Peng: 3-9yo macaque retina
Last updated: 2022-10-26
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Knit directory: Williams_Retina_scRNA_IMPG2_workflowr/
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Data acquisition
Single-cell RNA-seq data from the 3-9yo macaque retina corresponding to Peng et al 2019 were retrieved from the Broad single-cell portal. Below we additionally annotate the cluster numbers into named cell types based on the information provided in the supplied metadata file
Data were downloaded as follows
curl "https://singlecell.broadinstitute.org/single_cell/api/v1/bulk_download/generate_curl_config?accessions=SCP212&auth_code=Z2Yow3ap&directory=all&context=study" -o cfg.txt; curl -K cfg.txt && rm cfg.$Analysis of IMPG1 and IMPG2 expression in retinal cell types
Filter expression matrices for IMPG1 and IMPG2 expression
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_AC_expression2.txt > Macaque_fov_AC_expression2_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_BC_expression.txt > Macaque_fov_BC_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_EpiImmune_expression.txt > Macaque_fov_EpiImmune_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_HC_expression.txt > Macaque_fov_HC_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_PR_expression.txt > Macaque_fov_PR_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_fov_RGC_expression.txt > Macaque_fov_RGC_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_AC_expression.txt > Macaque_per_AC_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_BC_expression2.txt > Macaque_per_BC_expression2_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_EpiImmune_expression.txt > Macaque_per_EpiImmune_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_HC_expression.txt > Macaque_per_HC_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_PR_expression.txt > Macaque_per_PR_expression_IMPG1_IMPG2.txt
grep -E 'GENE|IMPG1|IMPG2' Macaque_per_RGC_expression.txt > Macaque_per_RGC_expression_IMPG1_IMPG2.txtLoad libraries
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(cowplot))Import metadata
Categories are modeled after Fig. 1C in the paper, where clusters are summarized by broader categories
meta <- read_csv("Macaque_NN_RGC_AC_BC_HC_PR_metadata_3.txt", col_names=c("CellID","Cluster","Subcluster"), skip=2) %>%
mutate(CellID = str_replace_all(CellID,"-","."))Import expression data
Macaque_fov_AC <- read_csv("Macaque_fov_AC_expression2_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Amacrine cells")
Macaque_fov_BC <- read_csv("Macaque_fov_BC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Bipolar cells")
Macaque_fov_EpiImmune <- read_csv("Macaque_fov_EpiImmune_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
mutate(Category = str_replace_all(Cluster, c("MG"="Muller glia", "Mic"="Microglia", "Endo"="Endothelial")))
Macaque_fov_HC <- read_csv("Macaque_fov_HC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Horizontal cells")
Macaque_fov_PR <- read_csv("Macaque_fov_PR_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
mutate(Category = str_replace_all(Cluster,"M/L Cones|S Cones", "Cones"))
Macaque_fov_RGC <- read_csv("Macaque_fov_RGC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Retinal ganglion cells")
Macaque_per_AC <- read_csv("Macaque_per_AC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Amacrine cells")
Macaque_per_BC <- read_csv("Macaque_per_BC_expression2_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Bipolar cells")
Macaque_per_EpiImmune <- read_csv("Macaque_per_EpiImmune_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
mutate(Category = str_replace_all(Cluster, c("MG"="Muller glia", "Mic"="Microglia", "Endo"="Endothelial")))
Macaque_per_HC <- read_csv("Macaque_per_HC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Horizontal cells")
Macaque_per_PR <- read_csv("Macaque_per_PR_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
mutate(Category = str_replace_all(Cluster,"M/L Cones|S Cones", "Cones"))
Macaque_per_RGC <- read_csv("Macaque_per_RGC_expression_IMPG1_IMPG2.txt", name_repair="minimal") %>%
pivot_longer(cols=!GENE,names_to="CellID",values_to="Expression") %>%
pivot_wider(names_from=GENE,values_from=Expression) %>%
mutate(CellID = str_replace_all(CellID,"-",".")) %>%
inner_join(meta,by="CellID") %>%
add_column(Category = "Retinal ganglion cells")macaque_all <- bind_rows(Macaque_fov_AC,Macaque_fov_BC,Macaque_fov_EpiImmune,Macaque_fov_HC,Macaque_fov_PR,Macaque_fov_RGC,Macaque_per_AC,Macaque_per_BC,Macaque_per_EpiImmune,Macaque_per_HC,Macaque_per_PR,Macaque_per_RGC)Plot boxplots of IMPG1 and IMPG2 expression
Split and color fovea and periphery separately
p1 <- macaque_all %>%
group_by(Category) %>%
ggplot(aes(x=reorder(Category,-IMPG2),y=IMPG1,fill=Subcluster)) +
geom_boxplot(outlier.shape = NA) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),legend.position = c(0.7, 0.8)) +
xlab("Macaque retina cell types") +
ylab("IMPG1 expression")
p2 <- macaque_all %>%
group_by(Category) %>%
ggplot(aes(x=reorder(Category,-IMPG2),y=IMPG2,fill=Subcluster)) +
geom_boxplot(outlier.shape = NA) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),legend.position = c(0.7, 0.8)) +
xlab("Macaque retina cell types") +
ylab("IMPG2 expression")
plot_grid(p1,p2)
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.5 (Ootpa)
Matrix products: default
BLAS/LAPACK: /nas/longleaf/rhel8/apps/r/4.1.0/lib/libopenblas_haswellp-r0.3.5.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.8
[9] ggplot2_3.3.6 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 lubridate_1.8.0 getPass_0.2-2 ps_1.6.0
[5] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.29 utf8_1.2.2
[9] R6_2.5.1 cellranger_1.1.0 backports_1.4.1 reprex_2.0.1
[13] evaluate_0.15 highr_0.9 httr_1.4.2 pillar_1.7.0
[17] rlang_1.0.4 readxl_1.3.1 rstudioapi_0.13 whisker_0.4
[21] callr_3.7.0 jquerylib_0.1.4 rmarkdown_2.12 labeling_0.4.2
[25] bit_4.0.4 munsell_0.5.0 broom_1.0.0 compiler_4.1.0
[29] httpuv_1.6.5 modelr_0.1.8 xfun_0.30 pkgconfig_2.0.3
[33] htmltools_0.5.2 tidyselect_1.1.2 fansi_1.0.3 withr_2.5.0
[37] crayon_1.5.1 tzdb_0.2.0 dbplyr_2.1.1 later_1.3.0
[41] grid_4.1.0 jsonlite_1.8.0 gtable_0.3.0 lifecycle_1.0.1
[45] DBI_1.1.2 git2r_0.30.1 magrittr_2.0.2 scales_1.2.0
[49] vroom_1.5.7 cli_3.3.0 stringi_1.7.6 farver_2.1.0
[53] fs_1.5.2 promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1
[57] ellipsis_0.3.2 generics_0.1.2 vctrs_0.4.1 tools_4.1.0
[61] bit64_4.0.5 glue_1.6.2 hms_1.1.1 parallel_4.1.0
[65] processx_3.5.2 fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
[69] rvest_1.0.2 knitr_1.37 haven_2.4.3 sass_0.4.0