|
| Status |
Public on Apr 17, 2024 |
| Title |
RPE/choroid complex, control 4 |
| Sample type |
SRA |
| |
|
| Source name |
RPE/choroid complex
|
| Organism |
Mus musculus |
| Characteristics |
tissue: RPE/choroid complex
genotype: Abca1/Abcg1 flox/flox LysM-Cre
treatment: AP20187 (i.p)
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Samples were collected in TRIzol reagent. mRNA was obtained using NucleoSpin RNA columns (Takara, 740955) according to the manufacturer’s instructions.
cDNA amplification for mRNA-Seq was performed by SMARTer
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
| |
|
| Description |
l5
|
| Data processing |
Basecalls and demultiplexing were performed with Illumina’s bcl2fastq2 software. RNA-seq reads were then aligned and quantitated to the Ensembl release 101 primary assembly with an Illumina DRAGEN Bio-IT on-premise server running version 3.9.3-8 software.
All gene counts were then imported into the R/Bioconductor package EdgeR and TMM normalization size factors were calculated to adjust for samples for differences in library size. Ribosomal genes and genes not expressed in the smallest group size minus one samples greater than one count-per-million were excluded from further analysis. The TMM size factors and the matrix of counts were then imported into the R/Bioconductor package Limma. Weighted likelihoods based on the observed mean-variance relationship of every gene and sample were then calculated for all samples and the count matrix was transformed to moderated log 2 counts-per-million with Limma’s voomWithQualityWeights. The performance of all genes was assessed with plots of the residual standard deviation of every gene to their average log-count with a robustly fitted trend line of the residuals. Differential expression analysis was then performed to analyze for differences between conditions and the results were filtered for only those genes with Benjamini-Hochberg false-discovery rate adjusted p-values less than or equal to 0.05.
For each contrast extracted with Limma, global perturbations in known Gene Ontology (GO) terms, MSigDb, and KEGG pathways were detected using the R/Bioconductor package GAGE to test for changes in expression of the reported log 2 fold-changes reported by Limma in each term versus the background log 2 fold-changes of all genes found outside the respective term. The R/Bioconductor package heatmap3 was used to display heatmaps across groups of samples for each GO or MSigDb term with a Benjamini-Hochberg false-discovery rate adjusted p-value less than or equal to 0.05. Perturbed KEGG pathways where the observed log 2 fold-changes of genes within the term were significantly perturbed in a single-direction versus background or in any direction compared to other genes within a given term with p-values less than or equal to 0.05 were rendered as annotated KEGG graphs with the R/Bioconductor package Pathview.
To find the most critical genes, the Limma voomWithQualityWeights transformed log 2 counts-per-million expression data was then analyzed via weighted gene correlation network analysis with the R/Bioconductor package WGCNA. Briefly, all genes were correlated across each other by Pearson correlations and clustered by expression similarity into unsigned modules using a power threshold empirically determined from the data. An eigengene was then created for each de novo cluster and its expression profile was then correlated across all coefficients of the model matrix. Because these clusters of genes were created by expression profile rather than known functional similarity, the clustered modules were given the names of random colors where grey is the only module that has any pre-existing definition of containing genes that do not cluster well with others. These de-novo clustered genes were then tested for functional enrichment of known GO terms with hypergeometric tests available in the R/Bioconductor package clusterProfiler. Significant terms with Benjamini-Hochberg adjusted p-values less than 0.05 were then collapsed by similarity into clusterProfiler category network plots to display the most significant terms for each module of hub genes in order to interpolate the function of each significant module. The information for all clustered genes for each module were then combined with their respective statistical significance results from Limma to determine whether or not those features were also found to be significantly differentially expressed.
Assembly: Mouse Ensembl GRCm38.76
Supplementary files format and content: all.gene_counts.tsv: Raw counts.
Supplementary files format and content: group_knockout_Differential_Expression.tsv: Differential gene expression performed comparing control and knockout
|
| |
|
| Submission date |
Jun 09, 2023 |
| Last update date |
Apr 17, 2024 |
| Contact name |
Ryo Terao |
| E-mail(s) |
terao@wustl.edu
|
| Phone |
314-362-6690
|
| Organization name |
Washington University School of Medicine in St. Louis
|
| Department |
Ophthalmology & Visual Sciences
|
| Street address |
517 S. Euclid Ave
|
| City |
St. Louis |
| State/province |
MO |
| ZIP/Postal code |
63110 |
| Country |
USA |
| |
|
| Platform ID |
GPL24247 |
| Series (1) |
| GSE234641 |
Effects of selective elimination of senescent cells on RPE/choroid complex in mice with subretinal drusenoid deposits |
|
| Relations |
| BioSample |
SAMN35688337 |
| SRA |
SRX20649798 |
