| Title: | Datasets for Multi-Omics Integration in a Plant Abiotic Stress Context |
|---|---|
| Description: | Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>. |
| Authors: | Sébastien Déjean [aut, cre], Harold Duruflé [aut], Aurélie Mercadié [aut] |
| Maintainer: | Sébastien Déjean <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0 |
| Built: | 2025-02-11 04:56:43 UTC |
| Source: | https://github.com/cran/WallomicsData |
The Altitude Cluster factor identifies the environment height from which is originated a given plant from the sample under study, either high altitude (denoted High), moderate altitude (Low) or the reference group's environment height (Col, the lowest of all).
data("Altitude_Cluster")data("Altitude_Cluster")
A factor with 3 levels.
# Load the data data("Altitude_Cluster") # Count how many samples are in each group table(Altitude_Cluster)# Load the data data("Altitude_Cluster") # Count how many samples are in each group table(Altitude_Cluster)
The Ecotype factor identifies the genotype specifically designed for a given ecosystem of the A. thaliana from which the studied sample comes from. We have a population of reference as well as 4 newly-described Pyrenean populations, namely:
Columbia, denoted Col (originating from Poland, acts as the reference ecotype)
Grip, denoted Grip
Herran, denoted Hern
L’Hospitalet-près-l’Andorre, denoted Hosp
Chapelle Saint Roch, denoted Roch
data("Ecotype")data("Ecotype")
A factor with 5 levels of A. thaliana genotypes.
# Load the data data("Ecotype") # Count how many samples are in each group table(Ecotype)# Load the data data("Ecotype") # Count how many samples are in each group table(Ecotype)
The Genetic Cluster factor identifies the genetic group from which comes from the studied sample, either Genetics Cluster 1 (constitued of Grip and Roch genotypes), Genetics Cluster 2 (Hern and Hosp genotypes) or Genetics Cluster 3 (Col genotype). See Ecotype for more information on genotypes.
data("Genetic_Cluster")data("Genetic_Cluster")
A factor with 3 levels.
# Load the data data("Genetic_Cluster") # Count how many samples are in each group table(Genetic_Cluster)# Load the data data("Genetic_Cluster") # Count how many samples are in each group table(Genetic_Cluster)
A dataset containing metabolomics variables measured on rosettes of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Metabolomics_Rosettes")data("Metabolomics_Rosettes")
A data frame with 30 rows and 6 variables:
Pectin_RGI: Rhamnogalacturonan I (µg/100mg)
Pectin_HG: Homogalacturonan (µg/100mg)
XG: Xyloglucan (µg/100mg)
Pectin_linearity: Linearity of pectin (Ratio)
Contribution_RG: Contribution of rhamnogalacturonan to pectin population (Ratio)
RGI_branching: Branching of Rhamnogalacturonan I (Ratio)
# Load the dataset data("Metabolomics_Rosettes") # Look at simple statistics summary(Metabolomics_Rosettes) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation plot(x = as.factor(substr(row.names(Metabolomics_Rosettes), 1, 7)), y = Metabolomics_Rosettes$Pectin_linearity, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Pectin linearity (Ratio)", main = "Pectin linearity distribution by genotype and growth temperature") grid() abline(h = 1, lty = 2) points(x = as.factor(substr(row.names(Metabolomics_Rosettes), 1, 7)), y = Metabolomics_Rosettes$Pectin_linearity, type = "p", pch = 19, lwd = 5, col = colors)# Load the dataset data("Metabolomics_Rosettes") # Look at simple statistics summary(Metabolomics_Rosettes) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation plot(x = as.factor(substr(row.names(Metabolomics_Rosettes), 1, 7)), y = Metabolomics_Rosettes$Pectin_linearity, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Pectin linearity (Ratio)", main = "Pectin linearity distribution by genotype and growth temperature") grid() abline(h = 1, lty = 2) points(x = as.factor(substr(row.names(Metabolomics_Rosettes), 1, 7)), y = Metabolomics_Rosettes$Pectin_linearity, type = "p", pch = 19, lwd = 5, col = colors)
A dataset containing metabolomics variables measured on floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Metabolomics_Stems")data("Metabolomics_Stems")
