Gene expression datasets used in the experiments.

DE analysis comparing two species and theuir two hybrid derivatives

H5ad file can be used as demo input for Cellxgene VIP. Dataset was the re-process from Schirmer et al Nature 2019 paper by using the raw fastq files. In order to reproduce the h5ad file, details could be found in https://github.com/interactivereport/cellxgene_VIP/blob/master/notebook/MS_Nature_Rowitch_snRNAseq.ipynb Two rds files are also included here which are the input files for sample differential expression (DE) analysis scripts (glmmTMB and Nebula)

About this dataset

Context

The dataset tabulates the median household income in Smyrna. It can be utilized to understand the trend in median household income and to analyze the income distribution in Smyrna by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Smyrna, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Smyrna, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Smyrna, DE
• Smyrna, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Smyrna median household income. You can refer the same here

Differential expression analysis R script and output from ATRA vs control treated RNA-seq samples presented in the Caenorhabditis Intervention Testing Program (CITP) manuscript: Survey of anti-aging computational predictions identifies all-trans retinoic acid modulation of conserved longevity pathways in genetically diverse Caenorhabditis nematode

Many organisms exhibit phenotypic plasticity, in which developmental processes result in different phenotypes depending on their environmental context. We focus on the molecular mechanisms underlying that environmental response. Pea aphids (Acyrthosiphon pisum) show a wing dimorphism, in which pea aphid mothers produce winged or wingless daughters when exposed to a crowded or low-density environment, respectively. We investigated the role of dopamine in mediating this wing plasticity, motivated by a previous study that found higher dopamine titers in wingless- versus winged-producing aphid mothers. In this study, we found that manipulating dopamine levels in aphid mothers affected the number of winged offspring they produced. Specifically, asexual female adults injected with a dopamine agonist produced a lower percentage of winged offspring, while asexual females injected with a dopamine antagonist produced a higher percentage of winged offspring, matching expectations based on the titer difference. We also found that genes involved in dopamine synthesis, degradation, and signaling were not differentially expressed between wingless- and winged-producing aphids. This result indicates that titer regulation happens in a non-transcriptional manner or that we sampled non-relevant timepoints or tissue. Overall, our work emphasizes that dopamine is an important component of how organisms process information about their environments. Methods This data set comprises previously published and reanalyzed RNA-seq data and pharmaceutical injection result data. In the RNAseq folder, the GenBank accession numbers for these sequences are SRR13238533 - SRR13238544, SRR2148902 - SRR2148909, and SRR21747201 - SRR21747212 (recorded in "Sample Accession Number.xlsx). Raw reads were processed using TrimGalore and FastQC (see “process_read.sh”). We trimmed adaptor sequences, filtered out poor-quality sequences (quality score cutoff 20), and discarded sequences shorter than 20nt. The resulting reads were then mapped to the pea aphid genome v3.0 using HISAT2 (see “map_read_hisat_v3.0genome.sh”, “modified genome and annotation” folder). All raw counts matrix was combined and processed into an R.data (“5lines_counts.Rdata”). Counts normalization and differential expression analysis were done using the v3.0 genome annotation with R package DESeq2 (“DE_analysis.Rmd”, “modified genome and annotation” folder). Information for dopamine-related genes investigated in this study can be found in table “gene_information.xlsx”. In the pharmaceutical injection folder, the raw injection result table (“pharmaceutical_injection_result.csv”) and the script to analyze the visualized data (“pharmaceutical_injection_analysis.Rmd”) are included in the dataset. To generate the injection data, healthy adult females were pooled and randomly divided into injection groups. Aphids were injected with 1 μg/μL apomorphine (Sigma), 1 μg/μL flupenthixol (Fisher Scientific), or insect Ringer’s as control using a pulled glass capillary needle. Both apomorphine and flupenthixol were dissolved in Ringer’s. Glass needles were prepared using a micropipette puller (P-1000, Sutter Instrument) with the setting of pull=150 and heat=504. Needle tips were broken against a glass slide edge. Injection was done with a microinjector (PLI-10, Warner Instruments) for 0.1s, 5.7psi, injecting roughly 0.3μL of liquid. After injection, aphids were transferred back to plants with a density of 3/cage for 24h for reproduction. Injected adult females were then removed while their offspring were kept growing on plants. Offspring were categorized as winged or wingless after at least reaching the third instar stage. For each cage, the offspring’s winged percentage was calculated as the number of winged offspring divided by the total number of offspring.

About this dataset

Context

The dataset tabulates the median household income in Ellendale. It can be utilized to understand the trend in median household income and to analyze the income distribution in Ellendale by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Ellendale, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Ellendale, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Ellendale, DE
• Ellendale, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Ellendale median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Clayton. It can be utilized to understand the trend in median household income and to analyze the income distribution in Clayton by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Clayton, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Clayton, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Clayton, DE
• Clayton, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Clayton median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Arden. It can be utilized to understand the trend in median household income and to analyze the income distribution in Arden by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Arden, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Arden, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Arden, DE
• Arden, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Arden median household income. You can refer the same here

Simulated RNA-seq data shows that histograms from p value sets with around one hundred true effects out of 20,000 features can be classified as 'uniform'. RNA-seq data was simulated with polyester R package (Frazee, 2015) on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5 and 2. Differential expression was assessed using DESeq2 R package (Love, 2014) using default settings and group 1 versus group 2 contrast. Effects denotes in facet labels the number of true effects and N denotes number of replicates. Red line denotes QC threshold used for dividing p histograms into discrete classes. Workflow and code used to run this simulation is available on rstats-tartu/simulate-rnaseq.

