Retinal Organoids counts data: Gene and Isoform Level Exploration
Required packages
Overview
HumanRetinaLRSData is a companion data package for our
long-read sequencing study characterising isoform diversity, splicing
dynamics, and allele-specific expression in developing human retinal
organoids.
In this study we used nanopore-based long-read RNA sequencing to profile human stem cell-derived retinal organoids across multiple developmental stages, as well as purified retinal ganglion cells. These datasets enable exploration of gene- and isoform-level expression patterns, detection of differential isoform usage, identification of neuron-specific splicing programs, and quantification of allelic imbalance.
All raw and processed data generated in the study have been deposited
in an Open Science Framework (OSF) repository. The
HumanRetinaLRSData package provides convenient access to
these files directly from the OSF node using the osfr
package.
For ease of use, HumanRetinaLRSData is designed to:
- query and retrieve study datasets from the OSF project,
- cache downloaded archives via
BiocFileCache, and - load gene- and isoform-level counts into R for downstream visualisation and analysis.
Visualise gene-level expression
We perform PCA on log2-transformed CPM values and colour samples by developmental stage.
stage_colors <- c(
"stage 1" = "orange",
"stage 2" = "seagreen",
"stage 3" = "purple"
)
expr_gene <- assay(se_gene, "cpm")
pca_gene <- prcomp(t(log2(expr_gene + 1)), scale. = TRUE)
var_gene <- round(
pca_gene$sdev^2 / sum(pca_gene$sdev^2) * 100, 1
)
pca_df_gene <- data.frame(
PC1 = pca_gene$x[, 1],
PC2 = pca_gene$x[, 2],
sample = colnames(expr_gene),
stage = colData(se_gene)[["stage"]]
)
ggplot(pca_df_gene, aes(x = PC1, y = PC2, color = stage)) +
geom_point(size = 3) +
geom_label_repel(
aes(label = sample),
size = 2.5, show.legend = FALSE
) +
scale_color_manual(values = stage_colors, name = "Stage") +
labs(
title = "PCA of Gene-Level Expression",
x = paste0("PC1 (", var_gene[1], "%)"),
y = paste0("PC2 (", var_gene[2], "%)")
) +
theme_bw(base_size = 12)
Visualise isoform-level expression
expr_iso <- assay(se_isoform, "cpm")
pca_iso <- prcomp(t(log2(expr_iso + 1)), scale. = TRUE)
var_iso <- round(
pca_iso$sdev^2 / sum(pca_iso$sdev^2) * 100, 1
)
pca_df_iso <- data.frame(
PC1 = pca_iso$x[, 1],
PC2 = pca_iso$x[, 2],
sample = colnames(expr_iso),
stage = colData(se_isoform)[["stage"]]
)
ggplot(pca_df_iso, aes(x = PC1, y = PC2, color = stage)) +
geom_point(size = 3) +
geom_label_repel(
aes(label = sample),
size = 2.5, show.legend = FALSE
) +
scale_color_manual(values = stage_colors, name = "Stage") +
labs(
title = "PCA of Isoform-Level Expression",
x = paste0("PC1 (", var_iso[1], "%)"),
y = paste0("PC2 (", var_iso[2], "%)")
) +
theme_bw(base_size = 12)
Interpretation
The PCA plots reveal:
- Separation by stage: Samples cluster by developmental stage, indicating distinct expression profiles.
- Variance explained: PC1 and PC2 capture the major sources of variation in the dataset.
- Gene vs. isoform: While samples cluster similarly by developmental stage at both levels, the proportion of variance explained by each PC differs. This reflects that isoform-level analysis may capture additional complexity from alternative splicing and transcript variation beyond what is seen at the gene level.
Session information
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
##
## 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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggrepel_0.9.8 ggplot2_4.0.3
## [3] HumanRetinaLRSData_1.0.0 SummarizedExperiment_1.42.0
## [5] Biobase_2.72.0 GenomicRanges_1.64.0
## [7] Seqinfo_1.2.0 IRanges_2.46.0
## [9] S4Vectors_0.50.3 BiocGenerics_0.58.1
## [11] generics_0.1.4 MatrixGenerics_1.24.0
## [13] matrixStats_1.5.0 rmarkdown_2.32
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.61 bslib_0.12.0
## [4] httr2_1.3.0 lattice_0.23-1 vctrs_0.7.3
## [7] tools_4.6.1 curl_8.0.0 tibble_3.3.1
## [10] RSQLite_3.53.3 blob_1.3.0 pkgconfig_2.0.3
## [13] Matrix_1.7-6 dbplyr_2.6.0 RColorBrewer_1.1-3
## [16] S7_0.2.2 lifecycle_1.0.5 compiler_4.6.1
## [19] farver_2.1.2 htmltools_0.5.9 sys_3.4.3
## [22] buildtools_1.0.0 sass_0.4.10 yaml_2.3.12
## [25] pillar_1.11.1 jquerylib_0.1.4 DelayedArray_0.38.2
## [28] cachem_1.1.0 abind_1.4-8 tidyselect_1.2.1
## [31] digest_0.6.39 stringi_1.8.9 dplyr_1.2.1
## [34] purrr_1.2.2 labeling_0.4.3 maketools_1.3.2
## [37] fastmap_1.2.0 grid_4.6.1 cli_3.6.6
## [40] SparseArray_1.12.3 magrittr_2.0.5 S4Arrays_1.12.1
## [43] triebeard_0.4.1 crul_1.6.0 withr_3.0.3
## [46] osfr_0.2.9 filelock_1.0.3 scales_1.4.0
## [49] bit64_4.8.6 XVector_0.52.0 httr_1.4.9
## [52] bit_4.6.0 otel_0.2.0 memoise_2.0.1
## [55] evaluate_1.0.5 knitr_1.52 BiocFileCache_3.2.0
## [58] urltools_1.7.3.1 rlang_1.3.0 Rcpp_1.1.2
## [61] glue_1.8.1 DBI_1.3.0 httpcode_0.3.0
## [64] jsonlite_2.0.0 R6_2.6.1 fs_2.1.0