Arabidopsis RSA
2024-08-02
library(readr)
RSA_Annie_2 <- read_csv("RSA_Annie_2.csv")## Rows: 2713 Columns: 22
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): image, root_name, root, root_ontology, parent_name, parent, first_...
## dbl (12): day, length, vector_length, surface, volume, direction, diameter, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.RSA_Annie_2## # A tibble: 2,713 × 22
## image day root_name root length vector_length surface volume direction
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2024062… 1 Pl1_M_02… f7a3… 0.517 0.503 0.0238 9.13e-5 272.
## 2 2024062… 1 Pl1_M_02… 7c16… 0.683 0.680 0.0629 4.76e-4 259.
## 3 2024062… 1 Pl1_M_01… 6092… 0.874 0.775 0.0502 2.39e-4 267.
## 4 2024062… 1 Pl1_M_07… c01c… 0.590 0.577 0.0352 1.82e-4 271.
## 5 2024062… 1 Pl1_M_02… 97c8… 0.737 0.716 0.0491 2.73e-4 268.
## 6 2024062… 1 Pl1_M_02… 7b9f… 0.613 0.593 0.0363 1.78e-4 281.
## 7 2024062… 1 Pl1_M_01… 7b8e… 0.656 0.636 0.0352 1.64e-4 262.
## 8 2024062… 1 Pl1_M_01… a58b… 0.522 0.463 0.0354 1.99e-4 259.
## 9 2024062… 1 Pl1_M_01… a626… 0.864 0.819 0.0492 2.34e-4 272.
## 10 2024062… 1 Pl1_M_08… 31e4… 0.876 0.855 0.0728 4.89e-4 258.
## # ℹ 2,703 more rows
## # ℹ 13 more variables: diameter <dbl>, root_order <dbl>, root_ontology <chr>,
## # parent_name <chr>, parent <chr>, insertion_position <dbl>,
## # insertion_angle <dbl>, n_child <dbl>, child_density <dbl>,
## # first_child <chr>, insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>saving raw file
RSA_Annie_2_raw <- RSA_Annie_2unique(RSA_Annie_2$day)## [1] 1 5 9Main_root <- subset(RSA_Annie_2, RSA_Annie_2$root_order == 0)
Main_root## # A tibble: 745 × 22
## image day root_name root length vector_length surface volume direction
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2024062… 1 Pl1_M_02… f7a3… 0.517 0.503 0.0238 9.13e-5 272.
## 2 2024062… 1 Pl1_M_02… 7c16… 0.683 0.680 0.0629 4.76e-4 259.
## 3 2024062… 1 Pl1_M_01… 6092… 0.874 0.775 0.0502 2.39e-4 267.
## 4 2024062… 1 Pl1_M_07… c01c… 0.590 0.577 0.0352 1.82e-4 271.
## 5 2024062… 1 Pl1_M_02… 97c8… 0.737 0.716 0.0491 2.73e-4 268.
## 6 2024062… 1 Pl1_M_02… 7b9f… 0.613 0.593 0.0363 1.78e-4 281.
## 7 2024062… 1 Pl1_M_01… 7b8e… 0.656 0.636 0.0352 1.64e-4 262.
## 8 2024062… 1 Pl1_M_01… a58b… 0.522 0.463 0.0354 1.99e-4 259.
## 9 2024062… 1 Pl1_M_01… a626… 0.864 0.819 0.0492 2.34e-4 272.
## 10 2024062… 1 Pl1_M_08… 31e4… 0.876 0.855 0.0728 4.89e-4 258.
## # ℹ 735 more rows
## # ℹ 13 more variables: diameter <dbl>, root_order <dbl>, root_ontology <chr>,
## # parent_name <chr>, parent <chr>, insertion_position <dbl>,
## # insertion_angle <dbl>, n_child <dbl>, child_density <dbl>,
## # first_child <chr>, insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>colnames(Main_root)## [1] "image" "day" "root_name"
## [4] "root" "length" "vector_length"
## [7] "surface" "volume" "direction"
## [10] "diameter" "root_order" "root_ontology"
## [13] "parent_name" "parent" "insertion_position"
## [16] "insertion_angle" "n_child" "child_density"
## [19] "first_child" "insertion_first_child" "last_child"
## [22] "insertion_last_child"Main_root[,c(4:6,11)]## # A tibble: 745 × 4
## root length vector_length root_order
## <chr> <dbl> <dbl> <dbl>
## 1 f7a3097e-2170-4f57-958f-79b79c3526d1 0.517 0.503 0
## 2 7c164c79-7545-4861-a193-eeb82490c2eb 0.683 0.680 0
## 3 60929313-8622-4671-82fb-62ba0ea02d7d 0.874 0.775 0
## 4 c01cb305-5041-44d6-8d18-6885e154c944 0.590 0.577 0
## 5 97c81166-0747-485d-b663-e9cab198c902 0.737 0.716 0
## 6 7b9f4dab-e660-47af-b358-93383c819238 0.613 0.593 0
## 7 7b8e778d-05f1-4473-9507-b4125954cb62 0.656 0.636 0
## 8 a58b3c48-fc3b-40f1-b4cb-707d0e7e65cb 0.522 0.463 0
## 9 a6262902-120c-48bf-a259-ae2cea830363 0.864 0.819 0
## 10 31e4dc65-4b4b-407e-b61f-9697a91001b8 0.876 0.855 0
## # ℹ 735 more rowscolnames(Main_root)## [1] "image" "day" "root_name"
## [4] "root" "length" "vector_length"
## [7] "surface" "volume" "direction"
## [10] "diameter" "root_order" "root_ontology"
## [13] "parent_name" "parent" "insertion_position"
## [16] "insertion_angle" "n_child" "child_density"
## [19] "first_child" "insertion_first_child" "last_child"
## [22] "insertion_last_child"MR_data_2 <- Main_root[,c(1:5,17,18,20:22)]
MR_data_2## # A tibble: 745 × 10
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 20240624001.rsml 1 Pl1_M_02_03 f7a3097e-217… 0.517 0 0
## 2 20240624001.rsml 1 Pl1_M_02_02 7c164c79-754… 0.683 0 0
## 3 20240624001.rsml 1 Pl1_M_01_01 60929313-862… 0.874 0 0
## 4 20240624001.rsml 1 Pl1_M_07_01 c01cb305-504… 0.590 0 0
## 5 20240624001.rsml 1 Pl1_M_02_04 97c81166-074… 0.737 0 0
## 6 20240624001.rsml 1 Pl1_M_02_01 7b9f4dab-e66… 0.613 0 0
## 7 20240624001.rsml 1 Pl1_M_01_04 7b8e778d-05f… 0.656 0 0
## 8 20240624001.rsml 1 Pl1_M_01_03 a58b3c48-fc3… 0.522 0 0
## 9 20240624001.rsml 1 Pl1_M_01_02 a6262902-120… 0.864 0 0
## 10 20240624001.rsml 1 Pl1_M_08_04 31e4dc65-4b4… 0.876 0 0
## # ℹ 735 more rows
## # ℹ 3 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>Lateral_root <- subset(RSA_Annie_2, RSA_Annie_2$root_order == 1)
Lateral_root## # A tibble: 1,958 × 22
## image day root_name root length vector_length surface volume direction
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 62824_0… 5 root_1 3b46… 0.0514 0.0514 0.00137 2.90e-6 326.
## 2 62824_0… 5 root_0 92d4… 0.0319 0.0319 0.00128 4.19e-6 350.
## 3 70224_0… 9 root_26 5416… 0.0487 0.0487 0.00356 2.08e-5 206.
## 4 70224_0… 9 root_26 ba83… 0.0747 0.0747 0.00199 4.20e-6 254.
## 5 70224_0… 9 root_0 3144… 0.124 0.123 0.0102 6.70e-5 309.
## 6 70224_0… 9 root_26 7f5a… 0.180 0.179 0.0121 6.81e-5 317.
## 7 70224_0… 9 root_26 77cc… 0.0435 0.0435 0.00263 1.29e-5 204.
## 8 70224_0… 9 root_26 d631… 0.327 0.308 0.0237 1.40e-4 222.
## 9 70224_0… 9 root_27 981c… 0.361 0.354 0.0263 1.55e-4 315.
## 10 70224_0… 9 root_28 8766… 0.681 0.641 0.0416 2.05e-4 297.
## # ℹ 1,948 more rows
## # ℹ 13 more variables: diameter <dbl>, root_order <dbl>, root_ontology <chr>,
## # parent_name <chr>, parent <chr>, insertion_position <dbl>,
## # insertion_angle <dbl>, n_child <dbl>, child_density <dbl>,
## # first_child <chr>, insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>unique(Lateral_root$root_order)## [1] 1MR_data_2$root[1]## [1] "f7a3097e-2170-4f57-958f-79b79c3526d1"temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_2$root[1])
temporary## # A tibble: 1 × 22
## image day root_name root length vector_length surface volume direction
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 70224_00… 9 root_4 9a8d… 0.189 0.184 0.0138 8.31e-5 310.
## # ℹ 13 more variables: diameter <dbl>, root_order <dbl>, root_ontology <chr>,
## # parent_name <chr>, parent <chr>, insertion_position <dbl>,
## # insertion_angle <dbl>, n_child <dbl>, child_density <dbl>,
## # first_child <chr>, insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>MR_data_3 <- subset(MR_data_2, MR_data_2$n_child >0)
temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_3$root[1])
temporary <- subset(temporary, temporary$day == MR_data_3$day[1])
temporary## # A tibble: 1 × 22
## image day root_name root length vector_length surface volume direction
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 62824_01… 5 root_1 3b46… 0.0514 0.0514 0.00137 2.90e-6 326.
