Project description:We present a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive- versus negative-fold change. We use this new “Platinum Spike” dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset.
Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this “Ag Spike”dataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms.
Project description:We present a new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive- versus negative-fold change. We use this new â??Platinum Spikeâ?? dataset to evaluate microarray analysis routes and contrast the results to those achieved using our earlier Golden Spike dataset. PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones were collected into 28 distinct pools, and three independent in vitro transcription and labeling reactions were performed for each pool. Labeled cRNAs from each individual pool were then added at specified amounts to samples A and B to achieve the desired fold change differences between samples. 24 cRNAs generated from DGCr2 cDNA clones were added to each sample in known concentrations before hybridization.
Project description:We have generated a wholly defined spike-in dataset for Agilent microarrays consisting of 12 arrays with more than 2000 differentially expressed, and approximately 3600 background, cRNAs. The composition of this âAg Spikeâdataset is identical to that of our previous Platinum Spike dataset (GSE21344) and therefore allows direct cross-platform comparison. Comparison between the Ag Spike and Platinum Spike studies shows high agreement between results obtained using the Affymetrix and Agilent platforms. We used the same 28 PCR pools that had been used before to generate the Platinum-Spike dataset (GSE21344). These pools contain PCR products from 5725 Drosophila Gene Collection release 1.0 (DGCr1) cDNA clones. We mixed PCR products from all pools together with specified relative abundance to generate two samples representing the A and B conditions. Both samples were in vitro transcribed and then labeled with Cy3 and Cy5 fluorescent dye. Labeled A samples were hybridized with B samples labeled with reverse dye to three Agilent Drosophila Gene Expression microarrays. The process from in vitro transcription to hybridization was repeated twice. At the end we generated 12 arrays that were randomly placed in three independent slides to remove potential systematic error due to array position.
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we assess the RNA-degradation and decay rates by subjecting both spike-in molecules to range of repeated freezing and thawing (freeze-thaw) cycles. We manually add spike-in molecules across a 96-well plate (containing cells and reagents), perform Smart-Seq2 method manually and generate single-cell libraries using Nextera XT kit
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform SMARTer method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform a replicate of Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit