| Title: | A data package for timecourse RNA-seq and microarray gene expression data sets |
|---|---|
| Description: | This data package contains timecourse gene expression data sets. The first dataset, from Shoemaker et al, consists of microarray samples from lung tissue of mice exposed to different influenzy strains from 14 timepoints. The two other datasets are leaf and root samples from sorghum crops exposed to pre- and post-flowering drought stress and a control condition, sampled across the plants lifetime. |
| Authors: | Elizabeth Purdom [aut] (ORCID: <https://orcid.org/0000-0001-9455-7990>), Nelle Varoquaux [aut, cre] (ORCID: <https://orcid.org/0000-0002-8748-6546>) |
| Maintainer: | Nelle Varoquaux <[email protected]> |
| License: | BSD 3-clause License + file LICENSE |
| Version: | 1.23.0 |
| Built: | 2026-10-10 10:42:16 UTC |
| Source: | https://github.com/bioc/timecoursedata |
See data(shoemaker2015) for a full description of the dataset and the metadata.
load_shoemaker2015()load_shoemaker2015()
a SummerizedExperiment of the Shoemaker 2015 data
mice_timecourse_data = load_shoemaker2015() head(mice_timecourse_data)mice_timecourse_data = load_shoemaker2015() head(mice_timecourse_data)
See data(varoquaux2019leaf) or data(varoquaux2019root) for a full description of the dataset and the metadata.
load_varoquaux2019(sample_type = "leaf")load_varoquaux2019(sample_type = "leaf")
sample_type |
"leaf", "root", optional, default: leaf Sets which sample to load. |
a SummerizedExperiment of the leaf or root samples of
Varoquaux et al.
sorghum_timecourse_data = load_varoquaux2019(sample_type="leaf") head(sorghum_timecourse_data)sorghum_timecourse_data = load_varoquaux2019(sample_type="leaf") head(sorghum_timecourse_data)
Time-course microarray data from "An Ultrasensitive Mechanism Regulates Influenza Virus-Induced Inflammation".
data(shoemaker2015)data(shoemaker2015)
A list with two related datasets: a gene expression data set (element
data) and corresponding meta data (element meta). The column
names of data correspond to the rownames of meta, linking the
samples together in the two datasets. See examples for accessing these two
data.frames.
This is data from a micro-array time-course experiment, exposing mice to three different strains of influenza, and collecting lung tissue during 14 time-points after infection (0, 3, 6, 9, 12, 18, 24, 30, 36, 48, 60 hours, then 3, 5, and 7 days later) [8]. The three strains of influenza used in the study are (1) a low pathogenicity seasonal H1N1 influenza virus (A/Kawasaki/UTK4/2009 [H1N1]), a mildly pathogenic virus from the 2009 pandemic season (A/California/04/2009 [H1N1]), and a highly pathogenic H5N1 avian influenza virus (A/Vietnam/1203/2004 [H5N1]. Mice were injected with 105 PFU of each virus. An additional 42 mice were injected with a lower dose of the Vietnam avian influenza virus (103 PFU).
data is a data frame with 39544 rows corresponding to genes
and 209 corresponding to samples. The rownames give the RefSeq name of the gene.
meta is a data frame with 209 rows corresponding to samples
and 3 named columns (the first column is just an index from 0-208):
The treatment group of the sample. "C"=Control, "K"=Kawasaki strain, "M"=California strain, "VH"=Vietnam strain, "VL"=Vietnam at lower dosage (103 PFU).
Identifies the replicate – each combination of treatment and timepoint was replicated three times (except for VH at timepoint 3, which has only 2 replicates).
Identifies the time passed (in days) since infection of the sample
Shoemaker et al. (2015) PLOS Pathog. (PubMed)
data(shoemaker2015) data = attr(shoemaker2015, "data") meta = attr(shoemaker2015, "meta")data(shoemaker2015) data = attr(shoemaker2015, "data") meta = attr(shoemaker2015, "meta")
Time-course RNA-seq leaf and root data from "Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses"
data(varoquaux2019leaf) data(varoquaux2019root)data(varoquaux2019leaf) data(varoquaux2019root)
A list with two related datasets: a gene expression data set (element
data) and corresponding meta data (element meta). The column
names of data correspond to the rownames of meta, linking the
samples together in the two datasets. See examples for accessing these two
data.frames.
This are two datasets from a mRNA-Seq time-course experiment,
measuring gene expression across time in the plant sorghum. One dataset
corresponds to the leaf of the plant (varoquaux2019leaf), the other
to the root from the same sets of plants (varoquaux2019leaf). Each
sample sequenced is actually a pool of at least 10 plants. Note that leaf
and root samples were processed and sequenced as two separate batches.
Any comparison between the two datasets should be traded with care.
data is a data frame with 34,211 rows corresponding to
sorghum genes with
(Phytozome
v3.0 gene ids). Root has 198 columns and leaf has 197 columns,
corresponding to samples. The root and leaf samples are from the same
plants, and have ids in the form of "0622162L05" or "0622162R05", with "L"
corresponding to the leaf and "R" corresponding to the root sample. Some
samples were removed for quality control reasons, resulting in slightly
different numbers of samples between the leaf and root.
The data provided is the raw data. Note that leafs and roots were sequenced as two different batches.
meta is a data frame with rows corresponding to samples and
and columns with meta data. These columns are the same across leaf and
root, allowing for easy reuse of code across the datasets:
Barcode of the sample, same as row name
Internal Barcode given to the sample by the sequencing facility (JGI)
Plot from which the sample was taken
Numeric value corresponding to the week sample was taken
Each condition combination was replicated three times, but not all replicates were sequenced. Each replicate came from a different plot. The replicate id tracks this plot for each condition. However, replicate 1 in one condition is a completely different plot from replicate 1 in another condition, though the replicates were physically close to each other in the field.
Genotype (i.e. variety) of the sample, one of BT642 or RT430
The drought condition, one of "Control" (regular watering), "Postflowering" (drought applied post flowering of the plant), or "Preflowering" (drought applied pre-flowering of the plant, and then watering resumed after flowering)
Either "Leaves" or "Root", only relevant if merging the two datasets
The day sample collected, in form of "Day14" (corresponding to Week 2)
Row plant was sampled from within the block
logical, indicating whether the week sampled corresponding to the plant flowering
The date sample was collected
The time of day plant was collected
The number of plants pooled for the sample
The Julian date of flowering for the block the sample was collected from
The date of flowering for the block the sample was collected from
The week of flowering for the block the sample was collected from
The day of flowering for the block the sample was collected from
Identifies timepoints with no replicates
Full set of factors (Genotype x Treatment x Week)
The following variables are features measured at the end of planting when the remaining plants from the plot were harvested:
The following variables are features regarding the quality of the mapping of the sequences from the sample:
Varoquaux et al, 2019 (PNAS)
data(varoquaux2019leaf) dataLeaf = attr(varoquaux2019leaf, "data") metaLeaf = attr(varoquaux2019leaf, "meta") data(varoquaux2019root) dataRoot = attr(varoquaux2019root, "data") metaRoot = attr(varoquaux2019root, "meta")data(varoquaux2019leaf) dataLeaf = attr(varoquaux2019leaf, "data") metaLeaf = attr(varoquaux2019leaf, "meta") data(varoquaux2019root) dataRoot = attr(varoquaux2019root, "data") metaRoot = attr(varoquaux2019root, "meta")