A data frame with 30 rows and 6 variables:
Pectin_RGI: Rhamnogalacturonan I (µg/100mg)
Pectin_HG: Homogalacturonan (µg/100mg)
XG: Xyloglucan (µg/100mg)
Pectin_linearity: Linearity of pectin (Ratio)
Contribution_RG: Contribution of rhamnogalacturonan to pectin population (Ratio)
RGI_branching: Branching of Rhamnogalacturonan I (Ratio)
# Load the dataset data("Metabolomics_Stems") # Look at simple statistics summary(Metabolomics_Stems) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation plot(x = as.factor(substr(row.names(Metabolomics_Stems), 1, 7)), y = Metabolomics_Stems$Pectin_linearity, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Pectin linearity (Ratio)", main = "Pectin linearity distribution by genotype and growth temperature") grid() abline(h = 1, lty = 2) points(x = as.factor(substr(row.names(Metabolomics_Stems), 1, 7)), y = Metabolomics_Stems$Pectin_linearity, type = "p", pch = 19, lwd = 5, col = colors)# Load the dataset data("Metabolomics_Stems") # Look at simple statistics summary(Metabolomics_Stems) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation plot(x = as.factor(substr(row.names(Metabolomics_Stems), 1, 7)), y = Metabolomics_Stems$Pectin_linearity, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Pectin linearity (Ratio)", main = "Pectin linearity distribution by genotype and growth temperature") grid() abline(h = 1, lty = 2) points(x = as.factor(substr(row.names(Metabolomics_Stems), 1, 7)), y = Metabolomics_Stems$Pectin_linearity, type = "p", pch = 19, lwd = 5, col = colors)
Bioinformatics Annotation and description, using the WallProtDB database, of all the Cell Wall Proteins (CWPs) identified on rosettes and floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for additionnal information.
data("Metadata")data("Metadata")
A data frame with 474 rows and 4 variables:
Acc_number: GenBank accession number (gene name)
Functional_classes: Functional classes of the CWPs
Protein_families: Protein families of the CWPs
Putative_functions: Putative functions of the CWPs
# Load the dataset data("Metadata") # Look at the dataset's dimensions dim(Metadata) head(Metadata) # How many functional classes ? table(Metadata$Functional_classes) # How many protein families ? table(Metadata$Protein_families)# Load the dataset data("Metadata") # Look at the dataset's dimensions dim(Metadata) head(Metadata) # How many functional classes ? table(Metadata$Functional_classes) # How many protein families ? table(Metadata$Protein_families)
A dataset containing phenotypic variables measured on rosettes of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Phenomics_Rosettes")data("Phenomics_Rosettes")
A data frame with 30 rows and 5 variables:
Mass: rosette mass (g)
Diameter: rosette diameter (cm)
Leaves_number: total number of leaves
Density: rosette density (g/cm²)
Area: projected rosette area (cm²)
# Load the data data("Phenomics_Rosettes") # Look at simple statistics dim(Phenomics_Rosettes) summary(Phenomics_Rosettes) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation: Leaves number distribution plot(x = as.factor(substr(row.names(Phenomics_Rosettes), 1, 7)), y = Phenomics_Rosettes$Leaves_number, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Number of rosette leaves", main = "Rosette leaves' distribution by genotype and growth temperature" ) grid() points(x = as.factor(substr(row.names(Phenomics_Rosettes), 1, 7)), y = Phenomics_Rosettes$Leaves_number, type = "p", pch = 19, lwd = 5, col = colors)# Load the data data("Phenomics_Rosettes") # Look at simple statistics dim(Phenomics_Rosettes) summary(Phenomics_Rosettes) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation: Leaves number distribution plot(x = as.factor(substr(row.names(Phenomics_Rosettes), 1, 7)), y = Phenomics_Rosettes$Leaves_number, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Number of rosette leaves", main = "Rosette leaves' distribution by genotype and growth temperature" ) grid() points(x = as.factor(substr(row.names(Phenomics_Rosettes), 1, 7)), y = Phenomics_Rosettes$Leaves_number, type = "p", pch = 19, lwd = 5, col = colors)
A dataset containing phenotypic variables measured on floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Phenomics_Stems")data("Phenomics_Stems")
