Example Data Files: Transcriptomics

PlatformData typeAdditional parameterSample Input fileSample OutputGeneral instruction
Affymetrix Expression Array Raw CEL Files NONE MiaPaCa2 parental vs MTX resistant arrays. This data is a subset of GEO record GSE16648 and contains 6 samples, 3 parental and 3 MTX resistant.This dataset is from the Affymetrix HGU133 Plus 2.0 Array.Data is available as a zipped file below. Results You need to create a zipped file containing all of the CEL files that you will use for the analysis . On MAC, select all CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample.
Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. parental vs resistant etc.)
ESTIMATE Primary prostate Cancer samples vs normal samples and primary prostate cancer samples vs metastasized prostate cancer samples. This data is from GEO record GSE32269 and a subset from GEO record GSE8218. These datasets are from the Affymetrix U133A 2.0 Array.Data is available as a zipped file below. Results You need to create a zipped file containing all of the data you will use for the analysis. On MAC, select all CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and the target groups to each sample, the next stage is to set up the LIMMA comparisons (e.g primary prostate cancer vs normal and primary prostate cancer vs metastasized samples)
SURVIVAL Survival analysis of 198 breast cancer samples from GEO record GSE7390. This data is from the Affymetrix U133A 2.0 Array Results While setting up the analysis parameters, please check 'yes' for survival analysis. You need to create a zipped file containing all of the files needed for analysis. On MAC, select all CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample. Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. poor survival vs good survival etc.)
COMBAT Meta-analysis of Glioblastoma and normal samples, data is taken from GEO dataset GSE30563 and a subset of 14 samples from GEO dataset GSE15824. This data is from the Affymetrix HGU133 Plus 2.0 Array. Results While setting up the anaysis parameters, please check 'yes' in the COMBAT box to apply batch effect correction using the COMBAT algorithm. You need to create a zipped file of all the CEL files needed for analysis. On MAC, select all the CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name,target group and study name to each sample. Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. glioblastoma vs normal etc.)
Normalised Data file Gene expression profiling of human glioma samples (U95Av2 arrays). This data was downloaded from GEO record GSE12657 and contains 7 Glioblastoma, 5 controls, 6 Pilocytic astrocytoma and 7 Oligodendroglioma cases. This data is also available as a zipped file below. Results You need to create a zipped file of your normalised data file. On MAC, select the normalised file & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample. N.B. your data must be logged expression values, each sample identifier must be unique.
Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. cancer vs normal etc.)
Affymetrix miRNA Array Raw CEL Files with survival Prostate Cancer vs normal samples. This data is from GEO record GSE23022 and contains 40 samples, 20 tumour and 20 normal. The data is also available as a zipped file below. Results While setting up the analysis parameters, please check 'yes' for survival. You need to create a zipped file containing all of the CEL files that you will use for the analysis . On MAC, select all CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample. The next step is to click on the 'survival' button and fill in the information required for survival for each sample on the interface, i.e overall survival and event status.
Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. cancer vs normal etc.)
Affymetrix Exon Array Raw CEL Files SURVIVAL Prostate Cancer sample vs metastatised samples. This data is a subset from GEO record GSE21034 and contains 73 samples, 57 tumour samples and 16 metastatis samples. The data is available as a zipped file below. Results While setting up the analysis parameters, please check 'yes' for survival. You need to create a zipped file containing all of the CEL files that you will use for the analysis . On MAC, select all CEL files & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample. he next step is to click on the 'survival' button and fill in the information required for survival for each sample on the interface, i.e overall survival and event status.
Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. cancer vs normal etc.)
Illumina Expression Array Unnormalised Data Prostate Cancer samples vs normal samples. This dataset is a subset from the GEO record GSE32571 and contains 30 samples, 15 tumour samples and 15 normal samples. The data is available as a zipped file below Results While setting up the user options, please select unnormalised data as teh data type. Please create a zip file of the unnnormalised matrix (note that there is no raw data option for Illumina expression analysis).The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample.
Based on the target groups that were set up, the next step is to decide which LIMMA comparisons you want to include in your analysis (e.g. cancer vs normal etc.)
RNA-seq Post-processing data file (counts table) Edge R method Prostrate cancer samples vs normal samples. This dataset is a subset from the GEO record GSE22260 and contains 20 samples, 9 tumour samples and 11 normal samples. Processed RNA-seq data is available as a zipped file below Results You need to create a zipped file of your unnormalised reads counts data. On MAC, select the normalised file & use compress. On PC,right click and select 'send to' The interface will automatically upload and decompress this file (which may take a few minutes,so please be patient) and will then ask you to assign a name and a target group to each sample. N.B. your data must be reads counts data, each sample identifier must be unique.
Based on the target groups that were set up, the next step is to decide which differential expression method you would like to use and set up the comparisons you want to include in your analysis (e.g. cancer vs normal etc.)