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Alemu's suggestions on model.zero.prob and sample size example
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@sthawinke
sthawinke committed Apr 2, 2020
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4 changes: 2 additions & 2 deletions R/SPsimSeq.R
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Expand Up @@ -101,7 +101,7 @@
#' set.seed(6452)
#' sim.data.bulk <- SPsimSeq(n.sim = 1, s.data = zhang.counts,
#' group = MYCN.status, n.genes = 2000, batch.config = 1,
#' group.config = c(0.5, 0.5), tot.samples = 20,
#' group.config = c(0.5, 0.5), tot.samples = ncol(zhang.counts),
#' pDE = 0.1, lfc.thrld = 0.5, result.format = "list")
#'
#' head(sim.data.bulk$counts[[1]][, seq_len(5)]) # count data
Expand Down Expand Up @@ -150,7 +150,7 @@ SPsimSeq <- function(n.sim = 1, s.data, batch = rep(1, ncol(s.data)),
n.genes = 1000, batch.config = 1, group.config = 1,
pDE = 0.1, cand.DE.genes = NULL, lfc.thrld = 0.5,
t.thrld = 2.5, llStat.thrld = 5, tot.samples = ncol(s.data),
model.zero.prob = TRUE, genewiseCor = TRUE,
model.zero.prob = FALSE, genewiseCor = TRUE,
log.CPM.transform = TRUE, lib.size.params = NULL,
variable.lib.size = FALSE, w = NULL,
result.format = "SCE", return.details = FALSE,
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98 changes: 48 additions & 50 deletions README.md
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Expand Up @@ -22,7 +22,7 @@ BioConductor installation

``` r
library(BiocManager)
BiocManager::install("SPsimSeq", update = FALSE)
BiocManager::install("SPsimSeq")
```

``` r
Expand Down Expand Up @@ -70,9 +70,7 @@ sim.data.bulk <- SPsimSeq(n.sim = 1, s.data = zhang.counts2,

## Selecting candidate DE genes ...

## Note: The number of DE genes detected in the source data is 79 and the number of DE genes required to be included in the simulated data is 200. Therefore, candidiate DE genes are sampled with replacement.

## Fitting zero probability model ...
## Note: The number of DE genes detected in the source data is 66 and the number of DE genes required to be included in the simulated data is 200. Therefore, candidiate DE genes are sampled with replacement.

## Estimating densities ...

Expand All @@ -88,36 +86,36 @@ head(sim.data.bulk1$counts[, seq_len(5)]) # count data
```

## Sample_1 Sample_2 Sample_3 Sample_4 Sample_5
## Gene_1 0 0 0 0 7
## Gene_2 1 1 1 8 0
## Gene_3 2 38 0 10 28
## Gene_4 3 25 2 132 232
## Gene_5 0 0 8 7 12
## Gene_6 1 1 0 58 1
## Gene_1 124 1 57 134 108
## Gene_2 1 3 44 0 91
## Gene_3 0 3 75 1 126
## Gene_4 11 10 24 10 6
## Gene_5 0 1 0 0 2
## Gene_6 187 31 332 27 73

``` r
head(sim.data.bulk1$colData) # sample info
```

## Batch Group sim.Lib.Size
## Sample_1 1 1 1484907
## Sample_2 1 1 1252074
## Sample_3 1 1 1861492
## Sample_4 1 1 1644716
## Sample_5 1 1 1769902
## Sample_6 1 1 1260043
## Sample_1 1 1 1499454
## Sample_2 1 1 1403642
## Sample_3 1 1 1395046
## Sample_4 1 1 1047452
## Sample_5 1 1 1428862
## Sample_6 1 1 1365620

``` r
head(sim.data.bulk1$rowData) # gene info
```

## DE.ind source.ID
## Gene_1 TRUE HIST1H2BM
## Gene_2 TRUE RP11-378A13.1
## Gene_3 TRUE HTR2C
## Gene_4 TRUE ZIC5
## Gene_5 TRUE WT1
## Gene_6 TRUE C2orf81
## DE.ind source.ID
## Gene_1 TRUE RP11-1006G14.4
## Gene_2 TRUE RP11-706O15.7
## Gene_3 TRUE RP11-706O15.7
## Gene_4 TRUE USP2-AS1
## Gene_5 TRUE XAGE5
## Gene_6 TRUE CAPS2

