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progressivehematopoiesis's Introduction

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:


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

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progressivehematopoiesis's Issues

Problems with sample and cell type assignment; Demuxlet metadata not matching

Hi Daniel,

I was trying to access your data from your cell report paper
https://figshare.com/projects/Single-cell_mapping_of_progressive_fetal-to-adult_transition_in_human_hematopoiesis/76143

I am interested in CD8 adult and newborns cells.
I downloaded the data "naive T cell single-cell RNA-seq, raw counts and annotation"

and I am following the R code:
scRNAseq_naiveTcells.Rmd

I am able to open the files and start processing them.
However, the metadata cell type classification in the code does not seem to match the one in the count files.
This is the code:

# Add Age
[email protected]$age <- "unknown"
[email protected]$age[grep("FS",Tcells$Sample)] <- "fetal"
[email protected]$age[grep("UCB",Tcells$Sample)] <- "cord"
[email protected]$age[grep("APB",Tcells$Sample)] <- "adult"

# Add Tcelltype
# This is dependent on both sample-assignment and 10X-lane,
# so I will need to add how samples were added to each lane
samples.4 <- c("FS3", "FS4", "FS5", "UCB1", "UCB2", "UCB5", "APB1", "APB2", "APB4", "APB5")
samples.8 <- c("FS1", "FS5", "UCB2", "UCB3", "UCB4", "UCB5", "APB2", "APB3", "APB5")
samples.48.4 <- c("FS1", "FS2", "UCB4", "UCB3", "APB3")
samples.48.8 <- c("FS3", "UCB1", "APB1", "APB4")
samples.48 <- c(samples.48.4,samples.48.8)

#For all the cells in each Lane, add the Tcelltype to a "Tcelltype" metadata
#iff the cell is assigned to a sample from its lane, and a singlet
[email protected]$Tcelltype <- NA
[email protected]$Tcelltype[(Tcells$Sample%in%samples.4) & (Tcells$Lane=="CD4")] <- "CD4"
[email protected]$Tcelltype[(Tcells$Sample%in%samples.48.4) & (Tcells$Lane=="CD4-8")] <- "CD4"
[email protected]$Tcelltype[(Tcells$Sample%in%samples.8) & (Tcells$Lane=="CD8")] <- "CD8"
[email protected]$Tcelltype[(Tcells$Sample%in%samples.48.8) & (Tcells$Lane=="CD4-8")] <- "CD8"

These are the sample names I am loading from Demuxlet files:
APB1801 APB1802 APB1803 APB1804 APB1805 FS1845 FS1848 FS1849 FS1850 FS1851
2964 2866 2850 3086 2945 3266 1690 2211 1377 3190
SLVR007 SLVR011 SLVR018 SLVR026 SLVR063
2758 3124 3267 2511 2048

Do you know what I am doing wrong?
Am I using the wrong files?

thanks,
Francesc

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