Influence of low-input RNA from FF and FFPE samples on gene expression quantification by NanoString, microarray and RNA-seq: a comparative study
Expression microarray · mouse · 206 samples
- What it is
- Expression microarrayGene activity read on a chip (the older technology that RNA-seq replaced).
- Organism
- Mus musculus (mouse)
- Samples
- 206 samples: 206 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed expression table (series matrix) and the array files, from NCBI.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE117678_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For arrays the series matrix is normally the normalised expression table you want; the raw array files (CEL and similar) are in the RAW.tar.
Also filed as BioProject PRJNA483003 and SRA study SRP155276. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 206 sample titles: 206 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM3305911 BC57_FF_50ng_std_rep1 (Nanostring)
- GSM3305912 BC57_FF_50ng_std_rep2 (Nanostring)
- GSM3305913 BC57_FF_50ng_std_rep3 (Nanostring)
- GSM3305914 MNU_FF_50ng_std_rep1 (Nanostring)
- GSM3305915 MNU_FF_50ng_std_rep2 (Nanostring)
- GSM3305916 MNU_FF_50ng_std_rep3 (Nanostring)
- GSM3305917 BC57_FFPE_50ng_std_rep1 (Nanostring)
- GSM3305918 BC57_FFPE_50ng_std_rep2 (Nanostring)
- GSM3305919 BC57_FFPE_50ng_std_rep3 (Nanostring)
- GSM3305920 MNU_FFPE_50ng_std_rep1 (Nanostring)
- GSM3305921 MNU_FFPE_50ng_std_rep2 (Nanostring)
- GSM3305922 MNU_FFPE_50ng_std_rep3 (Nanostring)
- GSM3305923 BC57_FF_50pg_OVA_NG_rep2 (Nanostring)
- GSM3305924 BC57_FF_50pg_OVA_NG_rep3 (Nanostring)
- GSM3305925 BC57_FF_50pg_OVA_NG_rep1 (Nanostring)
- GSM3305926 MNU_FF_50pg_OVA_NG_rep2 (Nanostring)
- GSM3305927 MNU_FF_50pg_OVA_NG_rep3 (Nanostring)
- GSM3305928 MNU_FF_50pg_OVA_NG_rep1 (Nanostring)
- GSM3305929 BC57_FF_2ng_OVA_NG_rep1 (Nanostring)
- GSM3305930 BC57_FF_2ng_OVA_NG_rep2 (Nanostring)
- GSM3305931 BC57_FF_2ng_OVA_NG_rep3 (Nanostring)
- GSM3305932 MNU_FF_2ng_OVA_NG_rep2 (Nanostring)
- GSM3305933 MNU_FF_2ng_OVA_NG_rep3 (Nanostring)
- GSM3305934 MNU_FF_2ng_OVA_NG_rep1 (Nanostring)
- GSM3305935 BC57_FF_250pg_OVA_NG_rep2 (Nanostring)
- GSM3305936 BC57_FF_250pg_OVA_NG_rep3 (Nanostring)
- GSM3305937 BC57_FF_250pg_OVA_NG_rep1 (Nanostring)
- GSM3305938 MNU_FF_250pg_OVA_NG_rep2 (Nanostring)
- GSM3305939 MNU_FF_250pg_OVA_NG_rep3 (Nanostring)
- GSM3305940 MNU_FF_250pg_OVA_NG_rep1 (Nanostring)
- GSM3305941 BC57_FFPE_50pg_OVA_NG_rep2 (Nanostring)
- GSM3305942 BC57_FFPE_50pg_OVA_NG_rep3 (Nanostring)
- GSM3305943 BC57_FFPE_50pg_OVA_NG_rep1 (Nanostring)
- GSM3305944 MNU_FFPE_50pg_OVA_NG_rep2 (Nanostring)
- GSM3305945 MNU_FFPE_50pg_OVA_NG_rep3 (Nanostring)
- GSM3305946 MNU_FFPE_50pg_OVA_NG_rep1 (Nanostring)
- GSM3305947 BC57_FFPE_2ng_OVA_NG_rep1 (Nanostring)
- GSM3305948 BC57_FFPE_2ng_OVA_NG_rep2 (Nanostring)
- GSM3305949 BC57_FFPE_2ng_OVA_NG_rep3 (Nanostring)
- GSM3305950 MNU_FFPE_2ng_OVA_NG_rep2 (Nanostring)
+ 166 more — browse all 206 samples with per-sample file links →
- GSE117677 Influence of low-input RNA from FF and FFPE samples on gene expression quantification by NanoString, microarray and RNA-seq: a comparative study [RNA-seq] 66 samples shares ffpe, quantification, comparative, nanostring, microarray, influence, low-input, rna-seq
- GSE272152 Optimizing murine sample sizes for RNA-seq studies revealed from large-scale comparative analysis 479 samples shares comparative, rna-seq
- GSE205592 Obesity-instructed TREM2-high macrophages identified by comparative analysis of diabetic mouse and human kidney at single-cell resolution [scRNA-seq: DKD_mouse] 41 samples shares comparative, rna-seq
- GSE283201 Comparative gene expression profiling analysis of hippocampus and liver RNA-seq data for young (3-month-old) and aged (22-month-old) WT mice in both control and ethanol feeding conditions. 32 samples shares comparative, rna-seq
- GSE205593 Obesity-instructed TREM2-high macrophages identified by comparative analysis of diabetic mouse and human kidney at single-cell resolution [scRNA-seq: Baseline_mouse] 24 samples shares comparative, rna-seq
- GSE232481 Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development [mouse scRNA-seq] 13 samples shares comparative, rna-seq
- GSE288210 Comparative gene expression profiling of RNA-seq data from islets of β-cell-specific ZZEF1 knockout mice (βZKO-Mip) and their littermate controls, treated with either azoramide or DMSO. 11 samples shares comparative, rna-seq
- GSE232197 Comparative gene expression profiling analysis of RNA-seq data for mCherryHigh and DTC populations compared to mCherryLow metastatic populations [mCherry Pops and DTCs] 9 samples shares comparative, rna-seq
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.