Example 2: simulating single cell RNA-seq from a single batch (read-counts)
---------------------------------------------------------------------------
Expand Down Expand Up @@ -154,7 +152,7 @@ sim.data.sc <- SPsimSeq(n.sim = 1, s.data = scNGP.data2,

## Selecting candidate DE genes ...

## Note: The number of DE genes detected in the source data is 51 and the number of DE genes required to be included in the simulated data is 200. Therefore, candidiate DE genes are sampled with replacement.
## Note: The number of DE genes detected in the source data is 42 and the number of DE genes required to be included in the simulated data is 200. Therefore, candidiate DE genes are sampled with replacement.

## Fitting zero probability model ...

Expand All @@ -180,12 +178,12 @@ head(counts(sim.data.sc1)[, seq_len(5)])
```

## Sample_1 Sample_2 Sample_3 Sample_4 Sample_5
## Gene_1 0 0 48 0 0
## Gene_2 81 7 0 0 0
## Gene_3 0 16 0 8 159
## Gene_4 0 0 0 0 8
## Gene_5 22 0 2 0 0
## Gene_6 0 0 0 0 29
## Gene_1 21 7 0 2 1
## Gene_2 19 0 1 0 0
## Gene_3 24 0 2 50 0
## Gene_4 0 60 0 12 35
## Gene_5 0 7 0 8 16
## Gene_6 5 16 11 8 41

``` r
colData(sim.data.sc1)
Expand All @@ -194,17 +192,17 @@ colData(sim.data.sc1)
## DataFrame with 100 rows and 3 columns
## Batch Group sim.Lib.Size
## <factor> <factor> <numeric>
## Sample_1 1 1 62456
## Sample_2 1 1 64650
## Sample_3 1 1 101039
## Sample_4 1 1 64104
## Sample_5 1 1 60436
## Sample_1 1 1 69309
## Sample_2 1 1 37024
## Sample_3 1 1 81538
## Sample_4 1 1 134070
## Sample_5 1 1 67218
## ... ... ... ...
## Sample_96 1 2 96673
## Sample_97 1 2 46853
## Sample_98 1 2 58025
## Sample_99 1 2 88526
## Sample_100 1 2 77400
## Sample_96 1 2 122978
## Sample_97 1 2 86100
## Sample_98 1 2 107930
## Sample_99 1 2 59519
## Sample_100 1 2 67167

``` r
rowData(sim.data.sc1)
Expand All @@ -213,14 +211,14 @@ rowData(sim.data.sc1)
## DataFrame with 2000 rows and 2 columns
## DE.ind source.ID
## <logical> <factor>
## Gene_1 TRUE ENSG00000093072
## Gene_2 TRUE ENSG00000169446
## Gene_3 TRUE ENSG00000125703
## Gene_4 TRUE ENSG00000081320
## Gene_5 TRUE ENSG00000106346
## Gene_1 TRUE ENSG00000174482
## Gene_2 TRUE ENSG00000087586
## Gene_3 TRUE ENSG00000164306
## Gene_4 TRUE ENSG00000145386
## Gene_5 TRUE ENSG00000105613
## ... ... ...
## Gene_1996 FALSE ENSG00000153767
## Gene_1997 FALSE ENSG00000006432
## Gene_1998 FALSE ENSG00000168291
## Gene_1999 FALSE ENSG00000108774
## Gene_2000 FALSE ENSG00000069424
## Gene_1996 FALSE ENSG00000274267
## Gene_1997 FALSE ENSG00000143126
## Gene_1998 FALSE ENSG00000175305
## Gene_1999 FALSE ENSG00000109099
## Gene_2000 FALSE ENSG00000237017
4 changes: 2 additions & 2 deletions man/SPsimSeq.Rd
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