GNPS - Foodomics - Workshop Dataset
Ontology highlight
ABSTRACT:
INSTRUMENT(S): maXis II
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Pieter C Dorrestein
PROVIDER: MSV000088335 | GNPS | Mon Nov 08 17:03:00 GMT 2021
REPOSITORIES: GNPS
Similar Datasets
Project description:Metal addition E coli Nissle dataset for use in workshop
2019-11-26 | MSV000084628 | MassIVE
Project description:The present dataset ("dataset 3") is a subset of a large metastudy on AML classfication. It contains normalized gene expression values of 1181 samples. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 3 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with high-throughput RNA sequencing (RNA-seq) and excluded studies with very small sample sizes (< 10 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE63085, GSE32874, GSE58335, GSE86884, GSE63703, GSE63646, GSE63816, GSE72790, GSE81259, GSE85712, GSE45735, GSE64655, GSE87186, GSE49642, GSE52656, GSE62190, GSE66917, GSE67039, GSE61162, GSE67184, GSE49601, GSE78785, GSE79970. All raw data files were downloaded from GEO. Transcript abundances were calculated using kallisto version 0.43.0 and all data was normalized with the R package DESeq2 (R version R-3.2.4, DESeq2 version 1.12.4) with standard parameters. Genome build hg38 was used for read alignment. No filtering of low-expressed genes was performed.
2019-12-18 | GSE122515 | GEO
Project description:Cancer: Surviving and Thriving is a 6-week workshop for cancer survivors. The overall goal of the study is to determine whether an online cancer survivor education and support workshop can have lasting beneficial effects in helping survivors improve their self-management of health skills and quality of life.
| 2076916 | ecrin-mdr-crc
Project description:Input files for the Dorrestein Lab in-house qiime2 workshop for base metabolomics figures.
2019-11-15 | MSV000084585 | MassIVE
Project description:The present dataset ("dataset 1") is a subset of a large metastudy on AML classfication. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 1 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion in the present study. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with the Affymetrix Human Genome U133 A Array (GLP96) and excluded studies with small sample sizes (< 50 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE10255, GSE1159, GSE12417, GSE12995, GSE13425, GSE14471, GSE14895, GSE16129, GSE25571, GSE26281, GSE33315, GSE34860, GSE37642, GSE43176, GSE4698, GSE51082, GSE6269, GSE67684, GSE83449, GSE8879, GSE9006, GSE9476. All CEL-files were downloaded from GEO and imported into R. Robist Multichip Average (RMA) expression measures were calculated using the R package affy.
2019-12-18 | GSE122505 | GEO
Project description:The present dataset ("dataset 2") is a subset of a large metastudy on AML classfication. In total, three datasets were generated, each containing data of a different platforms: dataset 1 (Affymetrix HG-U133 A microarrays), dataset 2 (Affymetrix HG-U133 2.0 microarrays) and dataset 3 (RNA-seq). Dataset 2 was generated using the following strategy: All data sets published in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) on 20 September 2017 were reviewed for inclusion. Basic criteria for inclusion were the cell type under study (human peripheral blood mononuclear cells (PMBCs) and/or bone marrow samples) as well as the species (Homo sapiens). Furthermore, GEO SuperSeries were excluded to avoid duplicated samples. We filtered the datasets for data generated with the Affymetrix Human Genome U133 Plus 2.0 Array (GLP570) and excluded studies with small sample sizes (< 50 samples). We then applied a disease-specific search, in which we filtered for acute myeloid leukemia, other leukemia and healthy or non-leukemia-related samples. The results of this search strategy were then internally reviewed and data were excluded based on the following criteria: (i) exclusion of duplicated samples, (ii) exclusion of studies that sorted single cell types (e.g. T cells or B cells) prior to gene expression profiling, (iii) exclusion of studies with inaccessible data. Other than that, no studies were excluded from our analysis. In total, the datasets contained samples from the following GSE Series: GSE12417, GSE25571, GSE37642, GSE6269, GSE67684, GSE10358, GSE10792, GSE11083, GSE12187, GSE12662, GSE13159, GSE13351, GSE13501, GSE13576, GSE14062, GSE14468, GSE14615, GSE15061, GSE15434, GSE16015, GSE16214, GSE17855, GSE18323, GSE18497, GSE19314, GSE21029, GSE21261, GSE21545, GSE22707, GSE22762, GSE22845, GSE26713, GSE27383, GSE27562, GSE28460, GSE34205, GSE35784, GSE42038, GSE42057, GSE43777, GSE46480, GSE47051, GSE49695, GSE50772, GSE60926, GSE61804, GSE62156, GSE63270, GSE66002, GSE6751, GSE67596, GSE68720, GSE68735, GSE68790, GSE68833, GSE6891, GSE70536, GSE7440, GSE76705, GSE7757, GSE78132, GSE79545, GSE87072, GSE9960. All CEL-files were downloaded from GEO and imported into R. Robist Multichip Average (RMA) expression measures were calculated using the R package affy.
2019-12-18 | GSE122511 | GEO
