Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “Differential abundance”
Differential abundance and gene set enrichment in plasma of cancer patients versus controls
<p>Version update: Human Protein Atlas v23 used for gene set enrichment analysis (instead of Human Protein Atlas v18)</p> <p><br>DESeq2 differential abundance output for genes with q < 0.05 and |log<sub>2</sub> fold change| > 1 in cancer vs control plasma samples:</p> <ul> <li><em>differentialabundance_pancancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in a pan-cancer plasma sample cohort (25 locally advanced to metastatic cancer types - 7 or 8 patients per type - vs 8 cancer-free control donors)</li> <li><em>differentialabundance_threecancer.txt</em>: tables with differentially abundant genes (|log2(fold change)|>1 and adjusted p>0.05) per cancer-control comparison (cancertype) in the three-cancer plasma cohort (ovarian, prostate and uterine cancer - 11 or 12 patients per type - vs 20 cancer-free controls) <ul> <li>Gene_id: Ensembl gene id (GChr38 v91); baseMean: mean of normalized counts for all samples; log2FoldChange: log2 fold change for cancer vs control; lfcSE: standard error for cancer vs control; stat: Wald statistic for cancer vs control; pvalue: Wald test p-value for cancer vs control; padj: Benjamini-Hochberg corrected p-value; cancertype: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls.</li> </ul> </li> </ul> <p>Gene set enrichment analyses based on fold change ranked gene lists (cancer versus control) - results obtained with fgea (v1.22.0):</p> <ul> <li><em>customgenesets.txt</em>: custom gene set lists based on RNA Atlas (&Human Protein Atlas), Tabula Sapiens, GTEX, TCGA data. <ul> <li>Reference: reference to create gene sets (including RNA Atlas, Human Protein Atlas, Tabula Sapiens, GTEX, and TCGA); set: set name; genes: gene list for set</li> </ul> </li> <li><em>GSEA_pancancer.txt</em> & <em>GSEA_threecancer.txt</em>: gene set enrichment results based on fold change ranked gene list (specific cancer type versus controls) in pan-cancer cohort and three-cancer cohort, respectively <ul> <li>Sets: gene set category (HALLMARK and KEGG: Hallmark and Canonical Pathways gene sets obtained from MSigDB (v2022.1); CUSTOM: custom tissue and cell type specific gene sets as defined in <em>customgenesets.txt)</em>; pathway: pathway/set name; pval: enrichment p-value; padj: Benjamini-Hochberg adjusted p-value; log2err: expected error for the standard deviation of the P-value logarithm; ES: enrichment score, same as in Broad GSEA implementation; NES: enrichment score normalized to mean enrichment of random samples of the same size; size: size of the pathway after removing genes without statistic values; leadingEdge: leading edge genes that drive the enrichment; Disease: respective cancer type abbreviation of cancer patient plasma samples that were compared to plasma samples of controls</li> </ul> </li> </ul>
Fig. 3 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 3. Cumulative distribution plots divided by year for (A) T. alpinus and (B) T. speciosus. For each species 2013 is shown in red, 2014 in teal, 2015 in pink. The x-axis represents each host individual, ordered from least to most flea infested, and the y-axis shows the cumulative proportion of total flea counts. The dotted line indicates individuals in the 90th percentile of flea abundances, illustrating that the top 10% most infected chipmunks usually account for close to 50% of all counted fleas. The proportion of individuals without fleas in each year is represented graphically as the proportion at which each colored line departs from the x-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 4. Relationships between fecal glucocorticoid metabolite levels, sex, and flea abundance for (A) T. alpinus and (B) T. speciosus. Points show the mean ± S.E. number of fleas counted for female (white) and male (black) individuals within FGM categories (FGM values were rounded to the nearest 10). Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each sex.
Fig. 2 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 2. Patterns of flea abundance across years, hosts, and flea species. Overall average flea abundances (A–B) and abundances of each flea species (C–D) in each year for T. alpinus (A, C) and T. speciosus (B, D). Abundances of each flea species on hosts of each sex (Males: closed circles, Females: open circles) on T. alpinus (E) and T. speciosus (F).
Fig. 5 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 5. Relationships between flea abundances and (A) the second principal component of temperature data; or (B) elevation for T. alpinus (white) and T. speciosus (black). Points show the mean ± S.E. number of fleas counted for a given study site in a given year. For each study site in each year, a mean ± S.E. temperature or elevation value is shown. Lines of best fit (based on all raw data points) ± 95% confidence intervals are overlaid for each species.
Fig. 1 in Host biology and environmental variables differentially predict flea abundances for two rodent hosts in a plague-relevant system
Fig. 1. Map showing study sites. Sites (see Supplementary Data S1 for more information) located in and around Yosemite National Park (green) were visited either in all three years (2013, 2014, and 2015; black), in two of the years (yellow), or in only one year (red). Yellow and black lines show significant roadways in the area. Lakes are shown in blue, including Mono Lake at top right. Inset shows Yosemite National Park (green) on a map of California. Site codes: AL: Arrowhead Lake; CL: Cathedral Lake (upper); GA: Glen Aulin; GL: Gaylor Lakes; HC: Hoffmann Creek; MA: Mammoth Lakes; ML: May Lake; PC: Porcupine Creek; SL: Saddlebag Lake; SLN: Saddlebag Lake, north-side (Greenstone and Steelhead Lakes); TM: Tuolumne Meadows. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Experimental manipulation of cavity temperature produces differential effects on parasite abundances in blue tit nests at two different latitudes
Fig. 4. Differences in the body mass of adult blue tit males by heat treatment of nest boxes and locality. Means ± intervals of confidence at 95% are shown.