## # ℹ 13 more variables: diameter <dbl>, root_order <dbl>, root_ontology <chr>,
## # parent_name <chr>, parent <chr>, insertion_position <dbl>,
## # insertion_angle <dbl>, n_child <dbl>, child_density <dbl>,
## # first_child <chr>, insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>dim(temporary)## [1] 1 22dim(temporary)[1]## [1] 1dim(temporary)[2]## [1] 22total_LRL <- sum(temporary$length)
LR_number <- dim(temporary)[1]
total_LRL## [1] 0.05141825LR_number## [1] 1MR_data_3$LRL <- 0
MR_data_3$LRno <- 0
MR_data_3## # A tibble: 247 × 12
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 237 more rows
## # ℹ 5 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>MR_data_3$LRL[1] <- total_LRL
MR_data_3$LRno[1] <- LR_number
MR_data_3## # A tibble: 247 × 12
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 237 more rows
## # ℹ 5 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>MR_data_noChild <- subset(MR_data_2, MR_data_2$n_child == 0)
MR_data_noChild## # A tibble: 498 × 10
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 20240624001.rsml 1 Pl1_M_02_03 f7a3097e-217… 0.517 0 0
## 2 20240624001.rsml 1 Pl1_M_02_02 7c164c79-754… 0.683 0 0
## 3 20240624001.rsml 1 Pl1_M_01_01 60929313-862… 0.874 0 0
## 4 20240624001.rsml 1 Pl1_M_07_01 c01cb305-504… 0.590 0 0
## 5 20240624001.rsml 1 Pl1_M_02_04 97c81166-074… 0.737 0 0
## 6 20240624001.rsml 1 Pl1_M_02_01 7b9f4dab-e66… 0.613 0 0
## 7 20240624001.rsml 1 Pl1_M_01_04 7b8e778d-05f… 0.656 0 0
## 8 20240624001.rsml 1 Pl1_M_01_03 a58b3c48-fc3… 0.522 0 0
## 9 20240624001.rsml 1 Pl1_M_01_02 a6262902-120… 0.864 0 0
## 10 20240624001.rsml 1 Pl1_M_08_04 31e4dc65-4b4… 0.876 0 0
## # ℹ 488 more rows
## # ℹ 3 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>dim(MR_data_3)## [1] 247 12for(i in 1:247){
temporary <- subset(Lateral_root, Lateral_root$parent == MR_data_3$root[i])
temporary <- subset(temporary, temporary$day == MR_data_3$day[i])
total_LRL <- sum(temporary$length)
LR_number <- dim(temporary)[1]
MR_data_3$LRL[i] <- total_LRL
MR_data_3$LRno[i] <- LR_number
}MR_data_3$check <- MR_data_3$n_child - MR_data_3$LRno
MR_data_3## # A tibble: 247 × 13
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 237 more rows
## # ℹ 6 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, check <dbl>unique(MR_data_3$check)## [1] 0head(MR_data_3)## # A tibble: 6 × 13
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 6282… 5 Pl4_M_01… 6013… 2.25 1 0 1.7297156
## 2 6282… 5 Pl12_M_0… f934… 3.39 1 0 1.31693
## 3 7022… 9 Pl16_C_0… 94e8… 5.40 11 3.32 0.82174563
## 4 7022… 9 Pl16_C_0… 32a8… 5.20 8 4.30 0.90441424
## 5 7022… 9 Pl16_C_0… fb21… 5.68 13 3.37 0.37227115
## 6 7022… 9 Pl16_C_0… f030… 6.93 11 5.02 2.025383
## # ℹ 5 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, check <dbl>MR_data_Child2 <- MR_data_3[,1:12]
MR_data_Child2## # A tibble: 247 × 12
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 237 more rows
## # ℹ 5 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>MR_data_noChild## # A tibble: 498 × 10
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 20240624001.rsml 1 Pl1_M_02_03 f7a3097e-217… 0.517 0 0
## 2 20240624001.rsml 1 Pl1_M_02_02 7c164c79-754… 0.683 0 0
## 3 20240624001.rsml 1 Pl1_M_01_01 60929313-862… 0.874 0 0
## 4 20240624001.rsml 1 Pl1_M_07_01 c01cb305-504… 0.590 0 0
## 5 20240624001.rsml 1 Pl1_M_02_04 97c81166-074… 0.737 0 0
## 6 20240624001.rsml 1 Pl1_M_02_01 7b9f4dab-e66… 0.613 0 0
## 7 20240624001.rsml 1 Pl1_M_01_04 7b8e778d-05f… 0.656 0 0
## 8 20240624001.rsml 1 Pl1_M_01_03 a58b3c48-fc3… 0.522 0 0
## 9 20240624001.rsml 1 Pl1_M_01_02 a6262902-120… 0.864 0 0
## 10 20240624001.rsml 1 Pl1_M_08_04 31e4dc65-4b4… 0.876 0 0
## # ℹ 488 more rows
## # ℹ 3 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>MR_data_noChild$LRL <- 0
MR_data_noChild$LRno <- 0
MR_all <- rbind(MR_data_Child2, MR_data_noChild)
unique(MR_all$day)## [1] 5 9 1?strsplit()MR_all$root_name[1]## [1] "Pl4_M_01_04"text <- strsplit(x = MR_all$root_name[1], split = "_")
text## [[1]]
## [1] "Pl4" "M" "01" "04"genotype <- text[[1]][3]
genotype## [1] "01"cond <- text[[1]][2]
cond## [1] "M"replicate <- text[[1]][4]
replicate## [1] "04"plate <- text[[1]][1]
plate## [1] "Pl4"dim(MR_all)## [1] 745 12for(i in 1:745){
text <- strsplit(x = MR_all$root_name[i], split = "_")
genotype <- text[[1]][3]
cond <- text[[1]][2]
replicate <- text[[1]][4]
plate <- text[[1]][1]
MR_all$genotype[i] <- genotype
MR_all$condition[i] <- cond
MR_all$plate[i] <- plate
MR_all$replicate[i] <- replicate
}## Warning: Unknown or uninitialised column: `genotype`.## Warning: Unknown or uninitialised column: `condition`.## Warning: Unknown or uninitialised column: `plate`.## Warning: Unknown or uninitialised column: `replicate`.MR_all## # A tibble: 745 × 16
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 735 more rows
## # ℹ 9 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, genotype <chr>,
## # condition <chr>, plate <chr>, replicate <chr>unique(MR_all$genotype)## [1] "01" "04" "06" "05" "03" "02" "08" "07" NAMR_all.nona <- na.omit(MR_all)
unique(MR_all.nona$genotype)## [1] "01" "04" "06" "05" "03" "02" "08" "07"unique(MR_all.nona$condition)## [1] "M" "C"dim(MR_all.nona)## [1] 725 16unique(MR_all$day)## [1] 5 9 1MR_all.nona$TRS <- MR_all.nona$length + MR_all.nona$LRL
MR_all.nona$aLRL <- MR_all.nona$LRL/ MR_all.nona$LRno
MR_all.nona$MRpLRL <- MR_all.nona$length / MR_all.nona$LRL
MR_all.nona## # A tibble: 725 × 19
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3… 2.25 1 0
## 2 62824_012.rsml 5 Pl12_M_04_03 f934e717-3acb… 3.39 1 0
## 3 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 4 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 5 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 6 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 7 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 8 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 9 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 10 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## # ℹ 715 more rows
## # ℹ 12 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, genotype <chr>,
## # condition <chr>, plate <chr>, replicate <chr>, TRS <dbl>, aLRL <dbl>,
## # MRpLRL <dbl>library(ggplot2)
library(ggpubr)MR_all <- MR_all.nona
unique(MR_all.nona$condition)## [1] "M" "C"MR_all.nona$condition <- as.factor(MR_all.nona$condition)
histogram_TRS <- ggdensity(MR_all.nona, x = "TRS",
add = "mean", rug = TRUE,facet.by = "day",
color = "condition", fill = "condition",
panel.labs = list(day = c("Day 1", "Day 2", "Day 3")),
palette = c("#00AFBB", "#E7B800", "#FF0000"))
histogram_TRS <- ggpar(histogram_TRS, xlab = "Total Root Size", ylab = "Density", legend.title = "Condition")
histogram_TRS
?ggparhistogram_LRL <- ggdensity(MR_all.nona, x = "LRL",
add = "mean", rug = TRUE,facet.by = "day",
color = "condition", fill = "condition",
palette = c("#00AFBB", "#E7B800", "#FF0000"))
histogram_LRL
pdf("histogram.TRS.pdf")
plot(histogram_TRS)
# if plotting multiple graphs - this command is extremely important
dev.off()## quartz_off_screen
## 2library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendpdf("Figure_MAIN_1.pdf", height = 15, width = 12)
plot_grid(histogram_TRS, histogram_LRL, ncol=2,
align = "hv", labels=c("AUTO"),
label_size = 24)
dev.off()## quartz_off_screen
## 2unique(MR_all$genotype)## [1] "01" "04" "06" "05" "03" "02" "08" "07"TRS_lgraph <- ggplot(data=MR_all.nona, aes(x= genotype, y=TRS, color = condition))
TRS_lgraph <- TRS_lgraph + geom_boxplot()
TRS_lgraph <- TRS_lgraph + facet_grid(~ condition) + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange"))
TRS_lgraph <- TRS_lgraph + ylab("Total root size (cm)") + xlab("") + theme(legend.position='none')
TRS_lgraph
#possibly if I did this right - no significance between the the diff
accessions
library(ggsignif)library(ggplot2)
library(ggsignif)
TRS_lgraph_sig <- ggplot(data = MR_all.nona, aes(x = genotype, y = TRS, color = condition)) +
geom_boxplot() +
facet_grid(~ condition) +
scale_color_manual(values = c("turquoise3", "maroon3", "dark orange")) +
ylab("Total root size (cm)") +
xlab("") +
theme(legend.position = 'none') +
geom_signif(comparisons = list(c("01", "07")),
map_signif_level = TRUE,
textsize = 3.5)
TRS_lgraph_sig
library(ggsci)
library(ggbeeswarm)
library(gapminder)
library(RColorBrewer)
library(ggridges)
better_TRS_graph <- ggplot(data=MR_all.nona, aes(x= genotype, y=TRS, color = condition))
better_TRS_graph <- better_TRS_graph + geom_beeswarm(alpha=0.6, priority = "density")
better_TRS_graph <- better_TRS_graph + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")## Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
## ℹ Please use the `fun` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.better_TRS_graph <- better_TRS_graph + facet_grid(day~ condition) + scale_color_manual(values=c("turquoise3", "maroon3", "dark orange"))
better_TRS_graph <- better_TRS_graph + ylab("Total root size (cm)") + xlab("Genotype") + theme(legend.position='none')
better_TRS_graph <- better_TRS_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
better_TRS_graph <- better_TRS_graph + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "01")
better_TRS_graph## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! argument "x" is missing, with no default## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! Can't find specified reference group: 1. Allowed values include one of: 4, 2, 14, 16, 8, 6, 12, 10## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! argument "x" is missing, with no default## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! Can't find specified reference group: 1. Allowed values include one of: 2, 8, 16, 12, 10, 14, 6, 4## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! argument "x" is missing, with no default## Warning: Computation failed in `stat_compare_means()`.
## Caused by error:
## ! Can't find specified reference group: 1. Allowed values include one of: 2, 16, 14, 4, 8, 6, 12, 10
day9 <- subset(MR_all.nona, MR_all.nona$day == "9")
day9## # A tibble: 241 × 19
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 70224_016.rsml 9 Pl16_C_06_04 94e86c63-b8aa… 5.40 11 3.32
## 2 70224_016.rsml 9 Pl16_C_06_03 32a861f3-e08b… 5.20 8 4.30
## 3 70224_016.rsml 9 Pl16_C_06_02 fb21ef9c-0363… 5.68 13 3.37
## 4 70224_016.rsml 9 Pl16_C_06_01 f030b1a4-8354… 6.93 11 5.02
## 5 70224_016.rsml 9 Pl16_C_05_04 8c4ccc99-1a35… 5.58 11 6.09
## 6 70224_016.rsml 9 Pl16_C_04_04 8ff8ab53-6d69… 6.57 14 4.20
## 7 70224_016.rsml 9 Pl16_C_04_03 8b9afaeb-9612… 7.65 23 5.21
## 8 70224_016.rsml 9 Pl16_C_04_02 28730492-9c81… 6.57 21 5.70
## 9 70224_016.rsml 9 Pl16_C_03_02 2a99cf64-03d3… 7.78 21 5.17
## 10 70224_016.rsml 9 Pl16_C_03_01 3da76b7a-d1c6… 6.74 11 4.18
## # ℹ 231 more rows
## # ℹ 12 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, genotype <chr>,
## # condition <fct>, plate <chr>, replicate <chr>, TRS <dbl>, aLRL <dbl>,
## # MRpLRL <dbl>day 9
?ggplot# decoding genotypes
day9$genotype <- gsub("01", "Col.0", day9$genotype)
day9$genotype <- gsub("02", "cis1.1", day9$genotype)
day9$genotype <- gsub("03", "agl16.1", day9$genotype)
day9$genotype <- gsub("04", "akr4c9.3A", day9$genotype)
day9$genotype <- gsub("05", "cis1.2", day9$genotype)
day9$genotype <- gsub("06", "agl16.2", day9$genotype)
day9$genotype <- gsub("07", "akr4c9.2I", day9$genotype)
day9$genotype <- gsub("08", "akr4c9.1F2", day9$genotype)
day9$condition <- gsub("C", "Control", day9$condition)
day9$condition <- gsub("M", "Mannitol", day9$condition)day9$genotype <- factor(day9$genotype, levels = c("Col.0", "akr4c9.3A", "akr4c9.2I", "akr4c9.1F2", "agl16.1", "agl16.2", "cis1.1", "cis1.2"))
better_TRS_graph <- ggplot(data=day9, aes(x= genotype, y=TRS, color = condition))
better_TRS_graph <- better_TRS_graph + geom_beeswarm(alpha=0.6, priority = "density")
better_TRS_graph <- better_TRS_graph + stat_summary(fun.y=mean, geom="point", shape=95, size=6, color="black", fill="black")
better_TRS_graph <- better_TRS_graph + facet_grid(~ condition) + scale_color_manual(values=c("royalblue", "orange"))
better_TRS_graph <- better_TRS_graph + ylab("Total root size (cm)") + xlab("") + theme(legend.position='none')
better_TRS_graph <- better_TRS_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
better_TRS_graph <- better_TRS_graph + stat_compare_means(label = "p.signif", method = "t.test", ref.group = "01")
better_TRS_graph <- better_TRS_graph + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
better_TRS_graph## Warning: Computation failed in `stat_compare_means()`.
