Tests for checking Batch Effects
Batch 180320 | |
---|---|
Condition crowned | 12 |
Condition worker | 10 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | NA | NA |
Full (Condition+Batch) | Condition | Batch | |
---|---|---|---|
Min. | 0 | 0 | 0 |
1st Qu. | 0.421 | 0.421 | 0 |
Median | 1.605 | 1.605 | 0 |
Mean | 2.987 | 2.987 | 0 |
3rd Qu. | 3.925 | 3.925 | 0 |
Max. | 45.88 | 45.88 | 0 |
Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | Ps<0.05 | |
---|---|---|---|---|---|---|---|
Batch P-values | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
Condition P-values | 0.0005341 | 0.3768 | 0.5742 | 0.5655 | 0.7743 | 1 | 0.01333 |
Boxplots for all values for each of the samples and are colored by batch membership.
Condition: worker (logFC) | AveExpr | t | P.Value | adj.P.Val | B | |
---|---|---|---|---|---|---|
HIST1H1B | -4.483 | 11.05 | -4.022 | 0.0006384 | 0.9967 | -4.582 |
LSM7 | -20.47 | 44.36 | -3.673 | 0.001457 | 0.9967 | -4.584 |
HMGB3 | -7.3 | 14.18 | -3.475 | 0.002316 | 0.9967 | -4.585 |
CHMP2A | -382.2 | 1091 | -3.446 | 0.00248 | 0.9967 | -4.585 |
PSMD7 | -40.15 | 114.5 | -3.263 | 0.003792 | 0.9967 | -4.586 |
ATP5L | -161 | 529.9 | -3.162 | 0.004786 | 0.9967 | -4.586 |
FN1 | 371.9 | 456.5 | 3.079 | 0.005795 | 0.9967 | -4.586 |
ANAPC15 | -135.4 | 422 | -3.022 | 0.006598 | 0.9967 | -4.587 |
CHP1 | -300 | 867.7 | -2.948 | 0.007797 | 0.9967 | -4.587 |
ATP5B | -833.4 | 2752 | -2.926 | 0.008196 | 0.9967 | -4.587 |
This plot helps identify outlying samples.
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
Proportion of Variance (%) | Cumulative Proportion of Variance (%) | Percent Variation Explained by Either Condition or Batch | Percent Variation Explained by Condition | Condition Significance (p-value) | Percent Variation Explained by Batch | Batch Significance (p-value) | |
---|---|---|---|---|---|---|---|
PC1 | 53.41 | 53.41 | 1.5 | 1.5 | 0.5895 | 0 | 1 |
PC2 | 7.228 | 60.63 | 0.4 | 0.4 | 0.7916 | 0 | 1 |
PC3 | 4.859 | 65.49 | 6 | 6 | 0.2718 | 0 | 1 |
PC4 | 3.904 | 69.4 | 5.1 | 5.1 | 0.3137 | 0 | 1 |
PC5 | 3.41 | 72.81 | 11.1 | 11.1 | 0.1289 | 0 | 1 |
PC6 | 3.181 | 75.99 | 4.7 | 4.7 | 0.3332 | 0 | 1 |
PC7 | 3.002 | 78.99 | 1.6 | 1.6 | 0.5732 | 0 | 1 |
PC8 | 2.529 | 81.52 | 11 | 11 | 0.1308 | 0 | 1 |
PC9 | 2.251 | 83.77 | 1.7 | 1.7 | 0.5678 | 0 | 1 |
PC10 | 2.04 | 85.81 | 10.2 | 10.2 | 0.147 | 0 | 1 |
PC11 | 1.864 | 87.67 | 0.6 | 0.6 | 0.7274 | 0 | 1 |
PC12 | 1.714 | 89.39 | 4.1 | 4.1 | 0.3679 | 0 | 1 |
PC13 | 1.658 | 91.05 | 5.4 | 5.4 | 0.2999 | 0 | 1 |
PC14 | 1.554 | 92.6 | 0.6 | 0.6 | 0.7369 | 0 | 1 |
PC15 | 1.363 | 93.96 | 0 | 0 | 0.9263 | 0 | 1 |
PC16 | 1.29 | 95.25 | 16.7 | 16.7 | 0.05931 | 0 | 1 |
PC17 | 1.255 | 96.51 | 4.1 | 4.1 | 0.3647 | 0 | 1 |
PC18 | 1.027 | 97.54 | 0.1 | 0.1 | 0.9001 | 0 | 1 |
PC19 | 0.969 | 98.5 | 7 | 7 | 0.2343 | 0 | 1 |
PC20 | 0.8343 | 99.34 | 5.3 | 5.3 | 0.3026 | 0 | 1 |
PC21 | 0.661 | 100 | 2.9 | 2.9 | 0.4493 | 0 | 1 |
PC22 | 5.315e-29 | 100 | 11.3 | 11.3 | 0.126 | 0 | 1 |
This is a heatmap plot showing the variation of gene expression mean, variance, skewness and kurtosis between samples grouped by batch to see the batch effects variation
## Note: Sample-wise p-value is calculated for the variation across samples on the measure across genes. Gene-wise p-value is calculated for the variation of each gene between batches on the measure across each batch. If the data is quantum normalized, then the Sample-wise measure across genes is same for all samples and Gene-wise p-value is a good measure.
This is a plot showing whether parametric or non-parameteric prior is appropriate for this data. It also shows the Kolmogorov-Smirnov test comparing the parametric and non-parameteric prior distribution.
## Warning in combatPlot(shinyInput$lcounts, batch = shinyInput$batch, mod = mod): There is no batch
## Number of Surrogate Variables found in the given data: 0