s41586-019-1773-3 · doi-reference
Inferring spatial and signaling relationships between cells from single cell transcriptomic data
10.1038/s41467-020-15968-5 · doi-reference
10.1016/j.cell.2019.05.006
10.1016/j.cell.2019.05.006 · doi-reference
10.1093/nar/gkaa740
10.1093/nar/gkaa740 · doi-reference
10.1126/science.aau5324
10.1126/science.aau5324 · doi-reference
10.1038/s41592-018-0175-z
10.1038/s41592-018-0175-z · doi-reference
10.1093/bioinformatics/btab298
10.1093/bioinformatics/btab298 · doi-reference
10.1126/science.aan3235
10.1126/science.aan3235 · doi-reference
10.1038/nbt.3192
10.1038/nbt.3192 · doi-reference
DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data
10.3389/fgene.2021.636743 · doi-reference
Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation
10.1038/s41598-022-07685-4 · doi-reference
10.1007/978-3-319-24574-4_28
10.1007/978-3-319-24574-4_28 · doi-reference
10.1038/s41587-021-01075-3
10.1038/s41587-021-01075-3 · doi-reference
10.1038/s41598-020-75708-z
10.1038/s41598-020-75708-z · doi-reference
10.1101/2020.11.15.378125
10.1101/2020.11.15.378125 · doi-reference
10.1038/s41587-019-0113-3
10.1038/s41587-019-0113-3 · doi-reference
10.1038/s41593-020-00787-0
10.1038/s41593-020-00787-0 · doi-reference
10.1101/2022.02.20.481171
10.1101/2022.02.20.481171 · doi-reference
10.1038/s41592-018-0229-2
10.1038/s41592-018-0229-2 · doi-reference
deconvoluting spatial transcriptomics data through graph-based artificial intelligence
10.1093/bib/bbaa414 · doi-reference
10.1093/nar/gkab043
10.1093/nar/gkab043 · doi-reference
10.1093/nar/gkac150
10.1093/nar/gkac150 · doi-reference
10.1038/s42003-020-01247-y
10.1038/s42003-020-01247-y · doi-reference
10.1186/s13059-021-02362-7
10.1186/s13059-021-02362-7 · doi-reference
10.1038/s41587-021-01139-4
10.1038/s41587-021-01139-4 · doi-reference
10.1016/j.cell.2019.05.031
10.1016/j.cell.2019.05.031 · doi-reference
10.1016/j.cell.2020.09.056
10.1016/j.cell.2020.09.056 · doi-reference
10.1126/science.aaa6090
10.1126/science.aaa6090 · doi-reference
10.1101/2021.04.26.441459
10.1101/2021.04.26.441459 · doi-reference
10.1038/s41587-022-01272-8
10.1038/s41587-022-01272-8 · doi-reference
10.1038/s41592-021-01264-7
10.1038/s41592-021-01264-7 · doi-reference
10.1023/a:1010933404324
10.1023/a:1010933404324 · doi-reference
10.1101/2020.06.04.105700
10.1101/2020.06.04.105700 · doi-reference
10.1186/s13059-022-02663-5
10.1186/s13059-022-02663-5 · doi-reference
10.1101/2021.07.11.451750
10.1101/2021.07.11.451750 · doi-reference
GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data
10.1186/s13059-020-02214-w · doi-reference
Inference and analysis of cell-cell communication using Cell Chat
10.1038/s41467-021-21246-9 · doi-reference
10.1038/s41592-019-0667-5
10.1038/s41592-019-0667-5 · doi-reference