- Load Required Libraries
- 📌 Boxplot of Raw Counts (Unnormalized)
- 📌 Histogram of Unfiltered Log Counts
- 📌 Histogram of filtered Log Counts (rowMeans > 0)
- 📌 Histogram of filtered Log Counts (rowMeans > 0.5)
- 📌 Histogram of filtered Log Counts (rowMeans > 0.5)
- 📌 Density Plots for Log2 CPM Values
- 📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 0)
- 📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 0.5)
- 📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 1)
Count Matrix Analysis
Sayan Paul
2025-01-31
Last updated: 2025-01-31
Checks: 7 0
Knit directory: CX5461_Project/
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Load Required Libraries
📍 Load Count Matrix
# Load the count matrix from the TSV file
counts_matrix <- read.csv("data/counts_matrix.csv", header=TRUE, check.names=FALSE)
# Remove "X" prefix from column names (if present)
colnames(counts_matrix) <- gsub("^X", "", colnames(counts_matrix))
# Display first few rows
head(counts_matrix) 17.3_CX.5461_0.1_3 17.3_DOX_0.5_24 87.1_DOX_0.5_24 87.1_VEH_0.1_24
1 8 1 5 6
2 238 227 358 276
3 4 11 12 7
4 0 0 1 0
5 0 0 1 0
6 0 0 0 0
87.1_VEH_0.5_24 87.1_CX.5461_0.1_48 87.1_CX.5461_0.5_48 87.1_DOX_0.1_48
1 3 3 9 7
2 298 349 254 335
3 8 8 9 10
4 0 1 0 0
5 0 0 0 0
6 0 0 0 1
87.1_DOX_0.5_48 87.1_VEH_0.1_48 87.1_VEH_0.5_48 17.3_VEH_0.1_24
1 7 2 5 5
2 427 234 267 225
3 12 3 10 2
4 1 0 0 0
5 1 0 0 0
6 1 0 0 0
17.3_VEH_0.5_24 17.3_CX.5461_0.1_48 17.3_CX.5461_0.5_48 17.3_DOX_0.1_48
1 3 5 3 10
2 187 214 168 301
3 4 7 3 11
4 0 0 0 2
5 0 0 0 0
6 0 0 0 0
17.3_DOX_0.5_48 17.3_VEH_0.1_48 17.3_VEH_0.5_48 84.1_CX.5461_0.1_3
1 14 5 4 0
2 370 304 151 220
3 6 5 5 4
4 1 0 0 0
5 1 0 0 0
6 0 0 0 0
17.3_CX.5461_0.5_3 84.1_CX.5461_0.5_3 84.1_DOX_0.1_3 84.1_DOX_0.5_3
1 4 1 0 0
2 178 217 235 228
3 2 4 6 9
4 0 1 0 0
5 1 0 0 0
6 0 0 0 0
84.1_VEH_0.1_3 84.1_VEH_0.5_3 84.1_CX.5461_0.1_24 84.1_CX.5461_0.5_24
1 0 0 0 0
2 235 178 330 244
3 8 3 3 3
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
84.1_DOX_0.1_24 84.1_DOX_0.5_24 84.1_VEH_0.1_24 17.3_DOX_0.1_3
1 0 0 1 6
2 310 320 211 213
3 13 5 2 7
4 0 1 0 2
5 0 0 0 0
6 0 0 0 0
84.1_VEH_0.5_24 84.1_CX.5461_0.1_48 84.1_CX.5461_0.5_48 84.1_DOX_0.1_48
1 0 0 0 0
2 241 153 279 266
3 11 3 7 9
4 0 0 0 0
5 1 0 0 0
6 0 0 0 0
84.1_DOX_0.5_48 84.1_VEH_0.1_48 84.1_VEH_0.5_48 90.1_CX.5461_0.1_3
1 2 0 0 6
2 272 174 194 321
3 5 6 14 11
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
90.1_CX.5461_0.5_3 90.1_DOX_0.1_3 17.3_DOX_0.5_3 90.1_DOX_0.5_3
1 0 1 3 7
2 260 320 207 340
3 4 11 13 9
4 0 0 0 1
5 0 0 0 0
6 0 0 0 0
