From 32843033429894acad9ee91f1c63ee7e10ae0660 Mon Sep 17 00:00:00 2001 From: tanlabcode <39063684+tanlabcode@users.noreply.github.com> Date: Sun, 7 Feb 2021 12:37:07 -0500 Subject: [PATCH 1/3] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 30555c3..cb295c5 100644 --- a/README.md +++ b/README.md @@ -52,7 +52,7 @@ This dashboard is to explore the integrative analysis results using GLUER. The m * the three windows are idependent from each other. * the differential analysis and downloading funciton of plotting data will make users to custermize their analysis needs. -More details of this Dashboard are found here. We have been keeping updating the functions of this dashboard. Please let me know if you have any suggestions for this dashboard. +More details of this Dashboard are found here. We have been keeping updating the functions of this dashboard. Please let us know if you have any suggestions for this dashboard.

@@ -64,4 +64,4 @@ More details of this Dashboard are found here. We have been keeping updating the Please feel free to contact Tao Peng (software.github@gmail.com) or Kai Tan (tank1@chop.edu) if you have any questions about the software. ## Reference -Peng, Tao, et al. "GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks" +Peng, Tao, et al. "GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks". bioRxiv From 0da14d414222a99adde4e4602b90fe06067b92df Mon Sep 17 00:00:00 2001 From: tanlabcode <39063684+tanlabcode@users.noreply.github.com> Date: Sun, 7 Feb 2021 12:40:08 -0500 Subject: [PATCH 2/3] Update PKG-INFO --- src/GLUER.egg-info/PKG-INFO | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/GLUER.egg-info/PKG-INFO b/src/GLUER.egg-info/PKG-INFO index 09307ee..0317e19 100644 --- a/src/GLUER.egg-info/PKG-INFO +++ b/src/GLUER.egg-info/PKG-INFO @@ -9,7 +9,7 @@ License: UNKNOWN Description:

GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks


- ## GLUER has been implemented in Python. + ## GLUER is implemented in Python. Single-cell omics assays such as RNA-Seq, ATAC-Seq and methylome sequencing have been developed to identify cell types and/or states in heterogeneous tissues. However, it is challenging to integrate these different types of single-cell omics data. Moreover, the advent of spatially resolved single-cell imaging data represent another challenge for integrative analysis with omics data. Here, we present a new algorithm, inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER), for integration of single-cell multi-omics data as well as imaging data. We tested GLUER using multiple datasets generated using multiomics data generated on the same single cells, which was taken as the ground truth. Our results demonstrate that GLUER has significantly improved performance in terms of the accuracy of matching cells with different data modalities, which in turn enhances downstream analyses such as clustering and trajectory inference. GLUER provides a principled analytical framework for studying the heterogeneity of cell populations using multi-omics and imaging data. From 907fd65b131158b6d6166ac0b8f2806963576435 Mon Sep 17 00:00:00 2001 From: tanlabcode <39063684+tanlabcode@users.noreply.github.com> Date: Sun, 7 Feb 2021 12:40:33 -0500 Subject: [PATCH 3/3] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cb295c5..d2686e6 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@

GLUER: integrative analysis of multi-omics and imaging data at single-cell resolution by deep neural networks


-## GLUER has been implemented in Python. +## GLUER is implemented in Python. Single-cell omics assays such as RNA-Seq, ATAC-Seq and methylome sequencing have been developed to identify cell types and/or states in heterogeneous tissues. However, it is challenging to integrate these different types of single-cell omics data. Moreover, the advent of spatially resolved single-cell imaging data represent another challenge for integrative analysis with omics data. Here, we present a new algorithm, inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER), for integration of single-cell multi-omics data as well as imaging data. We tested GLUER using multiple datasets generated using multiomics data generated on the same single cells, which was taken as the ground truth. Our results demonstrate that GLUER has significantly improved performance in terms of the accuracy of matching cells with different data modalities, which in turn enhances downstream analyses such as clustering and trajectory inference. GLUER provides a principled analytical framework for studying the heterogeneity of cell populations using multi-omics and imaging data.