Tests for checking Batch Effects
Batch 180305 | |
---|---|
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. | 0.319 | 0.319 | 0 |
Median | 1.407 | 1.407 | 0 |
Mean | 3.165 | 3.165 | 0 |
3rd Qu. | 4.232 | 4.232 | 0 |
Max. | 44.7 | 44.7 | 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 | 0.0006709 | 0.3584 | 0.599 | 0.5733 | 0.8028 | 1 | 0.01735 |
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 | |
---|---|---|---|---|---|---|
EXTL2 | 29.33 | 263 | 3.864 | 0.0009237 | 1 | -4.593 |
SIGLEC1 | 18.23 | 14.45 | 3.384 | 0.002857 | 1 | -4.593 |
SRM | -65.63 | 519.5 | -3.363 | 0.002998 | 1 | -4.593 |
PLA2G2D | 13.87 | 13.14 | 3.214 | 0.004236 | 1 | -4.594 |
TBXAS1 | -20.02 | 106.8 | -3.212 | 0.004253 | 1 | -4.594 |
PLA2G2F | 11.63 | 58.95 | 3.199 | 0.004379 | 1 | -4.594 |
ISPD | 39.95 | 277.9 | 3.118 | 0.00528 | 1 | -4.594 |
PAM | 596 | 4391 | 3.096 | 0.00555 | 1 | -4.594 |
IL1R2 | 11.38 | 13.09 | 3.061 | 0.006013 | 1 | -4.594 |
LOC100862671 | -5.417 | 14.95 | -3.039 | 0.006322 | 1 | -4.594 |
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 | 32.33 | 32.33 | 0.9 | 0.9 | 0.6757 | 0 | 1 |
PC2 | 10.44 | 42.76 | 3.4 | 3.4 | 0.4109 | 0 | 1 |
PC3 | 7.226 | 49.99 | 0 | 0 | 0.9426 | 0 | 1 |
PC4 | 5.074 | 55.06 | 8.8 | 8.8 | 0.1811 | 0 | 1 |
PC5 | 4.909 | 59.97 | 2.5 | 2.5 | 0.4818 | 0 | 1 |
PC6 | 4.513 | 64.49 | 3.2 | 3.2 | 0.4267 | 0 | 1 |
PC7 | 3.915 | 68.4 | 1 | 1 | 0.6512 | 0 | 1 |
PC8 | 3.106 | 71.51 | 12.1 | 12.1 | 0.1134 | 0 | 1 |
PC9 | 2.901 | 74.41 | 0.4 | 0.4 | 0.7824 | 0 | 1 |
PC10 | 2.647 | 77.05 | 0.1 | 0.1 | 0.9007 | 0 | 1 |
PC11 | 2.483 | 79.54 | 0 | 0 | 0.989 | 0 | 1 |
PC12 | 2.368 | 81.9 | 0.8 | 0.8 | 0.6922 | 0 | 1 |
PC13 | 2.23 | 84.13 | 0.7 | 0.7 | 0.7185 | 0 | 1 |
PC14 | 2.173 | 86.31 | 27.4 | 27.4 | 0.01251 | 0 | 1 |
PC15 | 2.121 | 88.43 | 4.7 | 4.7 | 0.3317 | 0 | 1 |
PC16 | 2.099 | 90.53 | 3.7 | 3.7 | 0.3916 | 0 | 1 |
PC17 | 2.081 | 92.61 | 0.3 | 0.3 | 0.8218 | 0 | 1 |
PC18 | 1.921 | 94.53 | 13.5 | 13.5 | 0.09197 | 0 | 1 |
PC19 | 1.885 | 96.41 | 1.1 | 1.1 | 0.6352 | 0 | 1 |
PC20 | 1.85 | 98.26 | 15.4 | 15.4 | 0.071 | 0 | 1 |
PC21 | 1.736 | 100 | 0.1 | 0.1 | 0.8925 | 0 | 1 |
PC22 | 6.567e-29 | 100 | 0.5 | 0.5 | 0.748 | 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: 3