90.1_VEH_0.1_3 90.1_VEH_0.5_3 90.1_CX.5461_0.1_24 90.1_CX.5461_0.5_24
1 2 3 3 7
2 320 279 258 311
3 11 7 10 11
4 0 1 0 0
5 0 0 1 0
6 0 0 0 0
90.1_DOX_0.1_24 90.1_DOX_0.5_24 90.1_VEH_0.1_24 90.1_VEH_0.5_24
1 3 8 4 1
2 331 453 398 395
3 9 18 15 15
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
90.1_CX.5461_0.1_48 17.3_VEH_0.1_3 90.1_CX.5461_0.5_48 90.1_DOX_0.1_48
1 4 7 5 6
2 415 324 283 283
3 17 9 11 8
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
90.1_DOX_0.5_48 90.1_VEH_0.1_48 90.1_VEH_0.5_48 75.1_CX.5461_0.1_3
1 20 2 10 1
2 453 301 265 309
3 9 7 5 10
4 0 0 0 0
5 1 1 0 0
6 1 0 0 0
75.1_CX.5461_0.5_3 75.1_DOX_0.1_3 75.1_DOX_0.5_3 75.1_VEH_0.1_3
1 1 3 3 2
2 306 317 216 303
3 11 7 7 10
4 0 0 0 1
5 0 0 0 0
6 0 0 0 0
17.3_VEH_0.5_3 75.1_VEH_0.5_3 75.1_CX.5461_0.1_24 75.1_CX.5461_0.5_24
1 3 2 2 1
2 248 269 368 263
3 8 10 17 6
4 0 0 1 0
5 1 0 0 0
6 0 0 0 0
75.1_DOX_0.1_24 75.1_DOX_0.5_24 75.1_VEH_0.1_24 75.1_VEH_0.5_24
1 2 2 0 1
2 540 284 405 429
3 28 4 16 9
4 0 11 2 0
5 0 3 0 0
6 0 0 0 0
75.1_CX.5461_0.1_48 75.1_CX.5461_0.5_48 75.1_DOX_0.1_48 17.3_CX.5461_0.1_24
1 3 1 6 6
2 356 357 344 236
3 11 21 13 7
4 0 0 0 0
5 0 0 1 0
6 0 0 0 0
75.1_DOX_0.5_48 75.1_VEH_0.1_48 75.1_VEH_0.5_48 78.1_CX.5461_0.1_3
1 3 3 0 0
2 317 304 406 222
3 10 21 11 3
4 10 0 1 0
5 1 0 0 0
6 1 0 0 0
78.1_CX.5461_0.5_3 78.1_DOX_0.1_3 78.1_DOX_0.5_3 78.1_VEH_0.1_3
1 0 1 0 0
2 196 221 205 231
3 5 3 5 8
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
78.1_VEH_0.5_3 78.1_CX.5461_0.1_24 17.3_CX.5461_0.5_24 78.1_CX.5461_0.5_24
1 0 0 2 0
2 171 191 238 153
3 2 2 6 1
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
78.1_DOX_0.1_24 78.1_DOX_0.5_24 78.1_VEH_0.1_24 78.1_VEH_0.5_24
1 0 1 1 0
2 410 55 183 184
3 11 0 1 8
4 0 2 0 0
5 0 0 0 0
6 0 0 0 0
78.1_CX.5461_0.1_48 78.1_CX.5461_0.5_48 78.1_DOX_0.1_48 78.1_DOX_0.5_48
1 0 1 1 0
2 234 187 245 205
3 4 3 5 1
4 0 0 0 16
5 0 0 0 0
6 0 0 0 1
78.1_VEH_0.1_48 17.3_DOX_0.1_24 78.1_VEH_0.5_48 87.1_CX.5461_0.1_3
1 0 10 1 2
2 226 364 212 251
3 7 9 4 10
4 0 0 0 0
5 0 1 0 0
6 1 0 0 0
87.1_CX.5461_0.5_3 87.1_DOX_0.1_3 87.1_DOX_0.5_3 87.1_VEH_0.1_3
1 4 3 9 6
2 307 289 344 242
3 11 7 6 7
4 1 0 0 0
5 0 0 0 0
6 0 0 0 0
87.1_VEH_0.5_3 87.1_CX.5461_0.1_24 87.1_CX.5461_0.5_24 87.1_DOX_0.1_24
1 4 7 4 4
2 300 423 314 283
3 14 15 4 5
4 1 0 0 0
5 0 0 0 0
6 0 0 0 0📌 Boxplot of Raw Counts (Unnormalized)
# Adjust graphical parameters for better spacing
par(mar = c(12, 5, 5, 2)) # Bottom margin increased to 12
# Boxplot of unnormalized counts
boxplot(counts_matrix,
main = "Boxplots of samples (Unnormalized)",
names = colnames(counts_matrix),