Files

• de_simulation_results.csv -- merged and processed DE analysis results of simulated data.
• simulate-reads-2021-01-25.tar.gz -- raw DE analysis results on 20,000 transcripts from human transcriptome using grid of 3, 6, and 10 replicates and 100, 200, 400, and 800 effects for two groups. Fold changes were set to 0.5, 1, and 2. Differential expression was assessed using DESeq2 with default settings.
• simulate-rnaseq.tar.gz -- snakemake workflow and input fasta file to simulate RNA-seq data with polyester and analyse results with DESeq2. Adjust settings in config.yaml to customise simulation. Includes software to run workflow on Linux, given that Conda and snakemake are installed.

The simulate-rnaseq.tar.gz archive can be re-executed on a vanilla machine that only has Conda and Snakemake installed via:

tar -xf simulate-rnaseq.tar.gz
snakemake --use-conda -n

Purpose: muscle transcriptomics of subjects supplemented with Urolithin A at different doses or placebo for 4 month.Method: RNA-seq was performed using Illumina HiSeq 4000 sequencing; single read 1 x 50 bp . The quantification of mRNA from the RNA-seq FASTQ files was performed using Salmon. Sample-wise quant.sf files containing raw transcript-level read estimates were read into R, v. 4.0.3 and were combined into a data matrix. Transcripts with very low total counts (< 10) across all samples were filtered out. The data was transformed using the variance stabilizing transformation (VST) method of R package DESeq2, v. 1.30.0. Top 10,000 transcripts with the highest variance across all samples were used for principal component analysis (PCA) using DESeq2. Data transformation and PCA was also done separately for each treatment group. Based on the PCAs, probable outlier samples were excluded and new PCAs were plotted without these samples. The raw transcript-level read count estimates were read in R and summarized to gene-level counts based on the provided transcript and gene ID annotations using summarizeToGene function of R package tximport, v. 1.18.0. DESeqDataSetFromTximport function of DESeq2 was then used for constructing a DESeqDataSet object for DE analysis. Pre-filtering was applied before the DE analysis by excluding genes with < 10 total counts across samples. Subset DE analysis was performed, contrasting Visit time D120 with baseline (BL) and by adjusting for the subject effect. The normalization and DE analysis was done separately for the three different treatment groups. Independent filtering option of DESeq2 was enabled (default), filtering out genes with very low counts and thus unlikely to show significant evidence. R package biomaRt. v. 2.46.0 was used for annotating the results with HGNC gene symbols, gene descriptions and gene biotypes. DESeq2-normalised expression values of all the samples in the given comparison were added to the result tables. Non-adjusted p-value 0.05 was used to filter the results by statistical significance. Results were also generated using DESeq2 function lfcShrink that allows for the shrinkage of the log2 fold change (LFC) estimates toward zero when the information for a gene is low (such as in those cases with low counts or high dispersion values) but has little effect on genes with high counts. The shrinked log2FC values were subsequently used for visualisation and ranking the genes. Muscle mRNA profiles from subjects enrolled in the ATLAS clinical study (NCT03464500)

Illustrative examples of multiple samples of the experiment with no shift effect.

About this dataset

Context

The dataset tabulates the median household income in Milton. It can be utilized to understand the trend in median household income and to analyze the income distribution in Milton by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Milton, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Milton, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Milton, DE
• Milton, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Milton median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Camden. It can be utilized to understand the trend in median household income and to analyze the income distribution in Camden by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Camden, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Camden, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Camden, DE
• Camden, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Camden median household income. You can refer the same here

A simple illustrative hypothetic example on the effect of global expression shift.

Labelling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs) between biological samples. The current data analysis platform relies on protein-level ratios, where peptide-level ratios are averaged to yield a single summary ratio for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is incorporated into the differential expression (DE) analysis. Here we propose a novel probabilistic framework EBprot that directly models the peptide-to-protein hierarchy and rewards the proteins with reproducible quantification over multiple peptides. To evaluate its performance with known DE states, we first verified that the peptide-level analysis of EBprot provides more accurate estimation of the false discovery rates and better receiver-operating characteristic than other protein ratio analyses using simulation datasets, and confirmed the superior classification performance in a UPS1 mixture spike-in dataset. To illustrate the performance of EBprot in realistic applications, we applied EBprot to a SILAC dataset for lung cancer subtype analysis and an iTRAQ dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells, each featuring a different experimental design. Through these various examples, we show that the peptide-level analysis of EBprot provides a competitive advantage over alternative methods for the DE analysis of labelling-based quantitative datasets.

About this dataset

Context

The dataset tabulates the median household income in Delmar. It can be utilized to understand the trend in median household income and to analyze the income distribution in Delmar by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Delmar, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Delmar, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Delmar, DE
• Delmar, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Delmar median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Wilmington. It can be utilized to understand the trend in median household income and to analyze the income distribution in Wilmington by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Wilmington, DE Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Wilmington, DE: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Wilmington, DE
• Wilmington, DE households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Wilmington median household income. You can refer the same here

Illustrative examples of experiments without global shift and with shifts of two directions.

The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.