A data frame with 30 rows and 4 variables:
Mass: floral stems mass (g)
Diameter: floral stems diameter (mm)
Length: length of the floral stems (cm)
Number_lateral_stems: number of lateral stems)
# Load the data data("Phenomics_Stems") # Look at simple statistics dim(Phenomics_Stems) summary(Phenomics_Stems) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation: Lateral stems distribution plot(x = as.factor(substr(row.names(Phenomics_Stems), 1, 7)), y = Phenomics_Stems$Number_lateral_stems, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Number of lateral stems", main = "Lateral stems' distribution by genotype and growth temperature" ) grid() points(x = as.factor(substr(row.names(Phenomics_Stems), 1, 7)), y = Phenomics_Stems$Number_lateral_stems, type = "p", pch = 19, lwd = 5, col = colors)# Load the data data("Phenomics_Stems") # Look at simple statistics dim(Phenomics_Stems) summary(Phenomics_Stems) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # A graphical representation: Lateral stems distribution plot(x = as.factor(substr(row.names(Phenomics_Stems), 1, 7)), y = Phenomics_Stems$Number_lateral_stems, col = "white", lty = 0, xlab = "Genotype x Temperature groups", ylab = "Number of lateral stems", main = "Lateral stems' distribution by genotype and growth temperature" ) grid() points(x = as.factor(substr(row.names(Phenomics_Stems), 1, 7)), y = Phenomics_Stems$Number_lateral_stems, type = "p", pch = 19, lwd = 5, col = colors)
A dataset containing the identification and quantification of Cell Wall Proteins (CWPs) performed using LC-MS/MS analysis on rosettes of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for additional information.
data("Proteomics_Rosettes_CW")data("Proteomics_Rosettes_CW")
A data frame with 30 rows and 364 variables.
# Load the dataset data("Proteomics_Rosettes_CW") # Look at data frame dimensions dim(Proteomics_Rosettes_CW) # Look at the first rows and columns head(Proteomics_Rosettes_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on proteomics res.pca <- prcomp(Proteomics_Rosettes_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes Cell Wall Proteomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Proteomics_Rosettes_CW") # Look at data frame dimensions dim(Proteomics_Rosettes_CW) # Look at the first rows and columns head(Proteomics_Rosettes_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on proteomics res.pca <- prcomp(Proteomics_Rosettes_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes Cell Wall Proteomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)
A dataset containing the identification and quantification of Cell Wall Proteins (CWPs) performed using LC-MS/MS analysis on floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for additionnal information.
data("Proteomics_Stems_CW")data("Proteomics_Stems_CW")
A data frame with 30 rows and 414 variables.
# Load the dataset data("Proteomics_Stems_CW") # Look at data frame dimensions dim(Proteomics_Stems_CW) # Look at the first rows and columns head(Proteomics_Stems_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on proteomics res.pca <- prcomp(Proteomics_Stems_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems Cell Wall Proteomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Proteomics_Stems_CW") # Look at data frame dimensions dim(Proteomics_Stems_CW) # Look at the first rows and columns head(Proteomics_Stems_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on proteomics res.pca <- prcomp(Proteomics_Stems_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems Cell Wall Proteomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)
The Temperature factor identifies the temperature at which the studied sample was exposed all along its growth, either 22°C (optimal condition) or 15°C (high altitude condition).
data("Temperature")data("Temperature")
A factor with 2 levels.