## Computation failed in `stat_compare_means()`.
## Caused by error in `if (ref.group == ".all.") ...`:
## ! missing value where TRUE/FALSE needed
## adding p-values
library(stringr)
library(multcompView)
Control <- subset(day9, day9$condition == "Control")
Mannitol <- subset(day9, day9$condition == "Mannitol")
Control$genotype <- as.factor(Control$genotype)
Mannitol$genotype <- as.factor(Mannitol$genotype)
Output <- TukeyHSD(aov(TRS ~ genotype, data = Control))
P7 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P7)
testc <- as.data.frame(stat.test$Letters)
testc$group2 <- rownames(testc)
testc$group1 <- rownames(testc)
testc$genotype <- rownames(testc)
testc$genotype <- rownames(testc)
colnames(testc)[1] <- "Tukey"
testc$condition <- "Control"
Output <- TukeyHSD(aov(TRS ~ genotype, data = Mannitol))
P7 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P7)
tests <- as.data.frame(stat.test$Letters)
tests$group2 <- rownames(tests)
tests$group1 <- rownames(tests)
tests$genotype <- rownames(tests)
tests$genotype <- rownames(tests)
colnames(tests)[1] <- "Tukey"
tests$condition <- "Mannitol"
test <- rbind(testc, tests)
test## Tukey group2 group1 genotype condition
## akr4c9.3A ab akr4c9.3A akr4c9.3A akr4c9.3A Control
## akr4c9.2I a akr4c9.2I akr4c9.2I akr4c9.2I Control
## akr4c9.1F2 ab akr4c9.1F2 akr4c9.1F2 akr4c9.1F2 Control
## agl16.1 ab agl16.1 agl16.1 agl16.1 Control
## agl16.2 ab agl16.2 agl16.2 agl16.2 Control
## cis1.1 b cis1.1 cis1.1 cis1.1 Control
## cis1.2 b cis1.2 cis1.2 cis1.2 Control
## Col.0 ab Col.0 Col.0 Col.0 Control
## akr4c9.3A1 abc akr4c9.3A akr4c9.3A akr4c9.3A Mannitol
## akr4c9.2I1 ab akr4c9.2I akr4c9.2I akr4c9.2I Mannitol
## akr4c9.1F21 a akr4c9.1F2 akr4c9.1F2 akr4c9.1F2 Mannitol
## agl16.11 c agl16.1 agl16.1 agl16.1 Mannitol
## agl16.21 abc agl16.2 agl16.2 agl16.2 Mannitol
## cis1.11 abc cis1.1 cis1.1 cis1.1 Mannitol
## cis1.21 abc cis1.2 cis1.2 cis1.2 Mannitol
## Col.01 bc Col.0 Col.0 Col.0 Mannitolbetter_TRS_graph_2 <- better_TRS_graph + stat_pvalue_manual(test, label = "Tukey", y.position = 25)
better_TRS_graph_2## Warning: Computation failed in `stat_compare_means()`.
## Computation failed in `stat_compare_means()`.
## Caused by error in `if (ref.group == ".all.") ...`:
## ! missing value where TRUE/FALSE needed
head(Mannitol)## # A tibble: 6 × 19
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 7022… 9 Pl1_M_01… 5c1c… 2.94 2 1.11 0
## 2 7022… 9 Pl1_M_08… 6e29… 4.72 6 3.38 0.4548766
## 3 7022… 9 Pl1_M_08… cbaf… 4.75 7 4.66 1.013784
## 4 7022… 9 Pl1_M_08… 7336… 5.01 6 2.82 0.19812164
## 5 7022… 9 Pl1_M_07… 434c… 4.36 4 4.66 1.4458275
## 6 7022… 9 Pl1_M_07… 6c41… 5.44 10 3.41 0.66941154
## # ℹ 11 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <fct>, condition <chr>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>head(day9)## # A tibble: 6 × 19
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 7022… 9 Pl16_C_0… 94e8… 5.40 11 3.32 0.82174563
## 2 7022… 9 Pl16_C_0… 32a8… 5.20 8 4.30 0.90441424
## 3 7022… 9 Pl16_C_0… fb21… 5.68 13 3.37 0.37227115
## 4 7022… 9 Pl16_C_0… f030… 6.93 11 5.02 2.025383
## 5 7022… 9 Pl16_C_0… 8c4c… 5.58 11 6.09 1.3077601
## 6 7022… 9 Pl16_C_0… 8ff8… 6.57 14 4.20 0
## # ℹ 11 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <fct>, condition <chr>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>library(ggpubr)library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layouthead(MR_all.nona)## # A tibble: 6 × 19
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 6282… 5 Pl4_M_01… 6013… 2.25 1 0 1.7297156
## 2 6282… 5 Pl12_M_0… f934… 3.39 1 0 1.31693
## 3 7022… 9 Pl16_C_0… 94e8… 5.40 11 3.32 0.82174563
## 4 7022… 9 Pl16_C_0… 32a8… 5.20 8 4.30 0.90441424
## 5 7022… 9 Pl16_C_0… fb21… 5.68 13 3.37 0.37227115
## 6 7022… 9 Pl16_C_0… f030… 6.93 11 5.02 2.025383
## # ℹ 11 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <chr>, condition <fct>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>my_graph <- ggplot(data=MR_all.nona, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("Main Root Length") + theme(legend.position='none')
my_graph
ggplotly(my_graph)geno_c <- subset(MR_all.nona, MR_all.nona$genotype == "01")geno_c <- subset(MR_all.nona, MR_all.nona$genotype == "01")
my_graph <- ggplot(data=geno_c, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("Col-O Main Root Length (cm)") + theme(legend.position='none')
my_graph
my_graph <- ggplot(data=geno_c, aes(x= day, y=TRS, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Total Root Length") + theme(legend.position='none')
my_graph
my_graph <- ggplot(data=geno_c, aes(x= day, y=LRno, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("number") + xlab("time (day)") + ggtitle("Lateral Root number") + theme(legend.position='none')
my_graph
my_graph <- ggplot(data=geno_c, aes(x= day, y=LRL, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length (cm)") + xlab("time (day)") + ggtitle("Lateral Root Length") + theme(legend.position='none')
my_graph
geno_02 <- subset(MR_all.nona, MR_all.nona$genotype == "02")
my_graph <- ggplot(data=geno_02, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("02 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_03 <- subset(MR_all.nona, MR_all.nona$genotype == "03")
my_graph <- ggplot(data=geno_03, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("03 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_04 <- subset(MR_all.nona, MR_all.nona$genotype == "04")
my_graph <- ggplot(data=geno_04, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("04 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_05 <- subset(MR_all.nona, MR_all.nona$genotype == "05")
my_graph <- ggplot(data=geno_05, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("05 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_06 <- subset(MR_all.nona, MR_all.nona$genotype == "06")
my_graph <- ggplot(data=geno_06, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("06 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_07 <- subset(MR_all.nona, MR_all.nona$genotype == "07")
my_graph <- ggplot(data=geno_07, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle("07 Main Root Length (cm)") + theme(legend.position='none')
my_graph
geno_08 <- subset(MR_all.nona, MR_all.nona$genotype == "08")
my_graph <- ggplot(data=geno_08, aes(x= day, y=length, group = root_name, color = genotype))
my_graph <- my_graph + geom_line(alpha = 0.2)
my_graph <- my_graph + facet_grid(~ condition)
my_graph <- my_graph + ylab("length") + xlab("time (day)") + ggtitle(" 08 Main Root Length (cm)") + theme(legend.position='none')
my_graph
MR_both_geno <- rbind(geno_c,geno_02, geno_03,geno_04,geno_05,geno_06, geno_07, geno_08)
MR_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=length, group = root_name, color = genotype))
MR_time_graph <- MR_time_graph + geom_line(alpha = 0.1)
MR_time_graph <- MR_time_graph + facet_grid(~ condition)
MR_time_graph <- MR_time_graph + scale_color_manual(values= c("blue", "orange", "black", "hotpink","red", "darkblue","darkgreen", "purple" ))
MR_time_graph <- MR_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Main Root Length")
MR_time_graph <- MR_time_graph + stat_summary(fun.y=mean, aes(group= genotype), size=0.7, geom="line", linetype = "dashed")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.MR_time_graph
LRL_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=LRL, group = root_name, color = genotype))
LRL_time_graph <- LRL_time_graph + geom_line(alpha = 0.2)
LRL_time_graph <- LRL_time_graph + facet_grid(~ condition)
LRL_time_graph <- LRL_time_graph + scale_color_manual(values= c("blue", "orange", "black", "hotpink","red", "darkblue","darkgreen", "purple"))
LRL_time_graph <- LRL_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Lateral Root Length")
LRL_time_graph <- LRL_time_graph + stat_summary(fun.y=mean, aes(group= genotype), size=0.7, geom="line", linetype = "dashed")
LRL_time_graph
LRno_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=LRno, group = root_name, color = genotype))
LRno_time_graph <- LRno_time_graph + geom_line(alpha = 0.2)
LRno_time_graph <- LRno_time_graph + facet_grid(~ condition)
LRno_time_graph <- LRno_time_graph + scale_color_manual(values= c("blue", "orange", "black", "hotpink","red", "darkblue","darkgreen", "purple"))
LRno_time_graph <- LRno_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Lateral Root Number")
LRno_time_graph <- LRno_time_graph + stat_summary(fun.y=mean, aes(group= genotype), size=0.7, geom="line", linetype = "dashed")
LRno_time_graph
TRS_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=TRS, group = root_name, color = genotype))
TRS_time_graph <- TRS_time_graph + geom_line(alpha = 0.2)
TRS_time_graph <- TRS_time_graph + facet_grid(~ condition)
TRS_time_graph <- TRS_time_graph + scale_color_manual(values= c("blue", "orange", "black", "hotpink","red", "darkblue","darkgreen", "purple"))
TRS_time_graph <- TRS_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Total Root Length")
TRS_time_graph <- TRS_time_graph + stat_summary(fun.y=mean, aes(group= genotype), size=0.7, geom="line", linetype = "dashed")
TRS_time_graph
aLRL_time_graph <- ggplot(data=MR_both_geno, aes(x= day, y=aLRL, group = root_name, color = genotype))
aLRL_time_graph <- aLRL_time_graph + geom_line(alpha = 0.2)
aLRL_time_graph <- aLRL_time_graph + facet_grid(~ condition)
aLRL_time_graph <- aLRL_time_graph + scale_color_manual(values= c("blue", "orange", "black", "hotpink","red", "darkblue","darkgreen", "purple"))
aLRL_time_graph <- aLRL_time_graph + ylab("length (cm)") + xlab("time (days after germination)") + ggtitle("Total Average Lateral Root Length")
aLRL_time_graph <- aLRL_time_graph + stat_summary(fun.y=mean, aes(group= genotype), size=0.7, geom="line", linetype = "dashed")
aLRL_time_graph## Warning: Removed 479 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 478 rows containing missing values or values outside the scale range
