Single-cell mapping of progressive fetal-to-adult transition in human naive T cells

This repository is a companion to a study of human naive T cells, classical monocytes, and hematopoietic progenitors. Code is available here. Transcriptome data is available through GEO (GSE158493; RNAseq counts / microarray transformed intensities only) and figshare (link; counts / intensities & fully processed objects).

If you use this repository, we ask that you cite the paper:

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Bunis, D. G., Bronevetsky, Y., Krow-Lucal, E., Bhakta, N. R., Kim, C. C., Nerella, S., Jones, N., Mendoza, V. F., Bryson, Y. J., Gern, J. E., Rutishauser, R. L., Ye, C. J., Sirota, M., McCune, J. M., & Burt, T. D. (2021). Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells. Cell Reports, 34(1). https://doi.org/10.1016/j.celrep.2020.108573

To use this code

1. Clone this repository
2. Download raw data from GEO (GSE158493) or figshare link.
3. Extract and organize data in to your local copy of the repository with the below structure. (If you downloaded the pre-processed versions from figshare, place them in the root directory to use them directly with the comparison.Rmd.)
4. Use the .Rproj file to open an R project with this root directory as its base.

ProgressiveHematopoiesis/
|- bulkRNAseq_CD4naiveTcells.Rmd
|- comparison_between_datasets.Rmd
|- microarray-and-qRTPCR_CD4naiveTcells-and-monocytes.Rmd
|- ProgressiveHematopoiesis.Rmd
|- scRNAseq_HSPCs.Rmd
|- scRNAseq_naiveTcells.Rmd
\- bulkRNAseq_CD4s/
   (unzipped bulk naive CD4 RNAseq data)
   |- bulkCD4_counts.txt
\- HSPCs/
   (unzipped HSPCs scRNAseq raw and annotation data)
   \- cellranger_Raw/
      |- barcodes.tsv
      |- genes.tsv
      |- matrix.mtx
   \- Demuxlet/
      |- HSPC.best
\- Microarray_annotatedData/
   (unzipped microarray data)
   |- 17_medGen_longAnn_Tcells.csv
   |- 17_medGen_longAnn_Mono.csv
\- Tcells/
   (unzipped naive T cells scRNAseq raw and annotation data)
   \- cellranger_Raw/
      |- barcodes.tsv
      |- genes.tsv
      |- matrix.mtx
   \- Demuxlet/
      |- CD4.best
      |- CD4-8.best
      |- CD8.best