las = 2, # Make labels vertical
cex.axis = 0.6) # Reduce text size for better fit
| Version | Author | Date |
|---|---|---|
| 8a48243 | sayanpaul01 | 2025-01-31 |
📍 Log-Transformed Counts (CPM)
# Compute CPM values
cpm <- cpm(counts_matrix)
lcpm <- cpm(counts_matrix, log=TRUE)
# Dimensions
dim(lcpm)[1] 28395 108📌 Histogram of Unfiltered Log Counts
hist(lcpm,
main = "Histogram of total counts (unfiltered)",
xlab = expression("Log"[2]*" counts-per-million"),
col = 4)
📍 Filtering Count Matrix (rowMeans > 0)
filcpm_matrix <- subset(lcpm, (rowMeans(lcpm) > 0))
dim(filcpm_matrix)[1] 14279 108📌 Histogram of filtered Log Counts (rowMeans > 0)
hist(filcpm_matrix,
main = "Histogram of filtered counts using rowMeans > 0 method",
xlab = expression("Log"[2]*" counts-per-million"),
col = 2)
| Version | Author | Date |
|---|---|---|
| 4ed05f7 | sayanpaul01 | 2025-01-31 |
📍 Filtering Count Matrix (rowMeans > 0.5)
filcpm_matrix1 <- subset(lcpm, (rowMeans(lcpm) > 0.5))
dim(filcpm_matrix1)[1] 13678 108📌 Histogram of filtered Log Counts (rowMeans > 0.5)
hist(filcpm_matrix1,
main = "Histogram of filtered counts using rowMeans > 0.5 method",
xlab = expression("Log"[2]*" counts-per-million"),
col = 5)
| Version | Author | Date |
|---|---|---|
| 4ed05f7 | sayanpaul01 | 2025-01-31 |
📍 Filtering Count Matrix (rowMeans > 1)
filcpm_matrix2 <- subset(lcpm, (rowMeans(lcpm) > 1))
dim(filcpm_matrix2)[1] 13047 108📌 Histogram of filtered Log Counts (rowMeans > 0.5)
hist(filcpm_matrix2,
main = "Histogram of filtered counts using rowMeans > 1 method",
xlab = expression("Log"[2]*" counts-per-million"),
col = 6)
| Version | Author | Date |
|---|---|---|
| 4ed05f7 | sayanpaul01 | 2025-01-31 |
📌 Density Plots for Log2 CPM Values
## Convert log2 CPM matrix into long format for visualization
log2_cpm_long <- as.data.frame(as.table(lcpm))
colnames(log2_cpm_long) <- c("Feature", "Sample", "Log2_CPM")
## Density Plot of Log2 CPM Values (Unfiltered)
ggplot(log2_cpm_long, aes(x = Log2_CPM)) +
geom_density(fill = "blue", alpha = 0.5) +
labs(title = "Density Plot of Log2 CPM Values (Unfiltered)", x = "Log2 CPM", y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
## Histogram + Density Plot for Log2 CPM Values (Unfiltered)
ggplot(log2_cpm_long, aes(x = Log2_CPM)) +
geom_histogram(aes(y = ..density..),
bins = 40,
fill = "lightblue",
color = "black") +
geom_density(alpha = 0.5, fill = "lightgreen") +
labs(title = "Log2 CPM Values (Unfiltered) (Density+Histogram)",
x = "Log2 CPM",
y = "Density") +
theme_minimal()Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 0)