## (`geom_line()`).
pdf("20221011_Arabidopsis_DUF_OE_RSA_Time_Graphs.pdf", width = 13, height = 13)
plot_grid(MR_time_graph , LRL_time_graph, LRno_time_graph, TRS_time_graph, labels = c("AUTO"), ncol = 2)
#pdf("20221011_Arabidopsis_DUF_OE_RSA_Time_Graphs.pdf", width = 13, height = 13)
dev.off()## quartz_off_screen
## 2Growth rate calculations
MR_both_geno## # A tibble: 725 × 19
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3-… 2.25 1 0
## 2 62824_008.rsml 5 Pl8_C_01_01 e8d074f3-f693-… 2.97 1 0
## 3 62824_008.rsml 5 Pl8_C_01_04 aeb10c8f-f7b7-… 2.60 1 0
## 4 62824_008.rsml 5 Pl8_C_01_02 c87267be-b262-… 3.42 2 6.26
## 5 62824_009.rsml 5 Pl9_C_01_01 456ad34f-f37b-… 2.04 1 0
## 6 62824_009.rsml 5 Pl9_C_01_02 7bcd00b4-d8df-… 3.39 2 5.81
## 7 70224_001.rsml 9 Pl1_M_01_04 5c1cd1f0-119a-… 2.94 2 1.11
## 8 70224_001.rsml 9 Pl1_M_01_01 60929313-8622-… 4.07 2 4.47
## 9 70224_001.rsml 9 Pl1_M_01_03 a58b3c48-fc3b-… 2.07 1 0
## 10 70224_006.rsml 9 Pl6_C_01_03 38879f0d-684c-… 5.22 7 2.43
## # ℹ 715 more rows
## # ℹ 12 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, genotype <chr>,
## # condition <fct>, plate <chr>, replicate <chr>, TRS <dbl>, aLRL <dbl>,
## # MRpLRL <dbl>new_name <- strsplit(MR_both_geno$root_name[1], "_")[[1]][2:4]
paste(new_name[1], new_name[2], new_name[3], sep="_")## [1] "M_01_04"for(i in 1:nrow(MR_both_geno)){
new_name <- strsplit(MR_both_geno$root_name[i], "_")[[1]][2:4]
MR_both_geno$root_name2[i] <-paste(new_name[1], new_name[2], new_name[3], sep="_")
}## Warning: Unknown or uninitialised column: `root_name2`.MR_both_geno## # A tibble: 725 × 20
## image day root_name root length n_child child_density
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 62824_012.rsml 5 Pl4_M_01_04 6013d64d-30a3-… 2.25 1 0
## 2 62824_008.rsml 5 Pl8_C_01_01 e8d074f3-f693-… 2.97 1 0
## 3 62824_008.rsml 5 Pl8_C_01_04 aeb10c8f-f7b7-… 2.60 1 0
## 4 62824_008.rsml 5 Pl8_C_01_02 c87267be-b262-… 3.42 2 6.26
## 5 62824_009.rsml 5 Pl9_C_01_01 456ad34f-f37b-… 2.04 1 0
## 6 62824_009.rsml 5 Pl9_C_01_02 7bcd00b4-d8df-… 3.39 2 5.81
## 7 70224_001.rsml 9 Pl1_M_01_04 5c1cd1f0-119a-… 2.94 2 1.11
## 8 70224_001.rsml 9 Pl1_M_01_01 60929313-8622-… 4.07 2 4.47
## 9 70224_001.rsml 9 Pl1_M_01_03 a58b3c48-fc3b-… 2.07 1 0
## 10 70224_006.rsml 9 Pl6_C_01_03 38879f0d-684c-… 5.22 7 2.43
## # ℹ 715 more rows
## # ℹ 13 more variables: insertion_first_child <chr>, last_child <chr>,
## # insertion_last_child <chr>, LRL <dbl>, LRno <dbl>, genotype <chr>,
## # condition <fct>, plate <chr>, replicate <chr>, TRS <dbl>, aLRL <dbl>,
## # MRpLRL <dbl>, root_name2 <chr>temp1 <- subset(MR_both_geno, MR_both_geno$root_name == unique(MR_both_geno$root_name)[22])
temp1## # A tibble: 3 × 20
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 7022… 9 Pl12_M_0… a702… 5.80 12 3.89 0.46097413
## 2 6282… 5 Pl12_M_0… a702… 3.18 0 0 null
## 3 2024… 1 Pl12_M_0… a702… 0.936 0 0 null
## # ℹ 12 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <chr>, condition <fct>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>, root_name2 <chr>temp2 <- temp1[order(temp1$day),]
temp2## # A tibble: 3 × 20
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 2024… 1 Pl12_M_0… a702… 0.936 0 0 null
## 2 6282… 5 Pl12_M_0… a702… 3.18 0 0 null
## 3 7022… 9 Pl12_M_0… a702… 5.80 12 3.89 0.46097413
## # ℹ 12 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <chr>, condition <fct>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>, root_name2 <chr>temp2$condition[1]## [1] M
## Levels: C Mtemp_MR <- temp2[,c("day", "length")]
plot(temp_MR$length~ temp_MR$day)
plot(temp_MR$length~ temp_MR$day)
# let's add the regression line to this graph
abline(lm(temp_MR$length~ temp_MR$day))
MR_model <- lm(temp_MR$length~ temp_MR$day)
MR_model##
## Call:
## lm(formula = temp_MR$length ~ temp_MR$day)
##
## Coefficients:
## (Intercept) temp_MR$day
## 0.2663 0.6080summary(MR_model)##
## Call:
## lm(formula = temp_MR$length ~ temp_MR$day)
##
## Residuals:
## 1 2 3
## 0.06156 -0.12312 0.06156
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.26629 0.15919 1.673 0.3430
## temp_MR$day 0.60802 0.02666 22.810 0.0279 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1508 on 1 degrees of freedom
## Multiple R-squared: 0.9981, Adjusted R-squared: 0.9962
## F-statistic: 520.3 on 1 and 1 DF, p-value: 0.02789MR_model$coefficients[[2]]## [1] 0.6080217MR_growth_rate <- MR_model$coefficients[[2]]
temp2## # A tibble: 3 × 20
## image day root_name root length n_child child_density insertion_first_child
## <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 2024… 1 Pl12_M_0… a702… 0.936 0 0 null
## 2 6282… 5 Pl12_M_0… a702… 3.18 0 0 null
## 3 7022… 9 Pl12_M_0… a702… 5.80 12 3.89 0.46097413
## # ℹ 12 more variables: last_child <chr>, insertion_last_child <chr>, LRL <dbl>,
## # LRno <dbl>, genotype <chr>, condition <fct>, plate <chr>, replicate <chr>,
## # TRS <dbl>, aLRL <dbl>, MRpLRL <dbl>, root_name2 <chr>LR_temp <- temp2[,c("day", "LRno", "aLRL")]
LR_temp2 <- na.omit(LR_temp)
LR_temp2## # A tibble: 1 × 3
## day LRno aLRL
## <dbl> <dbl> <dbl>
## 1 9 12 0.276LRno_model <- lm(LR_temp2$LRno ~ LR_temp2$day)
LRno_model##
## Call:
## lm(formula = LR_temp2$LRno ~ LR_temp2$day)
##
## Coefficients:
## (Intercept) LR_temp2$day
## 12 NAsummary(LRno_model)##
## Call:
## lm(formula = LR_temp2$LRno ~ LR_temp2$day)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12 NaN NaN NaN
## LR_temp2$day NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedomLRno_increase <- as.numeric(as.character(LRno_model$coefficients[[2]]))
LRno_increase## [1] NAaLRL_model <- lm(LR_temp2$aLRL ~ LR_temp2$day)
aLRL_model##
## Call:
## lm(formula = LR_temp2$aLRL ~ LR_temp2$day)
##
## Coefficients:
## (Intercept) LR_temp2$day
## 0.276 NAsummary(aLRL_model)##
## Call:
## lm(formula = LR_temp2$aLRL ~ LR_temp2$day)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.276 NaN NaN NaN
## LR_temp2$day NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedomaLRL_growth <- as.numeric(as.character(aLRL_model$coefficients[[2]]))
aLRL_growth## [1] NAtemp2$condition[1]## [1] M
## Levels: C Mtemp2$condition[1][1]## [1] M
## Levels: C Mnames <- c(text="root_name", "genotype", "condition", "MR.delta", "LRno.delta", "aLRL.delta")
growth_factors <- data.frame()
growth_factors## data frame with 0 columns and 0 rowsfor (k in names) growth_factors[[k]] <- as.character()
growth_factors[1,1] <- temp2$root_name[1]
growth_factors[1,2] <- temp2$genotype[1]
growth_factors[1,3] <- temp2$condition[[1]]
growth_factors[1,4] <- as.numeric(as.character(MR_growth_rate))
growth_factors[1,5] <- as.numeric(as.character(LRno_increase))
growth_factors[1,6] <- as.numeric(as.character(aLRL_growth))
growth_factors## root_name genotype condition MR.delta LRno.delta aLRL.delta
## 1 Pl12_M_01_3 01 2 0.6080217 <NA> <NA>length(unique(MR_both_geno$root_name))## [1] 259for(e in 1:259){
temp1 <- subset(MR_both_geno, MR_both_geno$root_name == unique(MR_both_geno$root_name)[e])
temp2 <- temp1[order(temp1$day),]
temp2
if(dim(temp2)[1] > 2){
############ MR calculations
temp_MR <- temp2[,c("day", "length")]
temp_MR$MRdouble <- "no"
for(i in 2:nrow(temp_MR)){
# we want the root to be at least 1 mm larger than the previous day - all the other ones will just indicate noise:
if(temp_MR$length[i] <= temp_MR$length[i-1]+0.09){
temp_MR$MRdouble[i] <- "yes"
} else{temp_MR$MRdouble[i] <- "no"}}
