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including variabili > Accounting for this variability dictates the biological accuracy of statistical methods. +

      + +

      + +Image credit: [Nguyen et al, Nat Communnications: Fig 7c](https://www.nature.com/articles/s41467-023-37126-3#Abs1) + ## Comparing DGE results @@ -100,13 +106,14 @@ dge %>% head() ``` ``` - gene log2FC_fm pct.1 pct.2 padj_fm log2FC_deseq2 padj_deseq2 -1 0610009B22Rik 0.4919641 0.238 0.138 6.745178e-07 0.178619741 0.5217744 -2 0610009O20Rik 0.3421628 0.259 0.152 2.299513e-06 0.021075663 0.9539047 -3 0610010K14Rik 0.3505411 0.163 0.088 1.093917e-05 -0.004912021 0.9819494 -4 0610012D04Rik 1.1632519 0.033 0.010 3.048507e-03 0.610698014 0.1015138 -5 0610012G03Rik 0.1019817 0.497 0.379 1.000000e+00 0.040974487 0.8935888 -6 0610030E20Rik 0.6008081 0.147 0.077 6.292494e-06 0.058878725 0.8641972 + gene log2FC_fm pct.1 pct.2 padj_fm log2FC_deseq2 padj_deseq2 sig +1 0610009B22Rik 0.4919641 0.238 0.138 6.745178e-07 0.178619741 0.5217744 FindMarkers +2 0610009O20Rik 0.3421628 0.259 0.152 2.299513e-06 0.021075663 0.9539047 FindMarkers +3 0610010K14Rik 0.3505411 0.163 0.088 1.093917e-05 -0.004912021 0.9819494 FindMarkers +4 0610012D04Rik 1.1632519 0.033 0.010 3.048507e-03 0.610698014 0.1015138 FindMarkers +5 0610012G03Rik 0.1019817 0.497 0.379 1.000000e+00 0.040974487 0.8935888 Not Significant +6 0610030E20Rik 0.6008081 0.147 0.077 6.292494e-06 0.058878725 0.8641972 FindMarkers + ``` We can again visualize how many genes are not significant and the number significant for each method. You'll notice that we have a few genes that listed `NA`, which is the result of DESeq2 giving an NA for the p-value. @@ -206,43 +213,8 @@ ggplot(dge) + Here we can see a pattern where `FindMakers()` finds more differential genes that have a larger difference in the proportion of cells. However, the analagous question can then be asked about what happens at different levels of expression? -To address this question, we start by looking at the single-cell expression values for each -```r -# Difference -avg_exp <- dge %>% select(gene, sig) - -# pct.1 = cold7 -seurat_1 <- subset(seurat_vsm, subset = (condition == "cold7")) -avg_cold7 <- AverageExpression(seurat_1, features=avg_exp$gene, assay="RNA") %>% - data.frame() %>% - rename("RNA"="avg_sc.1") %>% - rownames_to_column("gene") - -# pct.2 = TN -seurat_2 <- subset(seurat_vsm, subset = (condition == "TN")) -avg_TN <- AverageExpression(seurat_2, features=avg_exp$gene, assay="RNA") %>% - data.frame() %>% - rename("RNA"="avg_sc.2") %>% - rownames_to_column("gene") - -# Merge -avg_exp <- merge(avg_exp, merge(avg_cold7, avg_TN), by="gene") - -ggplot(avg_exp, aes(x=avg_sc.1, y=avg_sc.2, color=sig)) + - geom_point() + - scale_x_log10() + scale_y_log10() + - theme_bw() + geom_abline(slope=1) + - labs(x="cold7: Average single-cell expression", - y="TN: Average single-cell expression") -``` - -

      - -

      - - -Here we can see that there is a clear divide between results significant in DESeq2 and FindMarkers based upon the expression values at the single-cell level. +### DESeq2 normalization Now we can look the pseudo-bulked normalized expression values: @@ -303,8 +275,7 @@ ggplot(avg_exp, aes(x=avg_pb.1, y=avg_pb.2, color=sig)) +

      - -### DESeq2 +We can also look at the overall trends as a heatmap ```r # DESEq2 @@ -323,11 +294,50 @@ pheatmap(norm_sig, show_rownames=FALSE, annotation_row=anno_row, annotation_col=

      -### FindMarkers + + +### FindMarkers normalization > Instead, the most pronounced differences between pseudobulk and single-cell methods emerged among highly expressed genes. +To address this question, we start by looking at the single-cell expression values for each + +```r +# Difference +avg_exp <- dge %>% select(gene, sig) + +# pct.1 = cold7 +seurat_1 <- subset(seurat_vsm, subset = (condition == "cold7")) +avg_cold7 <- AverageExpression(seurat_1, features=avg_exp$gene, assay="RNA") %>% + data.frame() %>% + rename("RNA"="avg_sc.1") %>% + rownames_to_column("gene") + +# pct.2 = TN +seurat_2 <- subset(seurat_vsm, subset = (condition == "TN")) +avg_TN <- AverageExpression(seurat_2, features=avg_exp$gene, assay="RNA") %>% + data.frame() %>% + rename("RNA"="avg_sc.2") %>% + rownames_to_column("gene") + +# Merge +avg_exp <- merge(avg_exp, merge(avg_cold7, avg_TN), by="gene") + +ggplot(avg_exp, aes(x=avg_sc.1, y=avg_sc.2, color=sig)) + + geom_point() + + scale_x_log10() + scale_y_log10() + + theme_bw() + geom_abline(slope=1) + + labs(x="cold7: Average single-cell expression", + y="TN: Average single-cell expression") +``` + +

      + +

      + + +Here we can see that there is a clear divide between results significant in DESeq2 and FindMarkers based upon the expression values at the single-cell level. ### Conservative approach

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