Tests for checking Batch Effects
Batch 180223 | |
---|---|
Condition crowned | 12 |
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. | 0.9878 | 0.9878 | 0 |
Median | 4.372 | 4.372 | 0 |
Mean | 8.139 | 8.139 | 0 |
3rd Qu. | 11.88 | 11.88 | 0 |
Max. | 66.93 | 66.93 | 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 | 5.866e-06 | 0.1261 | 0.363 | 0.4075 | 0.6681 | 1 | 0.1353 |
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 | |
---|---|---|---|---|---|---|
WWC2 | 1483 | 2433 | 6.291 | 4.269e-06 | 0.06459 | -2.668 |
SETDB2 | 115.3 | 277.4 | 5.815 | 1.191e-05 | 0.06459 | -2.791 |
PFKFB2 | 2137 | 4688 | 5.793 | 1.25e-05 | 0.06459 | -2.797 |
FBXO48 | 84.14 | 146.8 | 5.509 | 2.339e-05 | 0.07355 | -2.878 |
BCL6 | 536.3 | 1218 | 5.502 | 2.373e-05 | 0.07355 | -2.88 |
ST8SIA1 | 115.7 | 177.7 | 5.27 | 3.987e-05 | 0.08294 | -2.95 |
TMEM68 | 141.5 | 541.4 | 5.133 | 5.425e-05 | 0.08294 | -2.993 |
DCP2 | 68.5 | 201.5 | 5.114 | 5.658e-05 | 0.08294 | -2.999 |
SLC9A7 | 468.5 | 1054 | 5.112 | 5.685e-05 | 0.08294 | -2.999 |
RND3 | 2480 | 6411 | 5.036 | 6.747e-05 | 0.08294 | -3.024 |
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 | 31.62 | 31.62 | 10.3 | 10.3 | 0.1559 | 0 | 1 |
PC2 | 10.55 | 42.17 | 6.3 | 6.3 | 0.273 | 0 | 1 |
PC3 | 9.399 | 51.57 | 8.8 | 8.8 | 0.1919 | 0 | 1 |
PC4 | 7.492 | 59.06 | 22.9 | 22.9 | 0.02808 | 0 | 1 |
PC5 | 6.354 | 65.42 | 10.4 | 10.4 | 0.1534 | 0 | 1 |
PC6 | 3.896 | 69.31 | 1.9 | 1.9 | 0.5524 | 0 | 1 |
PC7 | 3.718 | 73.03 | 2.1 | 2.1 | 0.5346 | 0 | 1 |
PC8 | 3.284 | 76.31 | 1.9 | 1.9 | 0.5487 | 0 | 1 |
PC9 | 2.776 | 79.09 | 5.8 | 5.8 | 0.2917 | 0 | 1 |
PC10 | 2.727 | 81.82 | 5.6 | 5.6 | 0.3034 | 0 | 1 |
PC11 | 2.531 | 84.35 | 7.8 | 7.8 | 0.2187 | 0 | 1 |
PC12 | 2.172 | 86.52 | 1.9 | 1.9 | 0.553 | 0 | 1 |
PC13 | 2.041 | 88.56 | 0.9 | 0.9 | 0.6768 | 0 | 1 |
PC14 | 2.018 | 90.58 | 2.2 | 2.2 | 0.5187 | 0 | 1 |
PC15 | 1.774 | 92.35 | 5 | 5 | 0.3295 | 0 | 1 |
PC16 | 1.724 | 94.08 | 0 | 0 | 0.9522 | 0 | 1 |
PC17 | 1.657 | 95.73 | 2.8 | 2.8 | 0.4685 | 0 | 1 |
PC18 | 1.503 | 97.24 | 0.1 | 0.1 | 0.8779 | 0 | 1 |
PC19 | 1.468 | 98.71 | 1.2 | 1.2 | 0.6397 | 0 | 1 |
PC20 | 1.294 | 100 | 2 | 2 | 0.5436 | 0 | 1 |
PC21 | 4.04e-29 | 100 | 9.3 | 9.3 | 0.1785 | 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: 1