# Load the data data("Temperature") # Count how many samples are in each group table(Temperature)# Load the data data("Temperature") # Count how many samples are in each group table(Temperature)
A dataset containing all the transcripts obtained by RNA-seq performed, according to the standard Illumina protocols, on rosettes of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Transcriptomics_Rosettes")data("Transcriptomics_Rosettes")
A data frame with 30 rows and 19763 variables.
# Load the dataset data("Transcriptomics_Rosettes") # Look at data frame dimensions dim(Transcriptomics_Rosettes) # Look at the first rows and columns head(Transcriptomics_Rosettes[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Rosettes, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes' Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Transcriptomics_Rosettes") # Look at data frame dimensions dim(Transcriptomics_Rosettes) # Look at the first rows and columns head(Transcriptomics_Rosettes[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Rosettes, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes' Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)
A dataset containing the transcripts encoding Cell Wall Proteins (CWPs) sorted from the 19 763 transcripts (see Transcriptomics_Rosettes) obtained by RNA-seq performed, according to the standard Illumina protocols, on rosettes of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Transcriptomics_Rosettes_CW")data("Transcriptomics_Rosettes_CW")
A data frame with 30 rows and 364 variables.
# Load the dataset data("Transcriptomics_Rosettes_CW") # Look at data frame dimensions dim(Transcriptomics_Rosettes_CW) # Look at the first rows and columns head(Transcriptomics_Rosettes_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Rosettes_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes Cell Wall Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Transcriptomics_Rosettes_CW") # Look at data frame dimensions dim(Transcriptomics_Rosettes_CW) # Look at the first rows and columns head(Transcriptomics_Rosettes_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Rosettes_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Rosettes Cell Wall Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)
A dataset containing all the transcripts obtained by RNA-seq performed, according to the standard Illumina protocols, on floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Transcriptomics_Stems")data("Transcriptomics_Stems")
A data frame with 30 rows and 22570 variables.
# Load the dataset data("Transcriptomics_Stems") # Look at data frame dimensions dim(Transcriptomics_Stems) # Look at the first rows and columns head(Transcriptomics_Stems[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Stems, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems' Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Transcriptomics_Stems") # Look at data frame dimensions dim(Transcriptomics_Stems) # Look at the first rows and columns head(Transcriptomics_Stems[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Stems, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems' Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)
A dataset containing the transcripts encoding Cell Wall Proteins (CWPs) sorted from the 22 570 transcripts (see Transcriptomics_Stems) obtained by RNA-seq performed, according to the standard Illumina protocols, on floral stems of five A. thaliana genotypes at two growth temperatures. See Ecotype and Temperature for more information.
data("Transcriptomics_Stems_CW")data("Transcriptomics_Stems_CW")
A data frame with 30 rows and 414 variables.
# Load the dataset data("Transcriptomics_Stems_CW") # Look at data frame dimensions dim(Transcriptomics_Stems_CW) # Look at the first rows and columns head(Transcriptomics_Stems_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Stems_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems Cell Wall Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)# Load the dataset data("Transcriptomics_Stems_CW") # Look at data frame dimensions dim(Transcriptomics_Stems_CW) # Look at the first rows and columns head(Transcriptomics_Stems_CW[,c(1:10)]) # Create a colors' vector colors <- c(rep("#A6CEE3",3), rep("#1F78B4",3), rep("#B2DF8A",3), rep("#33A02C",3), rep("#FB9A99",3), rep("#E31A1C",3), rep("#FDBF6F",3), rep("#FF7F00",3), rep("#CAB2D6",3), rep("#6A3D9A",3)) # PCA on transcriptomics res.pca <- prcomp(Transcriptomics_Stems_CW, center = TRUE, scale. = TRUE) plot(res.pca$x[,"PC1"], res.pca$x[,"PC2"], pch = 19, xlab = "PC1", ylab = "PC2", lwd = 5, main = "Individuals' plot (1 x 2) - PCA on Stems Cell Wall Transcriptomics", col = colors) text(res.pca$x[,"PC1"], res.pca$x[,"PC2"], labels = row.names(res.pca$x), cex = 0.8, pos = 3)