log2_cpm_long1 <- as.data.frame(as.table(filcpm_matrix))
colnames(log2_cpm_long1) <- c("Feature", "Sample", "Log2_CPM")
## Density Plot for Log2 CPM (rowMeans > 0)
ggplot(log2_cpm_long1, aes(x = Log2_CPM)) +
geom_density(fill = "blue", alpha = 0.5) +
labs(title = "Density Plot of Log2 CPM Values (rowMeans > 0)", x = "Log2 CPM", y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
## Histogram + Density Plot for Log2 CPM (rowMeans > 0)
ggplot(log2_cpm_long1, aes(x = Log2_CPM)) +
geom_histogram(aes(y = ..density..),
bins = 40,
fill = "lightblue",
color = "black") +
geom_density(alpha = 0.5, fill = "lightgreen") +
labs(title = "Log2 CPM Values (rowMeans > 0) (Density+Histogram)",
x = "Log2 CPM",
y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 0.5)
log2_cpm_long2 <- as.data.frame(as.table(filcpm_matrix1))
colnames(log2_cpm_long2) <- c("Feature", "Sample", "Log2_CPM")
## Density Plot for Log2 CPM (rowMeans > 0.5)
ggplot(log2_cpm_long2, aes(x = Log2_CPM)) +
geom_density(fill = "blue", alpha = 0.5) +
labs(title = "Density Plot of Log2 CPM Values (rowMeans > 0.5)", x = "Log2 CPM", y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
## Histogram + Density Plot for Log2 CPM (rowMeans > 0.5)
ggplot(log2_cpm_long2, aes(x = Log2_CPM)) +
geom_histogram(aes(y = ..density..),
bins = 40,
fill = "lightblue",
color = "black") +
geom_density(alpha = 0.5, fill = "lightgreen") +
labs(title = "Log2 CPM Values (rowMeans > 0.5) (Density+Histogram)",
x = "Log2 CPM",
y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
📌 Density Plots for Filtered Log2 CPM Values (rowMeans > 1)
log2_cpm_long3 <- as.data.frame(as.table(filcpm_matrix2))
colnames(log2_cpm_long3) <- c("Feature", "Sample", "Log2_CPM")
## Density Plot for Log2 CPM (rowMeans > 1)
ggplot(log2_cpm_long3, aes(x = Log2_CPM)) +
geom_density(fill = "blue", alpha = 0.5) +
labs(title = "Density Plot of Log2 CPM Values (rowMeans > 1)", x = "Log2 CPM", y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
## Histogram + Density Plot for Log2 CPM (rowMeans > 1)
ggplot(log2_cpm_long3, aes(x = Log2_CPM)) +
geom_histogram(aes(y = ..density..),
bins = 40,
fill = "lightblue",
color = "black") +
geom_density(alpha = 0.5, fill = "lightgreen") +
labs(title = "Log2 CPM Values (rowMeans > 1) (Density+Histogram)",
x = "Log2 CPM",
y = "Density") +
theme_minimal()
| Version | Author | Date |
|---|---|---|
| 0c3b718 | sayanpaul01 | 2025-01-31 |
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Hmisc_5.2-0
[2] corrplot_0.95
[3] ggrepel_0.9.6
[4] cowplot_1.1.3
[5] Homo.sapiens_1.3.1