temp_MR2 <- subset(temp_MR, temp_MR$MRdouble == "no")
temp_MR2
MR_model <- lm(temp_MR2$length~ temp_MR2$day)
MR_growth_rate <- MR_model$coefficients[[2]]
MR_growth_rate }else{
MR_growth_rate <- 0
}
############ LRno calculations
LR_temp <- temp2[,c("day", "LRno", "aLRL")]
LR_temp2 <- na.omit(LR_temp)
LR_temp2
dim(LR_temp2)
####################### safety precaution to calculate LR growth rate only for the plants that have LR at least for two days:
if(dim(LR_temp2)[1] > 1){
LRno_model <- lm(LR_temp2$LRno ~ LR_temp2$day)
LRno_increase <- as.numeric(as.character(LRno_model$coefficients[[2]]))
############ aLRL calculations
aLRL_model <- lm(LR_temp2$aLRL ~ LR_temp2$day)
aLRL_growth <- as.numeric(as.character(aLRL_model$coefficients[[2]]))
} else{
####################### safety precaution continued:
####################### so if you only have one day where LR are there - this wont be good enough to calculate LRno or LRL rate
####################### and thus:
LRno_increase <- 0
aLRL_growth <- 0
}
LRno_increase
aLRL_growth
############ adding the information to the table:
growth_factors[e,1] <- temp2$root_name[1]
growth_factors[e,2] <- temp2$genotype[1]
growth_factors[e,3] <- temp2$condition[1]
growth_factors[e,4] <- as.numeric(as.character(MR_growth_rate))
growth_factors[e,5] <- as.numeric(as.character(LRno_increase))
growth_factors[e,6] <- as.numeric(as.character(aLRL_growth))
}
growth_factors## root_name genotype condition MR.delta LRno.delta
## 1 Pl4_M_01_04 01 2 0 0
## 2 Pl8_C_01_01 01 1 0.813158725 4.5
## 3 Pl8_C_01_04 01 1 0.710006275 2.25
## 4 Pl8_C_01_02 01 1 0.8466061925 4.25
## 5 Pl9_C_01_01 01 1 0.617869375 1
## 6 Pl9_C_01_02 01 1 0.840557725 4.25
## 7 Pl1_M_01_04 01 2 0.2859252375 0
## 8 Pl1_M_01_01 01 2 0.3993961875 0
## 9 Pl1_M_01_03 01 2 0.1940168625 0
## 10 Pl6_C_01_03 01 1 0.536725875 0
## 11 Pl6_C_01_02 01 1 0.5971264625 0
## 12 Pl10_M_01_04 01 2 0 0
## 13 Pl10_M_01_03 01 2 0 0
## 14 Pl10_M_01_02 01 2 0 0
## 15 Pl10_M_01_01 01 2 0 0
## 16 Pl11_M_01_01 01 2 0.42590645 0
## 17 Pl11_M_01_03 01 2 0.5157705 0
## 18 Pl7_C_01_04 01 1 0.403761675 0
## 19 Pl7_C_01_03 01 1 0.502998425 0
## 20 Pl7_C_01_01 01 1 0.54359619375 0
## 21 Pl12_M_01_04 01 2 0 0
## 22 Pl12_M_01_3 01 2 0.6080217 0
## 23 Pl12_M_01_01 01 2 0.4099114875 0
## 24 Pl12_M_01_02 01 2 0.441791725 0
## 25 Pl8_C_01_03 01 1 0.591046995 0
## 26 Pl9_C_01_04 01 1 0.74088145 0
## 27 Pl9_C_01_03 01 1 0.7701522 0
## 28 Pl10_C_01_01 01 1 0 0
## 29 Pl1_M_01_02 01 2 0.140476945 0
## 30 Pl7_C_01_02 01 1 0.251226295 0
## 31 Pl6_C_01_04 01 1 0.4352060875 0
## 32 Pl6_C_01_01 01 1 0.250516125 0
## 33 Pl11_M_01_04 01 2 0 0
## 34 Pl11_M_01_02 01 2 0 0
## 35 Pl10_C_01_04 01 1 0 0
## 36 Pl10_C_01_03 01 1 0 0
## 37 Pl10_C_01_02 01 1 0 0
## 38 Pl8_C_02_02 02 1 0.8309767 3.25
## 39 Pl9_C_02_04 02 1 0.8403002125 4
## 40 Pl9_C_02_03 02 1 0.8994527875 2
## 41 Pl1_M_02_03 02 2 0.345294135 0
## 42 Pl1_M_02_02 02 2 0.185601775 0
## 43 Pl1_M_02_04 02 2 0.385113505 0
## 44 Pl1_M_02_01 02 2 0.34125653 0
## 45 Pl6_C_02_02 02 1 0.5317902 0
## 46 Pl6_C_02_04 02 1 0.169884475 0
## 47 Pl6_C_02_03 02 1 0.4919284875 0
## 48 Pl10_M_02_04 02 2 0 0
## 49 Pl10_M_02_03 02 2 0 0
## 50 Pl10_M_02_02 02 2 0 0
## 51 Pl10_M_02_01 02 2 0 0
## 52 Pl11_M_02_03 02 2 0.3819924875 0
## 53 Pl11_M_02_02 02 2 0.43507329625 0
## 54 Pl7_C_02_01 02 1 0.529759 0
## 55 Pl12_M_02_04 02 2 0.5745379 0
## 56 Pl12_M_02_03 02 2 0.4310821 0
## 57 Pl12_M_02_02 02 2 0.3916473875 0
## 58 Pl12_M_02_01 02 2 0.4807238075 0
## 59 Pl8_C_02_04 02 1 0.5845632375 0
## 60 Pl8_C_02_03 02 1 0.693181277954546 0
## 61 Pl8_C_02_01 02 1 0.6194379875 0
## 62 Pl9_C_02_02 02 1 0.5574316175 0
## 63 Pl9_C_02_01 02 1 0.962761725 0
## 64 Pl10_C_02_01 02 1 0 0
## 65 Pl7_C_02_04 02 1 0.398648825 0
## 66 Pl7_C_02_03 02 1 0.27379739375 0
## 67 Pl7_C_02_02 02 1 0.3684457 0
## 68 Pl6_C_02_01 02 1 0.4529618875 0
## 69 Pl11_M_02_04 02 2 0.409647375 0
## 70 Pl11_M_02_01 02 2 0.3578801125 0
## 71 Pl10_C_02_04 02 1 0 0
## 72 Pl10_C_02_03 02 1 0 0
## 73 Pl10_C_02_02 02 1 0 0
## 74 Pl16_C_03_02 03 1 0.928972825 0
## 75 Pl16_C_03_01 03 1 0.80565257875 0
## 76 Pl9_C_03_01 03 1 0.509486225 3.25
## 77 Pl9_C_03_02 03 1 0.568553245 1
## 78 Pl15_C_03_04 03 1 0.9316032 6.25
## 79 Pl12_M_03_04 03 2 0.4806823 0
## 80 Pl12_M_03_03 03 2 0.476534975 0
## 81 Pl8_C_03_04 03 1 0.5346180875 0
## 82 Pl8_C_03_03 03 1 0.57301961875 0
## 83 Pl8_C_03_02 03 1 0.57839092 0
## 84 Pl8_C_03_01 03 1 0.5683905 0
## 85 Pl9_C_03_04 03 1 0.589936025 0
## 86 Pl13_M_03_03 03 2 0.60909883375 0
## 87 Pl13_M_03_02 03 2 0.5748877375 0
## 88 Pl13_M_03_01 03 2 0.4479638175 0
## 89 Pl14_M_03_02 03 2 0.50800475 0
## 90 Pl14_M_03_04 03 2 0.340379 0
## 91 Pl14_M_03_01 03 2 0.5724312125 0
## 92 Pl15_C_03_03 03 1 0.62774635 0
## 93 Pl12_M_03_02 03 2 0.231007875 0
## 94 Pl12_M_03_01 03 2 0.37259555375 0
## 95 Pl9_C_03_03 03 1 0.488624925 0
## 96 Pl13_M_03_04 03 2 0.3598815375 0
## 97 Pl10_M_03_04 03 2 0 0
## 98 Pl10_M_03_03 03 2 0 0
## 99 Pl10_M_03_02 03 2 0 0
## 100 Pl10_M_03_01 03 2 0 0
## 101 Pl14_M_03_03 03 2 0 0
## 102 Pl15_C_03_02 03 1 0.437783855 0
## 103 Pl15_C_03_01 03 1 0.55199556625 0
## 104 Pl12_M_04_03 04 2 0.4833221875 1.75
## 105 Pl16_C_04_04 04 1 0.76160152 0
## 106 Pl16_C_04_03 04 1 0.87578631875 5.5
## 107 Pl16_C_04_02 04 1 0.7195959 5
## 108 Pl8_C_04_02 04 1 0.480335075 2
## 109 Pl13_M_04_04 04 2 0.602540475 2.25
## 110 Pl13_M_04_02 04 2 0.5412957 2
## 111 Pl10_M_04_02 04 2 0 0
## 112 Pl10_M_04_01 04 2 0 0
## 113 Pl15_C_04_02 04 1 0.8344400675 2.75
## 114 Pl12_M_04_04 04 2 0.48705905 0
## 115 Pl12_M_04_02 04 2 0.454433075 0
## 116 Pl8_C_04_04 04 1 0 0
## 117 Pl8_C_04_03 04 1 0 0
## 118 Pl9_C_04_04 04 1 0.61172129375 0
## 119 Pl9_C_04_03 04 1 0.59023315875 0
## 120 Pl9_C_04_02 04 1 0.5576237125 0
## 121 Pl9_C_04_01 04 1 0.5629938925 0
## 122 Pl13_M_04_03 04 2 0.45430235 0
## 123 Pl14_M_04_02 04 2 0.3788431375 0
## 124 Pl14_M_04_04 04 2 0.5239000625 0
## 125 Pl14_M_04_03 04 2 0.3860160875 0
## 126 Pl14_M_04_01 04 2 0.3314047875 0
## 127 Pl15_C_04_04 04 1 0.65063922125 0
## 128 Pl15_C_04_03 04 1 0.751748325 0
## 129 Pl15_C_04_01 04 1 0.6264209125 0
## 130 Pl12_M_04_01 04 2 0.46744599375 0
## 131 Pl16_C_04_01 04 1 0.4034346825 0
## 132 Pl8_C_04_01 04 1 0 0
## 133 Pl10_M_04_04 04 2 0 0
## 134 Pl10_M_04_03 04 2 0 0
## 135 MPl8_C_04_04 04 1 0 0
## 136 Pl10_C_04_04 04 1 0 0
## 137 Pl10_C_04_03 04 1 0 0
## 138 Pl10_C_04_02 04 1 0 0
## 139 Pl10_C_04_01 04 1 0 0
## 140 Pl16_C_05_04 05 1 0.64243801 0
## 141 Pl2_C_05_03 05 1 0.845673455 5
## 142 Pl3_C_05_03 05 1 0.8403463625 3.5
## 143 Pl4_M_05_04 05 2 0.46809217875 0
## 144 Pl4_M_05_03 05 2 0.369825425 0
## 145 Pl4_M_05_02 05 2 0.36377047 0
## 146 Pl4_M_05_01 05 2 0.502583475 0
## 147 Pl5_M_05_02 05 2 0.49386306875 0
## 148 Pl5_M_05_03 05 2 0.48627644375 0
## 149 Pl5_M_05_01 05 2 0.37578822875 0
## 150 Pl2_C_05_02 05 1 0 0
## 151 Pl2_C_05_04 05 1 0.6202776325 0
## 152 Pl2_C_05_01 05 1 0 0
## 153 Pl3_C_05_01 05 1 0.7456794325 0
## 154 Pl3_C_05_04 05 1 0.8046370625 0
## 155 Pl3_C_05_02 05 1 0.7191326125 0
## 156 Pl15_C_05_02 05 1 0.49424881625 0
## 157 Pl15_C_05_04 05 1 0.61359795 0
## 158 Pl15_C_05_03 05 1 0.47535795875 0
## 159 Pl15_C_05_01 05 1 0.58257980125 0
## 160 Pl16_C_05_03 05 1 0.449585725 0
## 161 Pl16_C_05_02 05 1 0.2159754375 0
## 162 Pl16_C_05_01 05 1 0.297969165 0
## 163 Pl5_M_05_04 05 2 0.33121922 0
## 164 Pl02_C_05_02 05 1 0 0
## 165 