Fig. 2 in Experimental manipulation of cavity temperature produces differential effects on parasite abundances in blue tit nests at two different latitudes
Fig. 2. Blowfly pupae abundance observed in control and heated nests of blue tits (Cyanistes caeruleus) in both localities (Spain and Germany). The data presented was controlled for the locality and the interaction between locality and treatment. Means ± intervals of confidence at 95% are shown.
Fig. 3 in Experimental manipulation of cavity temperature produces differential effects on parasite abundances in blue tit nests at two different latitudes
Fig. 3. Differences in the abundance of Haemoproteus/Plasmodium in blue tit males by treatment and locality. Means ± intervals of confidence at 95% are shown.
Fig. 1 in Experimental manipulation of cavity temperature produces differential effects on parasite abundances in blue tit nests at two different latitudes
Fig. 1. Daily variation of temperature in nests of blue tits. Temperature is decreasing at 00:00 and lower values for the day are attained close to 8:00 h. Data from two different nests with nestling of 7 days old are represented from A) Spain and B) Germany.
Data and code for: Changes in prey body size differentially reduces predation risk across predator and prey abundances
<p>Trophic interactions underpin the structure of ecological communities by describing the rate at which consumers exploit their resources. The rates at which predators consume their prey are influenced by prey traits, with many species inducing defensive modifications to prey traits following the threat of predation. Here we use different clonal lines of the protist <em>Paramecium</em> being consumed by <em>Stenostomum</em> predators to highlight how differences in prey traits impact rates of predation. Clonal lines differed in their body width traits and in their ability to induce changes in body width. By using a factorial cross of predator and prey abundances for different clonal lines we demonstrate how evolutionary or induced alterations in prey traits can impact the relative threat of predation. Our experiments show how interference among predators impacts predation rate and how increased body width increased predator handling times. Given that reductions in the strength of interspecific interactions are associated with increased levels of overall community stability, our results indicate how individual-level changes may scale up to impact whole communities. </p>
Differential responses to weather and land-cover conditions explain spatial variation in winter abundance trends in a migratory bird of conservation concern
Open the record for dataset details and reuse information.
Data and code for: Changes in prey body size differentially reduces predation risk across predator and prey abundances
Open the record for dataset details and reuse information.
Data for "A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies"
<p>Data for the manuscript: A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies (see also https://doi.org/10.1101/2022.05.09.491139)</p>
Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."
<p>The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.</p>
Data for: Differential genotype response to increased resource abundance helps explain parallel evolution of Daphnia populations in the wild
Open the record for dataset details and reuse information.
On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves: Bowtie2 counts and kallisto abundances
<p>Intra [1] and inter [2-5] study RNA-seq read datasets representing the varying brain compartments of foxes (n=24), as well as dogs (n=14) and wolves (n=6), as described in Lobo <em>et al.</em>, (2022) (under review), were mapped to the dog reference transcriptome [6], which contained 26,107 annotated transcripts (Ensembl CanFam3.1, release 92) [7], using Bowtie2 v.2.3.4.1 [8] and using kallisto v0.46.1 [9]. Count data obtained following each mapping approach for each dataset had high correlations (Lobo <em>et al.</em>, Figure S2). Bowtie2 counts were subsequently used in multiple differential analysis experiments in order to explore the effects of intra-condition count variation on the detection of differentially expressed transcripts. The individual count and abundance datasets for each corresponding RNA-seq dataset are available here.</p> <p> </p> <p>A preprint of Lobo et al., 2022, currently under review for PLOS ONE, is available [10]. The preprint however does not contain reviewer requested information on simulations as this, along with other additions including an additional author RL, has been subsequently added during the review process. These additions will be made available following review via a link to the final paper. </p> <p> </p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a> <br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a> <</p> <p> </p> <p>General details of the projects involved are available: <a href="https://cibio.up.pt/en/projects/is-hybridization-between-wolves-and-dogs-shaping-the-evolutionary-trajectory-of-wolf-populations-in-human-dominated-landscapes/">dog-wolf</a> and <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">chimerism</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Wang X, Pipes L, Trut L, Herbeck Y, Vladimirova A, Gulevich R, et al. Genomic responses to selection for tame/aggressive behaviors in the silver fox (Vulpes vulpes). Proc Natl Acad Sci. 2018;115: 10398–10403. doi:10.1073/pnas.1800889115</p> <p> </p> <p>2. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, et al. Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. 2013;24: 484–499. doi:10.1007/s00335-013-9480-0</p> <p> </p> <p>3. Fushan AA, Turanov AA, Lee SG, Kim EB, Lobanov A V, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14: 352–365. doi:10.1111/acel.12283</p> <p> </p> <p>4. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>5. Albert FW, Somel M, Carneiro M, Aximu-Petri A, Halbwax M, Thalmann O, et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. Akey JM, editor. PLoS Genet. 2012;8:e1002962. doi:10.1371/journal.pgen.1002962</p> <p> </p> <p>6. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172</p> <p> </p> <p>7. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p> </p> <p>8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012. doi:10.1038/nmeth.1923</p> <p> </p> <p>9. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016 345. 2016;34: 525–527. doi:10.1038/nbt.3519</p> <p> </p> <p>10. Lobo D, Godinho R, Archer JP. On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves. bioRxiv. 2022; 2022.01.24.477470. doi:10.1101/2022.01.24.477470</p>
A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures
Open the record for dataset details and reuse information.
Data from: Wildlife differentially affect tree and liana regeneration in a tropical forest: an 18‐yr study of experimental terrestrial defaunation versus artificially abundant herbivores
Open the record for dataset details and reuse information.
Data from: Models for assessing local-scale co-abundance of animal species while accounting for differential detectability and varied responses to the environment
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.