Tests for checking Batch Effects
Batch 180223 | |
---|---|
Condition crowned | 10 |
Condition worker | 9 |
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. | 1.721 | 1.721 | 0 |
Median | 6.059 | 6.059 | 0 |
Mean | 8.418 | 8.418 | 0 |
3rd Qu. | 12.25 | 12.25 | 0 |
Max. | 65.34 | 65.34 | 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 | 2.816e-05 | 0.1419 | 0.3097 | 0.379 | 0.5925 | 1 | 0.09164 |
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 | |
---|---|---|---|---|---|---|
PABPC4L | 33.71 | 56.37 | 5.635 | 2.657e-05 | 0.2124 | -3.08 |
ZMAT3 | 172.7 | 292.5 | 5.422 | 4.128e-05 | 0.2124 | -3.13 |
DGKH | 64.12 | 128.5 | 5.419 | 4.159e-05 | 0.2124 | -3.131 |
PIEZO1 | 619.8 | 1029 | 5.28 | 5.556e-05 | 0.2128 | -3.165 |
CYP7B1 | 79.16 | 122.9 | 4.962 | 0.0001089 | 0.3338 | -3.248 |
BMPR2 | 308.2 | 576.1 | 4.656 | 0.0002104 | 0.4773 | -3.335 |
KIAA1045 | 44.9 | 81.37 | 4.639 | 0.0002181 | 0.4773 | -3.339 |
LMBRD2 | 173.1 | 306.9 | 4.379 | 0.0003835 | 0.4843 | -3.418 |
ABCA1 | 881.6 | 2577 | 4.321 | 0.0004352 | 0.4843 | -3.436 |
BCL2L11 | 142.4 | 270.5 | 4.291 | 0.0004651 | 0.4843 | -3.445 |
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 | 42.71 | 42.71 | 8.1 | 8.1 | 0.2388 | 0 | 1 |
PC2 | 20.21 | 62.92 | 11.6 | 11.6 | 0.1539 | 0 | 1 |
PC3 | 8.948 | 71.87 | 15.1 | 15.1 | 0.1001 | 0 | 1 |
PC4 | 3.673 | 75.54 | 0.1 | 0.1 | 0.877 | 0 | 1 |
PC5 | 2.888 | 78.43 | 15.9 | 15.9 | 0.0909 | 0 | 1 |
PC6 | 2.881 | 81.31 | 0.1 | 0.1 | 0.8766 | 0 | 1 |
PC7 | 2.086 | 83.4 | 0.2 | 0.2 | 0.8576 | 0 | 1 |
PC8 | 1.997 | 85.4 | 13.3 | 13.3 | 0.1241 | 0 | 1 |
PC9 | 1.938 | 87.33 | 0 | 0 | 0.9871 | 0 | 1 |
PC10 | 1.815 | 89.15 | 17.3 | 17.3 | 0.07647 | 0 | 1 |
PC11 | 1.678 | 90.83 | 1.2 | 1.2 | 0.655 | 0 | 1 |
PC12 | 1.557 | 92.38 | 0 | 0 | 0.9769 | 0 | 1 |
PC13 | 1.398 | 93.78 | 1.6 | 1.6 | 0.6033 | 0 | 1 |
PC14 | 1.37 | 95.15 | 1.4 | 1.4 | 0.6279 | 0 | 1 |
PC15 | 1.286 | 96.44 | 0.8 | 0.8 | 0.7136 | 0 | 1 |
PC16 | 1.229 | 97.67 | 2.2 | 2.2 | 0.5433 | 0 | 1 |
PC17 | 1.177 | 98.84 | 8.9 | 8.9 | 0.214 | 0 | 1 |
PC18 | 1.156 | 100 | 2 | 2 | 0.561 | 0 | 1 |
PC19 | 6.993e-29 | 100 | 15 | 15 | 0.1012 | 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