Pl02_C_05_01 05 1 0 0
## 166 Pl16_C_06_04 06 1 0.5975780125 0
## 167 Pl16_C_06_03 06 1 0.58101372125 0
## 168 Pl16_C_06_02 06 1 0.60594389625 2.5
## 169 Pl16_C_06_01 06 1 0.7712185375 0
## 170 Pl5_M_06_04 06 2 0.7791661625 2.5
## 171 Pl5_M_06_02 06 2 0.682791045 2.75
## 172 Pl2_C_06_01 06 1 0.7774068875 3.5
## 173 Pl2_C_06_04 06 1 0.9291828375 5.75
## 174 Pl2_C_06_02 06 1 0.8567024125 4.5
## 175 Pl15_C_06_04 06 1 0.590455505 3.5
## 176 Pl4_M_06_04 06 2 0.45032603 0
## 177 Pl4_M_06_03 06 2 0.6110591875 0
## 178 Pl4_M_06_02 06 2 0.224844725 0
## 179 Pl4_M_06_01 06 2 0.5782555625 0
## 180 Pl5_M_06_03 06 2 0.6026246925 0
## 181 Pl5_M_06_01 06 2 0.3778364 0
## 182 Pl13_M_06_02 06 2 0.432037675 0
## 183 Pl14_M_06_04 06 2 0.369138575 0
## 184 Pl14_M_06_01 06 2 0.3215934575 0
## 185 Pl2_C_06_03 06 1 0.86997337875 0
## 186 Pl3_C_06_04 06 1 0.62828125 0
## 187 Pl3_C_06_03 06 1 0.724494425 0
## 188 Pl3_C_06_02 06 1 0.78818275 0
## 189 Pl3_C_06_01 06 1 0.85085735 0
## 190 Pl15_C_06_02 06 1 0.5234183375 0
## 191 Pl15_C_06_01 06 1 0.65049308375 0
## 192 Pl13_M_06_01 06 2 0.27841551875 0
## 193 Pl14_M_06_03 06 2 0.35816954125 0
## 194 Pl14_M_06_02 06 2 0.2331369125 0
## 195 Pl15_C_06_03 06 1 0.310479015 0
## 196 Pl1_M_07_04 07 2 0.46381331625 0
## 197 Pl1_M_07_02 07 2 0.5907877625 0
## 198 Pl1_M_07_01 07 2 0.5409841125 0
## 199 Pl1_M_07_03 07 2 0.4824966125 0
## 200 Pl6_C_07_04 07 1 0.81682194625 0
## 201 Pl6_C_07_03 07 1 1.0188094875 3.75
## 202 Pl6_C_07_02 07 1 0.55674614375 0
## 203 Pl6_C_07_01 07 1 0.7642341875 0
## 204 Pl2_C_07_03 07 1 0.66539724375 1
## 205 Pl2_C_07_01 07 1 0.67488196625 4
## 206 Pl3_C_07_02 07 1 0.75136841875 3
## 207 Pl11_M_07_04 07 2 0.46209546625 0
## 208 Pl11_M_07_03 07 2 0.65128393125 0
## 209 Pl11_M_07_02 07 2 0.4958353125 0
## 210 Pl11_M_07_01 07 2 0.549309625 0
## 211 Pl7_C_07_04 07 1 0.67212965 0
## 212 Pl7_C_07_03 07 1 0.56524455 0
## 213 Pl7_C_07_02 07 1 0.61659525 0
## 214 Pl7_C_07_01 07 1 0.593791975 0
## 215 Pl4_M_07_04 07 2 0.610299695 0
## 216 Pl4_M_07_03 07 2 0.69308045375 0
## 217 Pl4_M_07_02 07 2 0.63670645375 0
## 218 Pl4_M_07_01 07 2 0.58598602 0
## 219 Pl5_M_07_04 07 2 0.6112187625 0
## 220 Pl5_M_07_03 07 2 0.66664569625 0
## 221 Pl5_M_07_02 07 2 0.5571907425 0
## 222 Pl5_M_07_01 07 2 0.54080799625 0
## 223 Pl2_C_07_04 07 1 0.6174833875 0
## 224 Pl2_C_07_02 07 1 0.710517475 0
## 225 Pl3_C_07_04 07 1 0.6408862125 0
## 226 Pl3_C_07_03 07 1 0.6772189625 0
## 227 Pl3_C_07_01 07 1 0.72082412 0
## 228 Pl1_M_08_02 08 2 0.52466548 0
## 229 Pl1_M_08_03 08 2 0.508775925 0
## 230 Pl1_M_08_01 08 2 0.5356339875 0
## 231 Pl1_M_08_04 08 2 0.5920565125 0
## 232 Pl5_M_08_03 08 2 0.55415963125 1.25
## 233 Pl6_C_08_04 08 1 0.566273915 0
## 234 Pl6_C_08_03 08 1 0.8430136125 0
## 235 Pl6_C_08_02 08 1 0.7740699125 0
## 236 Pl6_C_08_01 08 1 0.65329229375 3.25
## 237 Pl3_C_08_04 08 1 0.63008300375 4
## 238 Pl3_C_08_03 08 1 0.54697688375 2
## 239 Pl11_M_08_04 08 2 0.5114103075 0
## 240 Pl11_M_08_03 08 2 0.59807305375 0
## 241 Pl11_M_08_02 08 2 0.49575432 0
## 242 Pl11_M_08_01 08 2 0.67394106875 2.25
## 243 Pl7_C_08_04 08 1 0.81965462 0
## 244 Pl7_C_08_03 08 1 0.92487395 0
## 245 Pl7_C_08_02 08 1 0.98616646625 0
## 246 Pl7_C_08_01 08 1 0.88724672125 0
## 247 Pl4_M_08_04 08 2 0.61069795 0
## 248 Pl4_M_08_03 08 2 0.5930884625 0
## 249 Pl4_M_08_02 08 2 0.387557745 0
## 250 Pl4_M_08_01 08 2 0.55736775 0
## 251 Pl5_M_08_01 08 2 0.588690925 0
## 252 Pl5_M_08_04 08 2 0.52489527 0
## 253 Pl5_M_08_02 08 2 0.666429645 0
## 254 Pl2_C_08_04 08 1 0.6272715 0
## 255 Pl2_C_08_03 08 1 0.63528245 0
## 256 Pl2_C_08_02 08 1 0.7086221 0
## 257 Pl2_C_08_01 08 1 0.691633475 0
## 258 Pl3_C_08_01 08 1 0.6411387825 0
## 259 Pl3_C_08_02 08 1 0.5919273375 0
## aLRL.delta
## 1 0
## 2 0.109739870407895
## 3 0.132206579125
## 4 0.098935055736842
## 5 0.0634006866
## 6 0.0995367347368421
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## 27 0
## 28 0
## 29 0
## 30 0
## 31 0
## 32 0
## 33 0
## 34 0
## 35 0
## 36 0
## 37 0
## 38 0.117982811446429
## 39 0.120255050411765
## 40 0.0584443816388889
## 41 0
## 42 0
## 43 0
## 44 0
## 45 0
## 46 0
## 47 0
## 48 0
## 49 0
## 50 0
## 51 0
## 52 0
## 53 0
## 54 0
## 55 0
## 56 0
## 57 0
## 58 0
## 59 0
## 60 0
## 61 0
## 62 0
## 63 0
## 64 0
## 65 0
## 66 0
## 67 0
## 68 0
## 69 0
## 70 0
## 71 0
## 72 0
## 73 0
## 74 0
## 75 0
## 76 0.0868226815
## 77 0.082140183
## 78 0.0778175775096154
## 79 0
## 80 0
## 81 0
## 82 0
## 83 0
## 84 0
## 85 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 0
## 100 0
## 101 0
## 102 0
## 103 0
## 104 0.05640729596875
## 105 0
## 106 0.12422206651087
## 107 0.112294295654762
## 108 0.103925753275
## 109 0.0881391596
## 110 0.04294646575
## 111 0
## 112 0
## 113 0.112859666211538
## 114 0
## 115 0
## 116 0
## 117 0
## 118 0
## 119 0
## 120 0
## 121 0
## 122 0
## 123 0
## 124 0
## 125 0
## 126 0
## 127 0
## 128 0
## 129 0
## 130 0
## 131 0
## 132 0
## 133 0
## 134 0
## 135 0
## 136 0
## 137 0
## 138 0
## 139 0
## 140 0
## 141 -0.0199634288690476
## 142 0.0991977055166666
## 143 0
## 144 0
## 145 0
## 146 0
## 147 0
## 148 0
## 149 0
## 150 0
## 151 0
## 152 0
## 153 0
## 154 0
## 155 0
## 156 0
## 157 0
## 158 0
## 159 0
## 160 0
## 161 0
## 162 0
## 163 0
## 164 0
## 165 0
## 166 0
## 167 0
## 168 0.152144571019231
## 169 0
## 170 0.0877431929583333
## 171 0.0434172150416667
## 172 0.1029270736
## 173 0.00533032031249999
## 174 0.1319099284375
## 175 0.0495053082833333
## 176 0
## 177 0
## 178 0
## 179 0
## 180 0
## 181 0
## 182 0
## 183 0
## 184 0
## 185 0
## 186 0
## 187 0
## 188 0
## 189 0
## 190 0
## 191 0
## 192 0
## 193 0
## 194 0
## 195 0
## 196 0
## 197 0
## 198 0
## 199 0
## 200 0
## 201 0.05821575040625
## 202 0
## 203 0
## 204 0.1179209265
## 205 0.119499510617647
## 206 0.119461052089286
## 207 0
## 208 0
## 209 0
## 210 0
## 211 0
## 212 0
## 213 0
## 214 0
## 215 0
## 216 0
## 217 0
## 218 0
## 219 0
## 220 0
## 221 0
## 222 0
## 223 0
## 224 0
## 225 0
## 226 0
## 227 0
## 228 0
## 229 0
## 230 0
## 231 0
## 232 0.0723639170833333
## 233 0
## 234 0
## 235 0
## 236 0.1036340475
## 237 0.104998590308824
## 238 0.118904786722222
## 239 0
## 240 0
## 241 0
## 242 0.0706312206
## 243 0
## 244 0
## 245 0
## 246 0
## 247 0
## 248 0
## 249 0
## 250 0
## 251 0
## 252 0
## 253 0
## 254 0
## 255 0
## 256 0
## 257 0
## 258 0
## 259 0growth_factors <- subset(growth_factors, growth_factors$MR.delta > 0)
growth_factors## root_name genotype condition MR.delta LRno.delta
