Computational resources
As a lab generating huge amount of data with different modalities, we also dedicated to developing new computational approaches to analyze the data and interpret the results, providing data browsers for easy access to our analysis, as well as making easy-to-follow step-by-step online tutorials for data analysis. This page lists the relevant computational resources developed in the lab.
GitHubexternal page
Computational tools
external page simspec [1]
An R package implementing two data integration methods of scRNA-seq: the reference similarity spectrum (RSS) and cluster similarity spectrum (CSS)
external page VoxHunt [2]
An R package to map scRNA-seq data of brain organoids or developing brain tissues onto the Allen developing mouse brain ISH atlas
external page Pando [3]
An R package for gene regulatory network inference, given single-cell multiomic measurements (RNA and ATAC)
Data browsers (Shiny apps)
ShinyCortex [4]
Data visualization of several public Smart-seq2-based scRNA-seq data of developing human brain and brain organoids
scApeX [5]
Data visualization of scRNA-seq data of human, chimpanzee and rhesus macaque brain organoids as well as adult prefrontal cortex
GutTubeR [6]
Data visualization of scRNA-seq data of developing human gut-tube tissues, as well as iPSC-derived intestinal organoids
EyeSee4is [7]
Data visualization of the time-course 4i-based spatial protein map and single-cell genomic data of human retinal organoids
Online tutorials for data analysis
external page Bulk RNA-seq data preprocessing and analysis
external page scRNA-seq data analysis in R
external page Condition comparison analysis of scRNA-seq data in R
external page scRNA-ATAC multiome data analysis in R
References:
[1] He, Z., Brazovskaja, A., Ebert, S., et al. CSS: cluster similarity spectrum integration of single-cell genomics data. Genome Biol 21, 224 (2020).
[2] Fleck, J.S., Sanchís-Calleja, F., He, Z., et al. Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases. Cell Stem Cell 28, 1148-1159.e8 (2021).
[3] Fleck, J.S., Jansen, S.M.J., Wollny, D. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 365–372 (2023).
[4] Kageyama, J., Wollny, D., Treutlein, B., Camp, J.C. ShinyCortex: Exploring Single-Cell Transcriptome Data From the Developing Human Cortex. Front Neurosci 12, 315 (2018).
[5] Kanton, S., Boyle, M.J., He, Z. et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature 574, 418–422 (2019).
[6] Yu, Q., Kilik, U., Holloway, E.M., et al. Charting human development using a multi-endodermal organ atlas and organoid models. Cell 184, 3281-3298.e22 (2021).
[7] Wahle, P., Brancati, G., Harmel, C., He, Z., et al. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nat Biotechnol 41, 1765–1775 (2023)