[6] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[7] org.Hs.eg.db_3.18.0
[8] GO.db_3.18.0
[9] OrganismDbi_1.44.0
[10] GenomicFeatures_1.54.4
[11] GenomicRanges_1.54.1
[12] GenomeInfoDb_1.38.8
[13] AnnotationDbi_1.64.1
[14] IRanges_2.36.0
[15] S4Vectors_0.40.1
[16] biomaRt_2.58.2
[17] scales_1.3.0
[18] lubridate_1.9.3
[19] forcats_1.0.0
[20] stringr_1.5.1
[21] purrr_1.0.2
[22] readr_2.1.5
[23] tidyr_1.3.1
[24] tibble_3.2.1
[25] tidyverse_2.0.0
[26] RColorBrewer_1.1-3
[27] Biobase_2.62.0
[28] BiocGenerics_0.48.1
[29] dplyr_1.1.4
[30] reshape2_1.4.4
[31] ggplot2_3.5.1
[32] edgeR_4.0.1
[33] limma_3.58.1
[34] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rstudioapi_0.17.1 jsonlite_1.8.9
[3] magrittr_2.0.3 farver_2.1.2
[5] rmarkdown_2.29 fs_1.6.3
[7] BiocIO_1.12.0 zlibbioc_1.48.0
[9] vctrs_0.6.5 memoise_2.0.1
[11] Rsamtools_2.18.0 RCurl_1.98-1.13
[13] base64enc_0.1-3 htmltools_0.5.8.1
[15] S4Arrays_1.2.1 progress_1.2.3
[17] curl_6.0.1 Formula_1.2-5
[19] SparseArray_1.2.4 sass_0.4.9
[21] bslib_0.8.0 htmlwidgets_1.6.4
[23] plyr_1.8.9 cachem_1.0.8
[25] GenomicAlignments_1.38.2 whisker_0.4.1
[27] lifecycle_1.0.4 pkgconfig_2.0.3
[29] Matrix_1.6-1.1 R6_2.5.1
[31] fastmap_1.1.1 GenomeInfoDbData_1.2.11
[33] MatrixGenerics_1.14.0 digest_0.6.34
[35] colorspace_2.1-0 ps_1.8.1
[37] rprojroot_2.0.4 RSQLite_2.3.3
[39] labeling_0.4.3 filelock_1.0.3
[41] timechange_0.3.0 httr_1.4.7
[43] abind_1.4-8 compiler_4.3.0
[45] bit64_4.0.5 withr_3.0.2
[47] backports_1.5.0 htmlTable_2.4.3
[49] BiocParallel_1.36.0 DBI_1.2.3
[51] rappdirs_0.3.3 DelayedArray_0.28.0
[53] rjson_0.2.23 tools_4.3.0
[55] foreign_0.8-87 httpuv_1.6.15
[57] nnet_7.3-19 glue_1.7.0
[59] restfulr_0.0.15 callr_3.7.6
[61] promises_1.3.0 grid_4.3.0
[63] checkmate_2.3.2 getPass_0.2-4
[65] cluster_2.1.6 generics_0.1.3
[67] gtable_0.3.6 tzdb_0.4.0
[69] data.table_1.14.10 hms_1.1.3
[71] xml2_1.3.6 XVector_0.42.0
[73] pillar_1.10.1 later_1.3.2
[75] BiocFileCache_2.10.2 lattice_0.22-5
[77] rtracklayer_1.62.0 bit_4.0.5
[79] tidyselect_1.2.1 RBGL_1.78.0
[81] locfit_1.5-9.8 Biostrings_2.70.1
[83] knitr_1.49 git2r_0.35.0
[85] gridExtra_2.3 SummarizedExperiment_1.32.0
[87] xfun_0.50 statmod_1.5.0
[89] matrixStats_1.4.1 stringi_1.8.3
[91] yaml_2.3.10 evaluate_1.0.3
[93] codetools_0.2-20 BiocManager_1.30.25
[95] graph_1.80.0 cli_3.6.1
[97] rpart_4.1.23 munsell_0.5.1
[99] processx_3.8.5 jquerylib_0.1.4
[101] Rcpp_1.0.12 dbplyr_2.5.0
[103] png_0.1-8 XML_3.99-0.17
[105] parallel_4.3.0 blob_1.2.4
[107] prettyunits_1.2.0 bitops_1.0-7
[109] crayon_1.5.3 rlang_1.1.3
[111] KEGGREST_1.42.0