## 2 Pl8_C_01_01 01 1 0.813158725 4.5
## 3 Pl8_C_01_04 01 1 0.710006275 2.25
## 4 Pl8_C_01_02 01 1 0.8466061925 4.25
## 5 Pl9_C_01_01 01 1 0.617869375 1
## 6 Pl9_C_01_02 01 1 0.840557725 4.25
## 7 Pl1_M_01_04 01 2 0.2859252375 0
## 8 Pl1_M_01_01 01 2 0.3993961875 0
## 9 Pl1_M_01_03 01 2 0.1940168625 0
## 10 Pl6_C_01_03 01 1 0.536725875 0
## 11 Pl6_C_01_02 01 1 0.5971264625 0
## 16 Pl11_M_01_01 01 2 0.42590645 0
## 17 Pl11_M_01_03 01 2 0.5157705 0
## 18 Pl7_C_01_04 01 1 0.403761675 0
## 19 Pl7_C_01_03 01 1 0.502998425 0
## 20 Pl7_C_01_01 01 1 0.54359619375 0
## 22 Pl12_M_01_3 01 2 0.6080217 0
## 23 Pl12_M_01_01 01 2 0.4099114875 0
## 24 Pl12_M_01_02 01 2 0.441791725 0
## 25 Pl8_C_01_03 01 1 0.591046995 0
## 26 Pl9_C_01_04 01 1 0.74088145 0
## 27 Pl9_C_01_03 01 1 0.7701522 0
## 29 Pl1_M_01_02 01 2 0.140476945 0
## 30 Pl7_C_01_02 01 1 0.251226295 0
## 31 Pl6_C_01_04 01 1 0.4352060875 0
## 32 Pl6_C_01_01 01 1 0.250516125 0
## 38 Pl8_C_02_02 02 1 0.8309767 3.25
## 39 Pl9_C_02_04 02 1 0.8403002125 4
## 40 Pl9_C_02_03 02 1 0.8994527875 2
## 41 Pl1_M_02_03 02 2 0.345294135 0
## 42 Pl1_M_02_02 02 2 0.185601775 0
## 43 Pl1_M_02_04 02 2 0.385113505 0
## 44 Pl1_M_02_01 02 2 0.34125653 0
## 45 Pl6_C_02_02 02 1 0.5317902 0
## 46 Pl6_C_02_04 02 1 0.169884475 0
## 47 Pl6_C_02_03 02 1 0.4919284875 0
## 52 Pl11_M_02_03 02 2 0.3819924875 0
## 53 Pl11_M_02_02 02 2 0.43507329625 0
## 54 Pl7_C_02_01 02 1 0.529759 0
## 55 Pl12_M_02_04 02 2 0.5745379 0
## 56 Pl12_M_02_03 02 2 0.4310821 0
## 57 Pl12_M_02_02 02 2 0.3916473875 0
## 58 Pl12_M_02_01 02 2 0.4807238075 0
## 59 Pl8_C_02_04 02 1 0.5845632375 0
## 60 Pl8_C_02_03 02 1 0.693181277954546 0
## 61 Pl8_C_02_01 02 1 0.6194379875 0
## 62 Pl9_C_02_02 02 1 0.5574316175 0
## 63 Pl9_C_02_01 02 1 0.962761725 0
## 65 Pl7_C_02_04 02 1 0.398648825 0
## 66 Pl7_C_02_03 02 1 0.27379739375 0
## 67 Pl7_C_02_02 02 1 0.3684457 0
## 68 Pl6_C_02_01 02 1 0.4529618875 0
## 69 Pl11_M_02_04 02 2 0.409647375 0
## 70 Pl11_M_02_01 02 2 0.3578801125 0
## 74 Pl16_C_03_02 03 1 0.928972825 0
## 75 Pl16_C_03_01 03 1 0.80565257875 0
## 76 Pl9_C_03_01 03 1 0.509486225 3.25
## 77 Pl9_C_03_02 03 1 0.568553245 1
## 78 Pl15_C_03_04 03 1 0.9316032 6.25
## 79 Pl12_M_03_04 03 2 0.4806823 0
## 80 Pl12_M_03_03 03 2 0.476534975 0
## 81 Pl8_C_03_04 03 1 0.5346180875 0
## 82 Pl8_C_03_03 03 1 0.57301961875 0
## 83 Pl8_C_03_02 03 1 0.57839092 0
## 84 Pl8_C_03_01 03 1 0.5683905 0
## 85 Pl9_C_03_04 03 1 0.589936025 0
## 86 Pl13_M_03_03 03 2 0.60909883375 0
## 87 Pl13_M_03_02 03 2 0.5748877375 0
## 88 Pl13_M_03_01 03 2 0.4479638175 0
## 89 Pl14_M_03_02 03 2 0.50800475 0
## 90 Pl14_M_03_04 03 2 0.340379 0
## 91 Pl14_M_03_01 03 2 0.5724312125 0
## 92 Pl15_C_03_03 03 1 0.62774635 0
## 93 Pl12_M_03_02 03 2 0.231007875 0
## 94 Pl12_M_03_01 03 2 0.37259555375 0
## 95 Pl9_C_03_03 03 1 0.488624925 0
## 96 Pl13_M_03_04 03 2 0.3598815375 0
## 102 Pl15_C_03_02 03 1 0.437783855 0
## 103 Pl15_C_03_01 03 1 0.55199556625 0
## 104 Pl12_M_04_03 04 2 0.4833221875 1.75
## 105 Pl16_C_04_04 04 1 0.76160152 0
## 106 Pl16_C_04_03 04 1 0.87578631875 5.5
## 107 Pl16_C_04_02 04 1 0.7195959 5
## 108 Pl8_C_04_02 04 1 0.480335075 2
## 109 Pl13_M_04_04 04 2 0.602540475 2.25
## 110 Pl13_M_04_02 04 2 0.5412957 2
## 113 Pl15_C_04_02 04 1 0.8344400675 2.75
## 114 Pl12_M_04_04 04 2 0.48705905 0
## 115 Pl12_M_04_02 04 2 0.454433075 0
## 118 Pl9_C_04_04 04 1 0.61172129375 0
## 119 Pl9_C_04_03 04 1 0.59023315875 0
## 120 Pl9_C_04_02 04 1 0.5576237125 0
## 121 Pl9_C_04_01 04 1 0.5629938925 0
## 122 Pl13_M_04_03 04 2 0.45430235 0
## 123 Pl14_M_04_02 04 2 0.3788431375 0
## 124 Pl14_M_04_04 04 2 0.5239000625 0
## 125 Pl14_M_04_03 04 2 0.3860160875 0
## 126 Pl14_M_04_01 04 2 0.3314047875 0
## 127 Pl15_C_04_04 04 1 0.65063922125 0
## 128 Pl15_C_04_03 04 1 0.751748325 0
## 129 Pl15_C_04_01 04 1 0.6264209125 0
## 130 Pl12_M_04_01 04 2 0.46744599375 0
## 131 Pl16_C_04_01 04 1 0.4034346825 0
## 140 Pl16_C_05_04 05 1 0.64243801 0
## 141 Pl2_C_05_03 05 1 0.845673455 5
## 142 Pl3_C_05_03 05 1 0.8403463625 3.5
## 143 Pl4_M_05_04 05 2 0.46809217875 0
## 144 Pl4_M_05_03 05 2 0.369825425 0
## 145 Pl4_M_05_02 05 2 0.36377047 0
## 146 Pl4_M_05_01 05 2 0.502583475 0
## 147 Pl5_M_05_02 05 2 0.49386306875 0
## 148 Pl5_M_05_03 05 2 0.48627644375 0
## 149 Pl5_M_05_01 05 2 0.37578822875 0
## 151 Pl2_C_05_04 05 1 0.6202776325 0
## 153 Pl3_C_05_01 05 1 0.7456794325 0
## 154 Pl3_C_05_04 05 1 0.8046370625 0
## 155 Pl3_C_05_02 05 1 0.7191326125 0
## 156 Pl15_C_05_02 05 1 0.49424881625 0
## 157 Pl15_C_05_04 05 1 0.61359795 0
## 158 Pl15_C_05_03 05 1 0.47535795875 0
## 159 Pl15_C_05_01 05 1 0.58257980125 0
## 160 Pl16_C_05_03 05 1 0.449585725 0
## 161 Pl16_C_05_02 05 1 0.2159754375 0
## 162 Pl16_C_05_01 05 1 0.297969165 0
## 163 Pl5_M_05_04 05 2 0.33121922 0
## 166 Pl16_C_06_04 06 1 0.5975780125 0
## 167 Pl16_C_06_03 06 1 0.58101372125 0
## 168 Pl16_C_06_02 06 1 0.60594389625 2.5
## 169 Pl16_C_06_01 06 1 0.7712185375 0
## 170 Pl5_M_06_04 06 2 0.7791661625 2.5
## 171 Pl5_M_06_02 06 2 0.682791045 2.75
## 172 Pl2_C_06_01 06 1 0.7774068875 3.5
## 173 Pl2_C_06_04 06 1 0.9291828375 5.75
## 174 Pl2_C_06_02 06 1 0.8567024125 4.5
## 175 Pl15_C_06_04 06 1 0.590455505 3.5
## 176 Pl4_M_06_04 06 2 0.45032603 0
## 177 Pl4_M_06_03 06 2 0.6110591875 0
## 178 Pl4_M_06_02 06 2 0.224844725 0
## 179 Pl4_M_06_01 06 2 0.5782555625 0
## 180 Pl5_M_06_03 06 2 0.6026246925 0
## 181 Pl5_M_06_01 06 2 0.3778364 0
## 182 Pl13_M_06_02 06 2 0.432037675 0
## 183 Pl14_M_06_04 06 2 0.369138575 0
## 184 Pl14_M_06_01 06 2 0.3215934575 0
## 185 Pl2_C_06_03 06 1 0.86997337875 0
## 186 Pl3_C_06_04 06 1 0.62828125 0
## 187 Pl3_C_06_03 06 1 0.724494425 0
## 188 Pl3_C_06_02 06 1 0.78818275 0
## 189 Pl3_C_06_01 06 1 0.85085735 0
## 190 Pl15_C_06_02 06 1 0.5234183375 0
## 191 Pl15_C_06_01 06 1 0.65049308375 0
## 192 Pl13_M_06_01 06 2 0.27841551875 0
## 193 Pl14_M_06_03 06 2 0.35816954125 0
## 194 Pl14_M_06_02 06 2 0.2331369125 0
## 195 Pl15_C_06_03 06 1 0.310479015 0
## 196 Pl1_M_07_04 07 2 0.46381331625 0
## 197 Pl1_M_07_02 07 2 0.5907877625 0
## 198 Pl1_M_07_01 07 2 0.5409841125 0
## 199 Pl1_M_07_03 07 2 0.4824966125 0
## 200 Pl6_C_07_04 07 1 0.81682194625 0
## 201 Pl6_C_07_03 07 1 1.0188094875 3.75
## 202 Pl6_C_07_02 07 1 0.55674614375 0
## 203 Pl6_C_07_01 07 1 0.7642341875 0
## 204 Pl2_C_07_03 07 1 0.66539724375 1
## 205 Pl2_C_07_01 07 1 0.67488196625 4
## 206 Pl3_C_07_02 07 1 0.75136841875 3
## 207 Pl11_M_07_04 07 2 0.46209546625 0
## 208 Pl11_M_07_03 07 2 0.65128393125 0
## 209 Pl11_M_07_02 07 2 0.4958353125 0
## 210 Pl11_M_07_01 07 2 0.549309625 0
## 211 Pl7_C_07_04 07 1 0.67212965 0
## 212 Pl7_C_07_03 07 1 0.56524455 0
## 213 Pl7_C_07_02 07 1 0.61659525 0
## 214 Pl7_C_07_01 07 1 0.593791975 0
## 215 Pl4_M_07_04 07 2 0.610299695 0
## 216 Pl4_M_07_03 07 2 0.69308045375 0
## 217 Pl4_M_07_02 07 2 0.63670645375 0
## 218 Pl4_M_07_01 07 2 0.58598602 0
## 219 Pl5_M_07_04 07 2 0.6112187625 0
## 220 Pl5_M_07_03 07 2 0.66664569625 0
## 221 Pl5_M_07_02 07 2 0.5571907425 0
## 222 Pl5_M_07_01 07 2 0.54080799625 0
## 223 Pl2_C_07_04 07 1 0.6174833875 0
## 224 Pl2_C_07_02 07 1 0.710517475 0
## 225 Pl3_C_07_04 07 1 0.6408862125 0
## 226 Pl3_C_07_03 07 1 0.6772189625 0
## 227 Pl3_C_07_01 07 1 0.72082412 0
## 228 Pl1_M_08_02 08 2 0.52466548 0
## 229 Pl1_M_08_03 08 2 0.508775925 0
## 230 Pl1_M_08_01 08 2 0.5356339875 0
## 231 Pl1_M_08_04 08 2 0.5920565125 0
## 232 Pl5_M_08_03 08 2 0.55415963125 1.25
## 233 Pl6_C_08_04 08 1 0.566273915 0
## 234 Pl6_C_08_03 08 1 0.8430136125 0
## 235 Pl6_C_08_02 08 1 0.7740699125 0
## 236 Pl6_C_08_01 08 1 0.65329229375 3.25
## 237 Pl3_C_08_04 08 1 0.63008300375 4
## 238 Pl3_C_08_03 08 1 0.54697688375 2
## 239 Pl11_M_08_04 08 2 0.5114103075 0
## 240 Pl11_M_08_03 08 2 0.59807305375 0
## 241 Pl11_M_08_02 08 2 0.49575432 0
## 242 Pl11_M_08_01 08 2 0.67394106875 2.25
## 243 Pl7_C_08_04 08 1 0.81965462 0
## 244 Pl7_C_08_03 08 1 0.92487395 0
## 245 Pl7_C_08_02 08 1 0.98616646625 0
## 246 Pl7_C_08_01 08 1 0.88724672125 0
## 247 Pl4_M_08_04 08 2 0.61069795 0
## 248 Pl4_M_08_03 08 2 0.5930884625 0
## 249 Pl4_M_08_02 08 2 0.387557745 0
## 250 Pl4_M_08_01 08 2 0.55736775 0
## 251 Pl5_M_08_01 08 2 0.588690925 0
## 252 Pl5_M_08_04 08 2 0.52489527 0
## 253 Pl5_M_08_02 08 2 0.666429645 0
## 254 Pl2_C_08_04 08 1 0.6272715 0
## 255 Pl2_C_08_03 08 1 0.63528245 0
## 256 Pl2_C_08_02 08 1 0.7086221 0
## 257 Pl2_C_08_01 08 1 0.691633475 0
## 258 Pl3_C_08_01 08 1 0.6411387825 0
## 259 Pl3_C_08_02 08 1 0.5919273375 0
## aLRL.delta
## 2 0.109739870407895
## 3 0.132206579125
## 4 0.098935055736842
## 5 0.0634006866
## 6 0.0995367347368421
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 16 0
