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finish logFC method, update the tutorial
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@ptdtan
ptdtan authored and root committed Jul 12, 2019
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70 changes: 40 additions & 30 deletions docs/Tutorial_1__Use_Venice_to_find_marker_genes.md
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Expand Up @@ -31,37 +31,37 @@ First let take a look at the structure of the pre-processed object
## 162 155 32 14

Now we find marker genes for all annotated clusters using
`VeniceAllMarkers` function in Signac
`VeniceAllMarkers` function in Signac.

system.time(pbmc.markers <- VeniceAllMarkers(pbmc, only.pos = TRUE, logfc.threshold = 0.25, verbose = F))

## user system elapsed
## 4.008 0.281 4.289
## 5.258 0.279 5.540

pbmc.markers %>% group_by(cluster) %>% top_n(n = 2, wt = Log2.fold.change)

## # A tibble: 18 x 8
## # Groups: cluster [9]
## Gene.Name Log2.fold.change Log10.adjusted.… Log10.p.value cluster
## <fct> <dbl> <dbl> <dbl> <fct>
## 1 CCR7 1.33 -26.9 -28.9 Naive …
## 2 LDLRAP1 1.10 -7.78 -9.38 Naive …
## 3 LTB 1.29 -77.4 -81.0 Memory…
## 4 AQP3 1.24 -14.8 -16.9 Memory…
## 5 S100A9 5.57 -288. -292. CD14+ …
## 6 S100A8 5.48 -281. -285. CD14+ …
## 7 CD79A 4.31 -210. -213. B
## 8 TCL1A 3.59 -97.9 -101. B
## 9 CCL5 3.06 -140. -144. CD8 T
## 10 GZMK 2.95 -58.5 -61.8 CD8 T
## 11 LST1 3.09 -94.0 -98.2 FCGR3A…
## 12 FCGR3A 3.31 -82.8 -86.2 FCGR3A…
## 13 GZMB 4.83 -103. -107. NK
## 14 GNLY 5.32 -103. -107. NK
## 15 HLA-DPB1 2.87 -11.7 -15.3 DC
## 16 FCER1A 3.87 -11.6 -15.2 DC
## 17 PPBP 8.58 -9.11 -13.2 Mk
## 18 PF4 7.24 -9.11 -13.2 Mk
## 1 CD3D 0.631 -55.6 -58.0 Naive …
## 2 CCR7 0.639 -26.9 -28.9 Naive …
## 3 IL32 0.738 -83.7 -87.9 Memory…
## 4 IL7R 0.666 -35.0 -37.6 Memory…
## 5 S100A9 1.95 -288. -292. CD14+ …
## 6 S100A8 2.00 -281. -285. CD14+ …
## 7 CD79A 1.83 -210. -213. B
## 8 CD79B 1.54 -178. -181. B
## 9 CCL5 1.57 -140. -144. CD8 T
## 10 NKG7 1.50 -137. -141. CD8 T
## 11 LST1 1.43 -94.0 -98.2 FCGR3A…
## 12 FCGR3A 1.59 -82.8 -86.2 FCGR3A…
## 13 GZMB 2.00 -103. -107. NK
## 14 GNLY 2.06 -103. -107. NK
## 15 HLA-DQA1 1.40 -12.4 -16.5 DC
## 16 FCER1A 1.60 -11.6 -15.2 DC
## 17 PPBP 2.72 -9.11 -13.2 Mk
## 18 PF4 2.54 -9.11 -13.2 Mk
## # … with 3 more variables: Dissimilarity <dbl>, Bin.count <dbl>,
## # Up.Down.score <dbl>

Expand All @@ -70,7 +70,7 @@ Compare with `Seurat::FindAllMarkers` results
system.time(pbmc.markers.seurat <- FindAllMarkers(pbmc, only.pos = TRUE, logfc.threshold = 0.25, verbose = F, min.cells.feature = 0))

## user system elapsed
## 122.215 0.918 123.130
## 114.057 1.416 115.483

pbmc.markers.seurat %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC)

Expand Down Expand Up @@ -105,34 +105,44 @@ type (cluster). We only plot top 20 features (all features if less than

### Heatmap of marker genes found by Venice

top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 10, wt = Log2.fold.change)
top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 10, wt = -Log10.adjusted.p.value)
DoHeatmap(pbmc, features = as.character(top10$Gene.Name)) + NoLegend()

![](Tutorial_1__Use_Venice_to_find_marker_genes_files/figure-markdown_strict/unnamed-chunk-6-1.png)

### Heatmap of marker genes found by Seurat default method

top10 <- pbmc.markers.seurat %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
top10 <- pbmc.markers.seurat %>% group_by(cluster) %>% top_n(n = 10, wt = -p_val_adj)
DoHeatmap(pbmc, features = as.character(top10$gene)) + NoLegend()

![](Tutorial_1__Use_Venice_to_find_marker_genes_files/figure-markdown_strict/unnamed-chunk-7-1.png)

To perform the Differential expression (DE) test on two individual
clusters, one can use the function `VeniceFindMarkers`. We designed the
user interface that is similar to Seurat R package (Butler et al.,
Nature Biotechnology 2018). Please specify two cluster names `ident.1`,
`ident.2` as the parameters.

### Find DE genes between CD14+ Mono and FCGR3A+ Mono using Venice algorithm

head(Signac::VeniceFindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", logfc.threshold = log(2)))

## Gene.Name Log2.fold.change Log10.adjusted.p.value Log10.p.value
## 1 LYZ 2.614275 -74.02894 -78.16610
## 2 FCGR3A -3.776553 -71.17833 -75.01446
## 3 S100A8 3.766437 -62.41254 -66.07258
## 4 S100A9 3.299060 -61.23949 -64.77459
## 5 IFITM2 -2.085807 -56.30591 -59.74410
## 6 LGALS2 2.956704 -49.87441 -53.23342
## 2 FCGR3A -1.6631222 -71.17833 -75.01446
## 3 S100A8 1.2752262 -62.41254 -66.07258
## 4 S100A9 0.7930380 -61.23949 -64.77459
## 5 IFITM2 -0.8010430 -56.30591 -59.74410
## 6 LGALS2 1.2904962 -49.87441 -53.23342
## 7 GPX1 0.7329328 -39.49077 -42.78284
## Dissimilarity Bin.count Up.Down.score
## 1 0.7480570 4 1
## 2 0.7303167 3 -1
## 3 0.6406856 5 1
## 4 0.6282687 5 1
## 5 0.5724071 4 -1
## 6 0.5219134 3 1
## 7 0.4105139 4 1

### Find DE genes between CD14+ Mono and FCGR3A+ Mono using Seurat default algorithm

head(FindMarkers(pbmc, ident.1 = "CD14+ Mono", ident.2 = "FCGR3A+ Mono", logfc.threshold = log(2)))

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2 changes: 1 addition & 1 deletion src/Venice.cpp
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Expand Up @@ -366,7 +366,7 @@ std::vector<struct GeneResult> VeniceTest(
int r = c_it.row();

exp[r].push_back({*c_it, cid});
total_exp[r][cid] += expm1(*c_it);
total_exp[r][cid] += *c_it;
--zero_cnt[r][cid];
}
}
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