## 17 0
## 18 0
## 19 0
## 20 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## 27 0
## 29 0
## 30 0
## 31 0
## 32 0
## 38 0.117982811446429
## 39 0.120255050411765
## 40 0.0584443816388889
## 41 0
## 42 0
## 43 0
## 44 0
## 45 0
## 46 0
## 47 0
## 52 0
## 53 0
## 54 0
## 55 0
## 56 0
## 57 0
## 58 0
## 59 0
## 60 0
## 61 0
## 62 0
## 63 0
## 65 0
## 66 0
## 67 0
## 68 0
## 69 0
## 70 0
## 74 0
## 75 0
## 76 0.0868226815
## 77 0.082140183
## 78 0.0778175775096154
## 79 0
## 80 0
## 81 0
## 82 0
## 83 0
## 84 0
## 85 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 102 0
## 103 0
## 104 0.05640729596875
## 105 0
## 106 0.12422206651087
## 107 0.112294295654762
## 108 0.103925753275
## 109 0.0881391596
## 110 0.04294646575
## 113 0.112859666211538
## 114 0
## 115 0
## 118 0
## 119 0
## 120 0
## 121 0
## 122 0
## 123 0
## 124 0
## 125 0
## 126 0
## 127 0
## 128 0
## 129 0
## 130 0
## 131 0
## 140 0
## 141 -0.0199634288690476
## 142 0.0991977055166666
## 143 0
## 144 0
## 145 0
## 146 0
## 147 0
## 148 0
## 149 0
## 151 0
## 153 0
## 154 0
## 155 0
## 156 0
## 157 0
## 158 0
## 159 0
## 160 0
## 161 0
## 162 0
## 163 0
## 166 0
## 167 0
## 168 0.152144571019231
## 169 0
## 170 0.0877431929583333
## 171 0.0434172150416667
## 172 0.1029270736
## 173 0.00533032031249999
## 174 0.1319099284375
## 175 0.0495053082833333
## 176 0
## 177 0
## 178 0
## 179 0
## 180 0
## 181 0
## 182 0
## 183 0
## 184 0
## 185 0
## 186 0
## 187 0
## 188 0
## 189 0
## 190 0
## 191 0
## 192 0
## 193 0
## 194 0
## 195 0
## 196 0
## 197 0
## 198 0
## 199 0
## 200 0
## 201 0.05821575040625
## 202 0
## 203 0
## 204 0.1179209265
## 205 0.119499510617647
## 206 0.119461052089286
## 207 0
## 208 0
## 209 0
## 210 0
## 211 0
## 212 0
## 213 0
## 214 0
## 215 0
## 216 0
## 217 0
## 218 0
## 219 0
## 220 0
## 221 0
## 222 0
## 223 0
## 224 0
## 225 0
## 226 0
## 227 0
## 228 0
## 229 0
## 230 0
## 231 0
## 232 0.0723639170833333
## 233 0
## 234 0
## 235 0
## 236 0.1036340475
## 237 0.104998590308824
## 238 0.118904786722222
## 239 0
## 240 0
## 241 0
## 242 0.0706312206
## 243 0
## 244 0
## 245 0
## 246 0
## 247 0
## 248 0
## 249 0
## 250 0
## 251 0
## 252 0
## 253 0
## 254 0
## 255 0
## 256 0
## 257 0
## 258 0
## 259 0unique(growth_factors$condition)## [1] "1" "2"write.csv(growth_factors, "20221011_DUF_OE_growth_factors.csv", row.names = FALSE)
### Visualizing the growth factors:
growth_factors$genotype <- gsub("01", "Col.0", growth_factors$genotype)
growth_factors$genotype <- gsub("02", "cis1.1", growth_factors$genotype)
growth_factors$genotype <- gsub("03", "agl16.1", growth_factors$genotype)
growth_factors$genotype <- gsub("04", "akr4c9.3A", growth_factors$genotype)
growth_factors$genotype <- gsub("05", "cis1.2", growth_factors$genotype)
growth_factors$genotype <- gsub("06", "agl16.2", growth_factors$genotype)
growth_factors$genotype <- gsub("07", "akr4c9.2I", growth_factors$genotype)
growth_factors$genotype <- gsub("08", "akr4c9.1F2", growth_factors$genotype)?ggerrorplot
library(ggplot2)
growth_factors$genotype <- factor(growth_factors$genotype, levels = c("Col.0", "akr4c9.3A", "akr4c9.2I", "akr4c9.1F2", "agl16.1", "agl16.2", "cis1.1", "cis1.2"))
growth_factors_2 <- subset(growth_factors, growth_factors$MR.delta >= 0)
growth_factors_2$MR.delta <- as.numeric(as.character(growth_factors_2$MR.delta))
growth_factors_2$aLRL.delta <- as.numeric(as.character(growth_factors_2$aLRL.delta))
growth_factors_2$LRno.delta <- as.numeric(as.character(growth_factors_2$LRno.delta))
growth_factors_2$condition <- factor(growth_factors_2$condition, levels = c("1", "2"))
MR.delta_p_geno <- ggerrorplot(growth_factors_2, y = "MR.delta", x = "genotype", fill="genotype", color="condition",
facet.by = c("condition"), ncol=3,
desc_stat = "mean_sd", add = "jitter",
panel.labs = list(condition = c("Control", "Mannitol")),
add.params = list(color = "darkgray"),
xlab="", ylab="Growth Rate (cm / day)")
MR.delta_p_geno <- MR.delta_p_geno + rremove("legend") + stat_compare_means(method="t.test", ref.group = "col",
label = "p.signif")
MR.delta_p_geno <- MR.delta_p_geno + ggtitle("Main Root Growth")
MR.delta_p_geno <- MR.delta_p_geno + theme(axis.text=element_text(size=10))
MR.delta_p_geno <- MR.delta_p_geno + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
MR.delta_p_geno <- MR.delta_p_geno + scale_color_manual(values= c("royalblue", "orange"))
MR.delta_p_geno## Warning: Computation failed in `stat_compare_means()`.
## Computation failed in `stat_compare_means()`.
## Caused by error in `if (ref.group == ".all.") ...`:
## ! missing value where TRUE/FALSE needed
head(growth_factors_2)## root_name genotype condition MR.delta LRno.delta aLRL.delta
## 2 Pl8_C_01_01 Col.0 1 0.8131587 4.50 0.10973987
## 3 Pl8_C_01_04 Col.0 1 0.7100063 2.25 0.13220658
## 4 Pl8_C_01_02 Col.0 1 0.8466062 4.25 0.09893506
## 5 Pl9_C_01_01 Col.0 1 0.6178694 1.00 0.06340069
## 6 Pl9_C_01_02 Col.0 1 0.8405577 4.25 0.09953673
## 7 Pl1_M_01_04 Col.0 2 0.2859252 0.00 0.00000000library(stringr)
library(multcompView)
Control <- subset(growth_factors_2, growth_factors_2$condition == "1")
Mannitol <- subset(growth_factors_2, growth_factors_2$condition == "2")
Control$genotype <- as.factor(Control$genotype)
Mannitol$genotype <- as.factor(Mannitol$genotype)
Output <- TukeyHSD(aov(MR.delta ~ genotype, data = Control))
P7 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P7)
testc <- as.data.frame(stat.test$Letters)
testc$group2 <- rownames(testc)
testc$group1 <- rownames(testc)
testc$genotype <- rownames(testc)
testc$genotype <- rownames(testc)
colnames(testc)[1] <- "Tukey"
testc$condition <- "1"
Output <- TukeyHSD(aov(MR.delta ~ genotype, data = Mannitol))
P7 = Output$genotype[,'p adj']
stat.test<- multcompLetters(P7)
tests <- as.data.frame(stat.test$Letters)
tests$group2 <- rownames(tests)
tests$group1 <- rownames(tests)
tests$genotype <- rownames(tests)
tests$genotype <- rownames(tests)
colnames(tests)[1] <- "Tukey"
tests$condition <- "2"
test <- rbind(testc, tests)
test## Tukey group2 group1 genotype condition
## akr4c9.3A a akr4c9.3A akr4c9.3A akr4c9.3A 1
## akr4c9.2I a akr4c9.2I akr4c9.2I akr4c9.2I 1
## akr4c9.1F2 a akr4c9.1F2 akr4c9.1F2 akr4c9.1F2 1
## agl16.1 a agl16.1 agl16.1 agl16.1 1
## agl16.2 a agl16.2 agl16.2 agl16.2 1
## cis1.1 a cis1.1 cis1.1 cis1.1 1
## cis1.2 a cis1.2 cis1.2 cis1.2 1
## Col.0 a Col.0 Col.0 Col.0 1
## akr4c9.3A1 abc akr4c9.3A akr4c9.3A akr4c9.3A 2
## akr4c9.2I1 a akr4c9.2I akr4c9.2I akr4c9.2I 2
## akr4c9.1F21 ab akr4c9.1F2 akr4c9.1F2 akr4c9.1F2 2
## agl16.11 abc agl16.1 agl16.1 agl16.1 2
## agl16.21 abc agl16.2 agl16.2 agl16.2 2
## cis1.11 c cis1.1 cis1.1 cis1.1 2
## cis1.21 bc cis1.2 cis1.2 cis1.2 2
## Col.01 c Col.0 Col.0 Col.0 2better_MRG_graph <- MR.delta_p_geno + stat_pvalue_manual(test, label = "Tukey", y.position = 1.2)
better_MRG_graph## Warning: Computation failed in `stat_compare_means()`.
## Computation failed in `stat_compare_means()`.
## Caused by error in `if (ref.group == ".all.") ...`:
## ! missing value where TRUE/FALSE needed